1,521 results on '"RADAR"'
Search Results
2. A Review on Radar-Based Human Detection Techniques.
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Buyukakkaslar, Muhammet Talha, Erturk, Mehmet Ali, and Aydin, Muhammet Ali
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CONTINUOUS wave radar , *AIR traffic control , *RESEARCH personnel , *RADAR , *TAXONOMY - Abstract
Radar systems are diverse and used in industries such as air traffic control, weather monitoring, and military and maritime applications. Within the scope of this study, we focus on using radar for human detection and recognition. This study evaluated the general state of micro-Doppler radar-based human recognition technology, the related literature, and state-of-the-art methods. This study aims to provide guidelines for new research in this area. This comprehensive study provides researchers with a thorough review of the existing literature. It gives a taxonomy of the literature and classifies the existing literature by the radar types used, the focus of the research, targeted use cases, and the security concerns raised by the authors. This paper serves as a repository for numerous studies that have been listed, critically evaluated, and systematically classified. [ABSTRACT FROM AUTHOR]
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- 2024
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3. A Novel Waveform Optimization Method for Orthogonal-Frequency Multiple-Input Multiple-Output Radar Based on Dual-Channel Neural Networks.
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Xia, Meng, Gong, Wenrong, and Yang, Lichao
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ARTIFICIAL neural networks , *RADAR targets , *RADAR , *MIMO radar , *BASEBAND , *MULTIPLEXING - Abstract
The orthogonal frequency-division multiplexing (OFDM) mode with a linear frequency modulation (LFM) signal as the baseband waveform has been widely studied and applied in multiple-input multiple-output (MIMO) radar systems. However, its high sidelobe levels after pulse compression affect the target detection of radar systems. For this paper, theoretical analysis was performed, to investigate the causes of high sidelobe levels in OFDM-LFM waveforms, and a novel waveform optimization design method based on deep neural networks is proposed. This method utilizes the classic ResNeXt network to construct dual-channel neural networks, and a new loss function is employed to design the phase and bandwidth of the OFDM-LFM waveforms. Meanwhile, the optimization factor is exploited, to address the optimization problem of the peak sidelobe levels (PSLs) and integral sidelobe levels (ISLs). Our numerical results verified the correctness of the theoretical analysis and the effectiveness of the proposed method. The designed OFDM-LFM waveforms exhibited outstanding performance in pulse compression and improved the detection performance of the radar. [ABSTRACT FROM AUTHOR]
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- 2024
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4. A Synthetic Aperture Radar Imaging Simulation Method for Sea Surface Scenes Combined with Electromagnetic Scattering Characteristics.
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He, Yao, Xu, Le, Huo, Jincong, Zhou, Huaji, and Shi, Xiaowei
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SURFACE scattering , *ELECTROMAGNETIC wave scattering , *OPTICS , *SURFACE properties , *RADAR - Abstract
Synthetic aperture radar (SAR) simulation is a vital tool for planning SAR missions, interpreting SAR images, and extracting valuable information. SAR imaging is essential for analyzing sea scenes, and the accuracy of sea surface and scattering models is crucial for effective SAR simulations. Traditional methods typically employ empirical formulas to fit sea surface scattering, which are not closely aligned with the principles of electromagnetic scattering. This paper introduces a novel approach by constructing multiple sea surface models based on the Pierson–Moskowitz (P-M) sea spectrum, integrated with the stereo wave observation projection (SWOP) expansion function to thoroughly account for the influence of wave fluctuation characteristics on radar scattering. Utilizing the shooting and bouncing ray-physical optics (SBR-PO) method, which adheres to the principles of electromagnetic scattering, this study not only analyzes sea surface scattering characteristics under various sea conditions but also facilitates the computation of scattering coupling between multiple targets. By constructing detailed scattering distribution data, the method achieves high-precision SAR simulation results. The scattering model developed using the SBR-PO method provides a more nuanced description of sea surface scenes compared to traditional methods, achieving an optimal balance between efficiency and accuracy, thus significantly enhancing sea surface SAR imaging simulations. [ABSTRACT FROM AUTHOR]
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- 2024
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5. Application of Instance Segmentation to Identifying Insect Concentrations in Data from an Entomological Radar.
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Wang, Rui, Ren, Jiahao, Li, Weidong, Yu, Teng, Zhang, Fan, and Wang, Jiangtao
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INSECT behavior , *FEATURE extraction , *RADAR , *INSECTS , *DATA extraction - Abstract
Entomological radar is one of the most effective tools for monitoring insect migration, capable of detecting migratory insects concentrated in layers and facilitating the analysis of insect migration behavior. However, traditional entomological radar, with its low resolution, can only provide a rough observation of layer concentrations. The advent of High-Resolution Phased Array Radar (HPAR) has transformed this situation. With its high range resolution and high data update rate, HPAR can generate detailed concentration spatiotemporal distribution heatmaps. This technology facilitates the detection of changes in insect concentrations across different time periods and altitudes, thereby enabling the observation of large-scale take-off, landing, and layering phenomena. However, the lack of effective techniques for extracting insect concentration data of different phenomena from these heatmaps significantly limits detailed analyses of insect migration patterns. This paper is the first to apply instance segmentation technology to the extraction of insect data, proposing a method for segmenting and extracting insect concentration data from spatiotemporal distribution heatmaps at different phenomena. To address the characteristics of concentrations in spatiotemporal distributions, we developed the Heatmap Feature Fusion Network (HFF-Net). In HFF-Net, we incorporate the Global Context (GC) module to enhance feature extraction of concentration distributions, utilize the Atrous Spatial Pyramid Pooling with Depthwise Separable Convolution (SASPP) module to extend the receptive field for understanding various spatiotemporal distributions of concentrations, and refine segmentation masks with the Deformable Convolution Mask Fusion (DCMF) module to enhance segmentation detail. Experimental results show that our proposed network can effectively segment concentrations of different phenomena from heatmaps, providing technical support for detailed and systematic studies of insect migration behavior. [ABSTRACT FROM AUTHOR]
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- 2024
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6. Interferometric Radars for Bridge Monitoring: Comparison among X-Bands, Ku-Bands, and W-Bands.
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Beni, Alessandra, Miccinesi, Lapo, Pagnini, Lorenzo, Cioncolini, Andrea, Shan, Jingfeng, and Pieraccini, Massimiliano
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RADAR interferometry , *STRUCTURAL health monitoring , *RADAR , *DETECTORS , *COMPARATIVE studies , *DISPLACEMENT (Mechanics) - Abstract
Interferometric radars are widely used sensors for structural health monitoring. They are able to perform dynamic measurements of displacement with sub-millimeter precision. Today, the Ku-band is the most common, due to the spread of commercial systems operating in this band. At the same time, the W-band sensors are gaining ever more interest. Other popular systems work in the X-band. Since the characteristics of the measurements dramatically depend on the operative frequency, it is essential to highlight their differences. For instance, higher frequency allows for high displacement resolution, but it is more subject to phase wrapping and decorrelation effects. In this paper, a direct comparison between radars operating in X, Ku, and W-band for bridge monitoring is carried out. The radars provide frequency-modulated continuous-wave signals. Experimental campaigns were performed both in controlled and realistic scenarios (a stayed bridge). The results of the experiments demonstrate that all the three sensors are suitable for performing dynamic structure monitoring despite their differences. It is worth noting that this comparative analysis has highlighted the role of amplitude variation in phase/displacement measurement. Regarding this point, the three different bands exhibit significant differences. [ABSTRACT FROM AUTHOR]
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- 2024
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7. Analysis of Nearshore Near-Inertial Oscillations Using Numerical Simulation with Data Assimilation in the Pearl River Estuary of the South China Sea.
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Jiang, Zihao, Wei, Chunlei, Yang, Fan, and Wei, Jun
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ACOUSTIC Doppler current profiler , *CURRENT fluctuations , *RADAR , *ACOUSTIC measurements , *SENSITIVITY analysis - Abstract
The High-Frequency (HF) radar network has become an effective method for detecting coastal currents. In this study, we confirmed the effectiveness of the HF radar measurements by comparing with the Acoustic Doppler Current Profiler (ADCP) and explore the possibility of assimilating radar data into a regional coastal ocean model. A regional high-resolution model with resolution of 10 m was first built in the Pearl River Estuary (PRE). However, analysis of the Hovmöller diagrams from the model simulations in this study indicated a significant deficiency in representing Near-Inertial Oscillations (NIOs) in the PRE, particularly in the east–west direction, despite including wind fields in the input data, during the week from 3 to 8 August 2022. To overcome the model deficiency, we conducted a set of assimilation experiments and performed sensitivity analyses. The results of sensitivity experiments indicate that the model exhibits a sufficient capacity to replicate NIOs after assimilation, lasting approximately 5–6 days. To further analyze the reasons for the decay in the magnitude of the NIOs, data from the three ADCP stations were compared with model results by applying the momentum equation. The assimilated vertical diffusion term outperforms the unassimilated model in representing NIOs. These findings highlight the importance of the vertical diffusion term for simulating NIOs and the data assimilation in improving the model's representation of physical processes. [ABSTRACT FROM AUTHOR]
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- 2024
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8. Enhancing Integrated Sensing and Communication (ISAC) Performance for a Searching–Deciding Alternation Radar-Comm System with Multi-Dimension Point Cloud Data.
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Chen, Leyan, Liu, Kai, Gao, Qiang, Wang, Xiangfen, and Zhang, Zhibo
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POINT cloud , *DEEP learning , *INTELLIGENT transportation systems , *RADAR , *TRAFFIC safety - Abstract
In developing modern intelligent transportation systems, integrated sensing and communication (ISAC) technology has become an efficient and promising method for vehicle road services. To enhance traffic safety and efficiency through real-time interaction between vehicles and roads, this paper proposes a searching–deciding scheme for an alternation radar-communication (radar-comm) system. Firstly, its communication performance is derived for a given detection probability. Then, we process the echo data from real-world millimeter-wave (mmWave) radar into four-dimensional (4D) point cloud datasets and thus separate different hybrid modes of single-vehicle and vehicle fleets into three types of scenes. Based on these datasets, an efficient labeling method is proposed to assist accurate vehicle target detection. Finally, a novel vehicle detection scheme is proposed to classify various scenes and accurately detect vehicle targets based on deep learning methods. Extensive experiments on collected real-world datasets demonstrate that compared to benchmarks, the proposed scheme obtains substantial radar performance and achieves competitive communication performance. [ABSTRACT FROM AUTHOR]
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- 2024
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9. Efficient Jamming Policy Generation Method Based on Multi-Timescale Ensemble Q-Learning.
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Qian, Jialong, Zhou, Qingsong, Li, Zhihui, Yang, Zhongping, Shi, Shasha, Xu, Zhenjia, and Xu, Qiyun
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MARKOV processes , *ERROR rates , *DECISION making , *RADAR , *RADAR interference , *ALGORITHMS - Abstract
With the advancement of radar technology toward multifunctionality and cognitive capabilities, traditional radar countermeasures are no longer sufficient to meet the demands of countering the advanced multifunctional radar (MFR) systems. Rapid and accurate generation of the optimal jamming strategy is one of the key technologies for efficiently completing radar countermeasures. To enhance the efficiency and accuracy of jamming policy generation, an efficient jamming policy generation method based on multi-timescale ensemble Q-learning (MTEQL) is proposed in this paper. First, the task of generating jamming strategies is framed as a Markov decision process (MDP) by constructing a countermeasure scenario between the jammer and radar, while analyzing the principle radar operation mode transitions. Then, multiple structure-dependent Markov environments are created based on the real-world adversarial interactions between jammers and radars. Q-learning algorithms are executed concurrently in these environments, and their results are merged through an adaptive weighting mechanism that utilizes the Jensen–Shannon divergence (JSD). Ultimately, a low-complexity and near-optimal jamming policy is derived. Simulation results indicate that the proposed method has superior jamming policy generation performance compared with the Q-learning algorithm, in terms of the short jamming decision-making time and low average strategy error rate. [ABSTRACT FROM AUTHOR]
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- 2024
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10. Multi-Target Pairing Method Based on PM-ESPRIT-like DOA Estimation for T/R-R HFSWR.
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Li, Shujie, Wu, Xiaochuan, Chen, Siming, Deng, Weibo, and Zhang, Xin
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VANDERMONDE matrices , *CROSS correlation , *AZIMUTH , *RADAR , *ROTATIONAL motion , *ANGLES - Abstract
The transmit/receive-receive (T/R-R) synergetic High Frequency Surface Wave Radar (HFSWR) has increasingly attracted attention due to its high localization accuracy, but multi-target pairing needs to be performed before localization in multi-target scenarios. However, existing multi-target parameter matching methods have primarily focused on track association, which falls under the category of information-level fusion techniques, with few methods based on detected points. In this paper, we propose a multi-target pairing method with high computational efficiency based on angle information for T/R-R synergetic HFSWR. To be more specific, a dual-receiving array signal model under long baseline condition is firstly constructed. Then, the amplitude and phase differences of the same target reaching two subarrays are calculated to establish the cross-correlation matrix. Subsequently, in order to extract the rotation factor matrices containing pairing information and improve angle estimation performance, we utilize the conjugate symmetry properties of the uniform linear array (ULA) manifold matrix for generalized virtual aperture extension. Ultimately, azimuths estimation and multi-target pairing are accomplished by combining the propagator method (PM) and the ESPRIT algorithm. The proposed method relies solely on angle information for multi-target pairing and leverages the rotational invariance property of Vandermonde matrices to avoid peak searching or iterations, making it computationally efficient. Furthermore, the proposed method maintains superb performance regardless of whether the spatial angles are widely separated or very close. Simulation results validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
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- 2024
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11. Intra-Pulse Modulation Recognition of Radar Signals Based on Efficient Cross-Scale Aware Network.
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Liang, Jingyue, Luo, Zhongtao, and Liao, Renlong
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CONVOLUTIONAL neural networks , *PARALLEL processing , *COMPUTATIONAL complexity , *IMAGE recognition (Computer vision) , *RADAR - Abstract
Radar signal intra-pulse modulation recognition can be addressed with convolutional neural networks (CNNs) and time–frequency images (TFIs). However, current CNNs have high computational complexity and do not perform well in low-signal-to-noise ratio (SNR) scenarios. In this paper, we propose a lightweight CNN known as the cross-scale aware network (CSANet) to recognize intra-pulse modulation based on three types of TFIs. The cross-scale aware (CSA) module, designed as a residual and parallel architecture, comprises a depthwise dilated convolution group (DDConv Group), a cross-channel interaction (CCI) mechanism, and spatial information focus (SIF). DDConv Group produces multiple-scale features with a dynamic receptive field, CCI fuses the features and mitigates noise in multiple channels, and SIF is aware of the cross-scale details of TFI structures. Furthermore, we develop a novel time–frequency fusion (TFF) feature based on three types of TFIs by employing image preprocessing techniques, i.e., adaptive binarization, morphological processing, and feature fusion. Experiments demonstrate that CSANet achieves higher accuracy with our TFF compared to other TFIs. Meanwhile, CSANet outperforms cutting-edge networks across twelve radar signal datasets, providing an efficient solution for high-precision recognition in low-SNR scenarios. [ABSTRACT FROM AUTHOR]
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- 2024
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12. Camera-Radar Fusion with Radar Channel Extension and Dual-CBAM-FPN for Object Detection.
- Author
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Sun, Xiyan, Jiang, Yaoyu, Qin, Hongmei, Li, Jingjing, and Ji, Yuanfa
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OBJECT recognition (Computer vision) , *FEATURE extraction , *RADAR , *CAMERAS , *DETECTORS - Abstract
When it comes to road environment perception, millimeter-wave radar with a camera facilitates more reliable detection than a single sensor. However, the limited utilization of radar features and insufficient extraction of important features remain pertinent issues, especially with regard to the detection of small and occluded objects. To address these concerns, we propose a camera-radar fusion with radar channel extension and a dual-CBAM-FPN (CRFRD), which incorporates a radar channel extension (RCE) module and a dual-CBAM-FPN (DCF) module into the camera-radar fusion net (CRF-Net). In the RCE module, we design an azimuth-weighted RCS parameter and extend three radar channels, which leverage the secondary redundant information to achieve richer feature representation. In the DCF module, we present the dual-CBAM-FPN, which enables the model to focus on important features by inserting CBAM at the input and the fusion process of FPN simultaneously. Comparative experiments conducted on the NuScenes dataset and real data demonstrate the superior performance of the CRFRD compared to CRF-Net, as its weighted mean average precision (wmAP) increases from 43.89% to 45.03%. Furthermore, ablation studies verify the indispensability of the RCE and DCF modules and the effectiveness of azimuth-weighted RCS. [ABSTRACT FROM AUTHOR]
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- 2024
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13. Motion Clutter Suppression for Non-Cooperative Target Identification Based on Frequency Correlation Dual-SVD Reconstruction.
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He, Weikun, Luo, Yichuan, and Shang, Xiaoxiao
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SINGULAR value decomposition , *FUZZY algorithms , *EIGENVALUES , *RADAR , *ENTROPY , *RADAR in aeronautics - Abstract
Non-cooperative targets, such as birds and unmanned aerial vehicles (UAVs), are typical low-altitude, slow, and small (LSS) targets with low observability. Radar observations in such scenarios are often complicated by strong motion clutter originating from sources like airplanes and cars. Hence, distinguishing between birds and UAVs in environments with strong motion clutter is crucial for improving target monitoring performance and ensuring flight safety. To address the impact of strong motion clutter on discriminating between UAVs and birds, we propose a frequency correlation dual-SVD (singular value decomposition) reconstruction method. This method exploits the strong power and spectral correlation characteristics of motion clutter, contrasted with the weak scattering characteristics of bird and UAV targets, to effectively suppress clutter. Unlike traditional clutter suppression methods based on SVD, our method avoids residual clutter or target loss while preserving the micro-motion characteristics of the targets. Based on the distinct micro-motion characteristics of birds and UAVs, we extract two key features: the sum of normalized large eigenvalues of the target's micro-motion component and the energy entropy of the time–frequency spectrum of the radar echoes. Subsequently, the kernel fuzzy c-means algorithm is applied to classify bird and UAV targets. The effectiveness of our proposed method is validated through results using both simulation and experimental data. [ABSTRACT FROM AUTHOR]
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- 2024
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14. Human Fall Detection with Ultra-Wideband Radar and Adaptive Weighted Fusion.
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Huang, Ling, Zhu, Anfu, Qian, Mengjie, and An, Huifeng
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RADAR , *CLASSIFICATION , *HUMAN beings - Abstract
To address the challenges in recognizing various types of falls, which often exhibit high similarity and are difficult to distinguish, this paper proposes a human fall classification system based on the SE-Residual Concatenate Network (SE-RCNet) with adaptive weighted fusion. First, we designed the innovative SE-RCNet network, incorporating SE modules after dense and residual connections to automatically recalibrate feature channel weights and suppress irrelevant features. Subsequently, this network was used to train and classify three types of radar images: time–distance images, time–distance images, and distance–distance images. By adaptively fusing the classification results of these three types of radar images, we achieved higher action recognition accuracy. Experimental results indicate that SE-RCNet achieved F1-scores of 94.0%, 94.3%, and 95.4% for the three radar image types on our self-built dataset. After applying the adaptive weighted fusion method, the F1-score further improved to 98.1%. [ABSTRACT FROM AUTHOR]
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- 2024
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15. AOHDL: Adversarial Optimized Hybrid Deep Learning Design for Preventing Attack in Radar Target Detection.
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Akhtar, Muhammad Moin, Li, Yong, Cheng, Wei, Dong, Limeng, Tan, Yumei, and Geng, Langhuan
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RADAR targets , *GENERATIVE adversarial networks , *AUTONOMOUS vehicles , *RADAR , *DETECTORS - Abstract
In autonomous driving, Frequency-Modulated Continuous-Wave (FMCW) radar has gained widespread acceptance for target detection due to its resilience and dependability under diverse weather and illumination circumstances. Although deep learning radar target identification models have seen fast improvement, there is a lack of research on their susceptibility to adversarial attacks. Various spoofing attack techniques have been suggested to target radar sensors by deliberately sending certain signals through specialized devices. In this paper, we proposed a new adversarial deep learning network for spoofing attacks in radar target detection (RTD). Multi-level adversarial attack prevention using deep learning is designed for the coherence pulse deep feature map from DAALnet and Range-Doppler (RD) map from TDDLnet. After the discrimination of the attack, optimization of hybrid deep learning (OHDL) integrated with enhanced PSO is used to predict the range and velocity of the target. Simulations are performed to evaluate the sensitivity of AOHDL for different radar environment configurations. RMSE of AOHDL is almost the same as OHDL without attack conditions and it outperforms the earlier RTD implementations. [ABSTRACT FROM AUTHOR]
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- 2024
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16. Joint Constant-Modulus Waveform and RIS Phase Shift Design for Terahertz Dual-Function MIMO Radar and Communication System.
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Yang, Rui, Jiang, Hong, and Qu, Liangdong
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MIMO systems , *MIMO radar , *OPTIMIZATION algorithms , *TELECOMMUNICATION systems , *RADAR , *TERAHERTZ technology - Abstract
This paper considers a terahertz (THz) dual-function multi-input multi-output (MIMO) radar and communication system with the assistance of a reconfigurable intelligent surface (RIS) and jointly designs the constant modulus (CM) waveform and RIS phase shifts. A weighted optimization scheme is presented, to minimize the weighted sum of three objectives, including communication multi-user interference (MUI) energy, the negative of multi-target illumination power and the MIMO radar waveform similarity error under a CM constraint. For the formulated non-convex problem, a novel alternating coordinate descent (ACD) algorithm is introduced, to transform it into two subproblems for waveform and phase shift design. Unlike the existing optimization algorithms that solve each subproblem by iteratively approximating the optimal solution with iteration stepsize selection, the ACD algorithm can alternately solve each subproblem by dividing it into multiple simpler problems, to achieve closed-form solutions. Our numerical simulations demonstrate the superiorities of the ACD algorithm over the existing methods. In addition, the impacts of the weighting coefficients, RIS and channel conditions on the radar communication performance of the THz system are analyzed. [ABSTRACT FROM AUTHOR]
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- 2024
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17. Volume-Based Occupancy Detection for In-Cabin Applications by Millimeter Wave Radar.
- Author
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Gharamohammadi, Ali, Dabak, Anand G., Yang, Zigang, Khajepour, Amir, and Shaker, George
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MILLIMETER waves , *SMART cities , *ARTIFICIAL intelligence , *RADAR , *AUTONOMOUS vehicles - Abstract
In-cabin occupancy detection has become increasingly important due to incidents involving children left in vehicles under extreme temperature conditions. Frequency modulated continuous wave (FMCW) radars are widely used for non-contact monitoring and sensing applications, particularly for occupancy detection. However, the confined and metallic environment inside vehicle cabins presents significant challenges due to multipath reflections. This paper introduces a novel approach that detects the occupied space in each seat to determine occupancy, using the variance of detected points as an indicator of volume occupancy. In an experimental study involving 70 different scenarios with single and multiple subjects, we classify occupants in each seat into one of three categories: adult, baby, or empty. The proposed method achieves an overall accuracy of 96.7% using an Adaboost classifier and a miss-detection rate of 1.8% for detecting babies. This approach demonstrates superior robustness to multipath interference compared to traditional energy-based methods, offering a significant advancement in in-cabin occupancy detection technology. [ABSTRACT FROM AUTHOR]
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- 2024
- Full Text
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18. Distributed Phased Multiple-Input Multiple-Output Radars for Early Warning: Observation Area Generation.
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Luo, Dengsanlang and Wen, Gongjian
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PHASED array radar , *BEAMFORMING , *RESOURCE management , *RADAR , *COMPUTER simulation , *MIMO radar - Abstract
This paper introduces a resource management approach for distributed multiple-input multiple-output (MIMO) radar systems equipped with phased array antennas. The approach focuses and adjusts narrow beams from all transmit and receive nodes to generate a regularly shaped observation area for reliable detection. Based on this, a structured early warning framework can be built by evenly arranging sufficient observation areas to cover the surveillance region and periodically scanning these areas for continuous monitoring. Observation area generation, a core technique for this framework, involves the joint optimization of beamforming weights for both transmit and receive nodes, as well as the beam dwell time. Our optimization strategy is designed to achieve two key objectives: minimizing beam dwell time to ensure rapid alerts for defense systems, and minimizing node transmit power to extend operational time while reducing the risk of intercept. To address the problem of observation area generation, we propose a two-stage method. The first stage uses the signal-to-clutter-plus-noise ratio (SCNR) as a new criterion to determine the transmit and receive beamforming weights. The second stage employs a power factor as an additional variable to scale the transmit beamforming weights under various beam dwell times, constructing a Pareto solution set for the problem. Numerical simulations validate the effectiveness of our method, demonstrating improved detection capabilities compared to monostatic phased array radar systems. [ABSTRACT FROM AUTHOR]
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- 2024
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19. A Multi-Objective Intelligent Optimization Method for Sensor Array Optimization in Distributed SAR-GMTI Radar Systems.
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Li, Xianghai, Wang, Rong, Liang, Gengchen, and Yang, Zhiwei
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OPTIMIZATION algorithms , *SENSOR arrays , *COVARIANCE matrices , *RADAR , *PROBLEM solving - Abstract
The design and optimization of sensor array configurations is a significant challenge for distributed SAR-GMTI radar systems because the system performance of distributed array radar is a comprehensive result of several conflicting evaluation indicators. This paper developed a multi-objective intelligent optimization method to solve the global optimal problem of array configurations in terms of achieving optimal GMTI performance. Firstly, to formulate the relationship between array configuration and GMTI performance, we established three objective functions derived from evaluating indicators of SAR-GMTI performance. Specifically, in the objective functions, we proposed a novel clutter covariance matrix model that added several typical non-ideal factors of the real-world detection environment. This provides a way to build a bridge between the array configuration, environment clutter, and GMTI performance. Then, we proposed an improved multi-objective snake optimization algorithm (IMOSOA) that combined the Pareto optimization mechanism with snake optimization to solve the multi-objective optimization problem while reconciling the conflicts between different objective functions. Meanwhile, some significant improvements were made to speed up convergence. That is, tent chaotic mapping-based initialization, multi-group coevolution, and individual mutation strategies were applied to solve the non-convergence problem of global searching. Finally, in the case of an airborne SAR-GMTI system, numerical experiments demonstrated that the proposed IMOSOA has superior performance than other contrast methods, especially in terms of GMTI applications. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
20. Sensing-Efficient Transmit Beamforming for ISAC with MIMO Radar and MU-MIMO Communication.
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Liu, Huimin, Li, Yong, Cheng, Wei, Dong, Limeng, and Yan, Beiming
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TRANSMITTING antennas , *REGULARIZATION parameter , *BEAMFORMING , *RADAR , *FAIRNESS - Abstract
We focus on an integrated sensing and communication (ISAC) system—a single platform equipped with multiple antennas transmitting a waveform to detect targets and communicate with downlink users. Due to spectrum sharing between multiple-input–multiple-output (MIMO) radar and multiuser MIMO (MU-MIMO) communication, beamforming is becoming increasingly important as a technique that enables the creation of directional beams. In this paper, we propose a novel joint transmit beamforming design scheme that employs a beam pattern approximation strategy for radar sensing and utilizes rate-splitting for multiuser communication offering advanced interference management strategies. The optimization problems are formulated from both radar-centric and trade-off viewpoints. First, we propose a radar-centric beamforming scheme to achieve sensing efficiency through beam pattern approximation, while requiring the fairness signal-to-interference-plus-noise ratio (SINR) to be higher than a given threshold to guarantee a minimal level of communication quality, while the obtained performance for the communication system is limited in this scheme. To address this problem, we propose a beamforming design scheme from a trade-off viewpoint that flexibly optimizes both sensing and communication performances with a regularization parameter. Finally, we propose a partial rate-splitting-based beamforming design method aimed at maximizing the effective sensing power, with the constraint of a minimal sum rate for downlink users. Numerical results are provided to assess the effectiveness of all proposed schemes. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
21. Limited Sample Radar HRRP Recognition Using FWA-GAN.
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Song, Yiheng, Zhang, Liang, and Wang, Yanhua
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ARTIFICIAL neural networks , *GENERATIVE adversarial networks , *RESEARCH personnel , *RADAR - Abstract
In radar High-Resolution Range Profile (HRRP) target recognition, the targets of interest are always non-cooperative, posing a significant challenge in acquiring sufficient samples. This limitation results in the prevalent issue of limited sample availability. To mitigate this problem, researchers have sought to integrate handcrafted features into deep neural networks, thereby augmenting the information content. Nevertheless, existing methodologies for fusing handcrafted and deep features often resort to simplistic addition or concatenation approaches, which fail to fully capitalize on the complementary strengths of both feature types. To address these shortcomings, this paper introduces a novel radar HRRP feature fusion technique grounded in the Feature Weight Assignment Generative Adversarial Network (FWA-GAN) framework. This method leverages the generative adversarial network architecture to facilitate feature fusion in an innovative manner. Specifically, it employs the Feature Weight Assignment Model (FWA) to adaptively assign attention weights to both handcrafted and deep features. This approach enables a more efficient utilization and seamless integration of both feature modalities, thereby enhancing the overall recognition performance under conditions of limited sample availability. As a result, the recognition rate increases by over 4% compared to other state-of-the-art methods on both the simulation and experimental datasets. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
22. Perturbation Transmit Beamformer Based Fast Constant Modulus MIMO Radar Waveform Design.
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Zheng, Hao, Wu, Hao, Zhang, Yinghui, Yan, Junkun, Xu, Jian, and Sun, Yantao
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LINEAR programming , *CROSS correlation , *RADAR , *COMPUTER simulation , *MIMO radar , *SIGNALS & signaling - Abstract
In this paper, a fast method to generate a constant-modulus (CM) waveform for a multiple-input, multiple-output, (MIMO) radar is proposed. To simplify the optimization process, the design of the transmit waveform is decoupled from the design of transmit beamformers (TBs) and subpulses. To further improve the computational efficiency, the TBs' optimization is conducted in parallel, and a linear programming model is proposed to match the desired beampattern. Additionally, we incorporate the perturbation vectors into the TBs' optimization so that the TBs can be adjusted to satisfy the CM constraint. To quickly generate the CM subpulses with the desired range-compression (RC) performance, the classical linear frequency modulation (LFM) signal and non-LFM (NLFM) are adopted as subpulses. Meanwhile, to guarantee the RC performance of the final angular waveform, the selection of LFM signal parameters is analyzed to achieve a low cross-correlation between subpulses. Numerical simulations verify the transmit beampattern performance, RC performance, and computational efficiency of the proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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23. Deciphering Optimal Radar Ensemble for Advancing Sleep Posture Prediction through Multiview Convolutional Neural Network (MVCNN) Approach Using Spatial Radio Echo Map (SREM).
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Lai, Derek Ka-Hei, Tam, Andy Yiu-Chau, So, Bryan Pak-Hei, Chan, Andy Chi-Ho, Zha, Li-Wen, Wong, Duo Wai-Chi, and Cheung, James Chung-Wai
- Subjects
- *
CONVOLUTIONAL neural networks , *TRANSFORMER models , *SLEEP quality , *SLEEP apnea syndromes , *MULTISENSOR data fusion - Abstract
Assessing sleep posture, a critical component in sleep tests, is crucial for understanding an individual's sleep quality and identifying potential sleep disorders. However, monitoring sleep posture has traditionally posed significant challenges due to factors such as low light conditions and obstructions like blankets. The use of radar technolsogy could be a potential solution. The objective of this study is to identify the optimal quantity and placement of radar sensors to achieve accurate sleep posture estimation. We invited 70 participants to assume nine different sleep postures under blankets of varying thicknesses. This was conducted in a setting equipped with a baseline of eight radars—three positioned at the headboard and five along the side. We proposed a novel technique for generating radar maps, Spatial Radio Echo Map (SREM), designed specifically for data fusion across multiple radars. Sleep posture estimation was conducted using a Multiview Convolutional Neural Network (MVCNN), which serves as the overarching framework for the comparative evaluation of various deep feature extractors, including ResNet-50, EfficientNet-50, DenseNet-121, PHResNet-50, Attention-50, and Swin Transformer. Among these, DenseNet-121 achieved the highest accuracy, scoring 0.534 and 0.804 for nine-class coarse- and four-class fine-grained classification, respectively. This led to further analysis on the optimal ensemble of radars. For the radars positioned at the head, a single left-located radar proved both essential and sufficient, achieving an accuracy of 0.809. When only one central head radar was used, omitting the central side radar and retaining only the three upper-body radars resulted in accuracies of 0.779 and 0.753, respectively. This study established the foundation for determining the optimal sensor configuration in this application, while also exploring the trade-offs between accuracy and the use of fewer sensors. [ABSTRACT FROM AUTHOR]
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- 2024
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24. Short-Term Precipitation Radar Echo Extrapolation Method Based on the MS-DD3D-RSTN Network and STLoss Function.
- Author
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Yang, Wulin, Yang, Hao, Zhou, Hang, Dong, Yuanchang, Zhang, Chenghong, and Chen, Chaoping
- Subjects
- *
PRECIPITATION forecasting , *DEEP learning , *RADAR , *EXTRAPOLATION , *FORECASTING - Abstract
Short-term precipitation forecasting is essential for agriculture, transportation, urban management, and tourism. The radar echo extrapolation method is widely used in precipitation forecasting. To address issues like forecast degradation, insufficient capture of spatiotemporal dependencies, and low accuracy in radar echo extrapolation, we propose a new model: MS-DD3D-RSTN. This model employs spatiotemporal convolutional blocks (STCBs) as spatiotemporal feature extractors and uses the spatial-temporal loss (STLoss) function to learn intra-frame and inter-frame changes for end-to-end training, thereby capturing the spatiotemporal dependencies in radar echo signals. Experiments on the Sichuan dataset and the HKO-7 dataset show that the proposed model outperforms advanced models in terms of CSI and POD evaluation metrics. For 2 h forecasts with 20 dBZ and 30 dBZ reflectivity thresholds, the CSI metrics reached 0.538, 0.386, 0.485, and 0.198, respectively, representing the best levels among existing methods. The experiments demonstrate that the MS-DD3D-RSTN model enhances the ability to capture spatiotemporal dependencies, mitigates forecast degradation, and further improves radar echo prediction performance. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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25. MF-Match: A Semi-Supervised Model for Human Action Recognition.
- Author
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Yun, Tianhe and Wang, Zhangang
- Subjects
- *
HUMAN activity recognition , *MACHINE learning , *TECHNOLOGICAL innovations , *RADAR , *ACCURACY of information - Abstract
Human action recognition (HAR) technology based on radar signals has garnered significant attention from both industry and academia due to its exceptional privacy-preserving capabilities, noncontact sensing characteristics, and insensitivity to lighting conditions. However, the scarcity of accurately labeled human radar data poses a significant challenge in meeting the demand for large-scale training datasets required by deep model-based HAR technology, thus substantially impeding technological advancements in this field. To address this issue, a semi-supervised learning algorithm, MF-Match, is proposed in this paper. This algorithm computes pseudo-labels for larger-scale unsupervised radar data, enabling the model to extract embedded human behavioral information and enhance the accuracy of HAR algorithms. Furthermore, the method incorporates contrastive learning principles to improve the quality of model-generated pseudo-labels and mitigate the impact of mislabeled pseudo-labels on recognition performance. Experimental results demonstrate that this method achieves action recognition accuracies of 86.69% and 91.48% on two widely used radar spectrum datasets, respectively, utilizing only 10% labeled data, thereby validating the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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26. Overview of Radar Alignment Methods and Analysis of Radar Misalignment's Impact on Active Safety and Autonomous Systems.
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Burza, Rafał Michał
- Subjects
- *
ROAD users , *TRACKING algorithms , *MULTISENSOR data fusion , *SYSTEM safety , *AUTONOMOUS vehicles , *TRACKING radar - Abstract
The rapid development of active safety systems in the automotive industry and research in autonomous driving requires reliable, high-precision sensors that provide rich information about the surrounding environment and the behaviour of other road users. In practice, there is always some non-zero mounting misalignment, i.e., angular inaccuracy in a sensor's mounting on a vehicle. It is essential to accurately estimate and compensate for this misalignment further programmatically (in software). In the case of radars, imprecise mounting may result in incorrect/inaccurate target information, problems with the tracking algorithm, or a decrease in the power reflected from the target. Sensor misalignment should be mitigated in two ways: through the correction of an inaccurate alignment angle via the estimated value of the misalignment angle or alerting other components of the system of potential sensor degradation if the misalignment is beyond the operational range. This work analyses misalignment's influences on radar sensors and other system components. In the mathematically proven example of a vertically misaligned radar, pedestrian detectability dropped to one-third of the maximum range. In addition, mathematically derived heading estimation errors demonstrate the impact on data association in data fusion. The simulation results presented show that the angle of misalignment exponentially increases the risk of false track splitting. Additionally, the paper presents a comprehensive review of radar alignment techniques, mostly found in the patent literature, and implements a baseline algorithm, along with suggested key performance indicators (KPIs) to facilitate comparisons for other researchers. [ABSTRACT FROM AUTHOR]
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- 2024
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27. Flight Attitude Estimation with Radar for Remote Sensing Applications.
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Weber, Christoph, Eggert, Marius, and Udelhoven, Thomas
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REMOTE sensing , *REMOTE sensing by radar , *DRONE aircraft , *UNITS of measurement , *RADAR - Abstract
Unmanned aerial vehicles (UAVs) and radar technology have benefitted from breakthroughs in recent decades. Both technologies have found applications independently of each other, but together, they also unlock new possibilities, especially for remote sensing applications. One of the key factors for a remote sensing system is the estimation of the flight attitude. Despite the advancements, accurate attitude estimation remains a significant challenge, particularly due to the limitations of a conventional Inertial Measurement Unit (IMU). Because these sensors may suffer from issues such as drifting, additional effort is required to obtain a stable attitude. Against that background, this study introduces a novel methodology for making an attitude estimation using radar data. Herein, we present a drone measurement system and detail its calculation process. We also demonstrate our results using three flight scenarios and outline the limitations of the approach. The results show that the roll and pitch angles can be calculated using the radar data, and we conclude that the findings of this research will help to improve the flight attitude estimation of remote sensing flights with a radar sensor. [ABSTRACT FROM AUTHOR]
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- 2024
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28. Study on Optimization Method for InSAR Baseline Considering Changes in Vegetation Coverage.
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Guo, Junqi, Xi, Wenfei, Yang, Zhiquan, Huang, Guangcai, Xiao, Bo, Jin, Tingting, Hong, Wenyu, Gui, Fuyu, and Ma, Yijie
- Subjects
- *
SYNTHETIC aperture radar , *VEGETATION dynamics , *DEFORMATION of surfaces , *LAND subsidence , *LANDSLIDES , *RADAR - Abstract
Time-series Interferometric Synthetic Aperture Radar (InSAR) technology, renowned for its high-precision, wide coverage, and all-weather capabilities, has become an essential tool for Earth observation. However, the quality of the interferometric baseline network significantly influences the monitoring accuracy of InSAR technology. Therefore, optimizing the interferometric baseline is crucial for enhancing InSAR's monitoring accuracy. Surface vegetation changes can disrupt the coherence between SAR images, introducing incoherent noise into interferograms and reducing InSAR's monitoring accuracy. To address this issue, we propose and validate an optimization method for the InSAR baseline that considers changes in vegetation coverage (OM-InSAR-BCCVC) in the Yuanmou dry-hot valley. Initially, based on the imaging times of SAR image pairs, we categorize all interferometric image pairs into those captured during months of high vegetation coverage and those from months of low vegetation coverage. We then remove the image pairs with coherence coefficients below the category average. Using the Small Baseline Subset InSAR (SBAS-InSAR) technique, we retrieve surface deformation information in the Yuanmou dry-hot valley. Landslide identification is subsequently verified using optical remote sensing images. The results show that significant seasonal changes in vegetation coverage in the Yuanmou dry-hot valley lead to noticeable seasonal variations in InSAR coherence, with the lowest coherence in July, August, and September, and the highest in January, February, and December. The average coherence threshold method is limited in this context, resulting in discontinuities in the interferometric baseline network. Compared with methods without baseline optimization, the interferometric map ratio improved by 17.5% overall after applying the OM-InSAR-BCCVC method, and the overall inversion error RMSE decreased by 0.5 rad. From January 2021 to May 2023, the radar line of sight (LOS) surface deformation rate in the Yuanmou dry-hot valley, obtained after atmospheric correction by GACOS, baseline optimization, and geometric distortion region masking, ranged from −73.87 mm/year to 127.35 mm/year. We identified fifteen landslides and potential landslide sites, primarily located in the northern part of the Yuanmou dry-hot valley, with maximum subsidence exceeding 100 mm at two notable points. The OM-InSAR-BCCVC method effectively reduces incoherent noise caused by vegetation coverage changes, thereby improving the monitoring accuracy of InSAR. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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29. Radar Target Classification Using Enhanced Doppler Spectrograms with ResNet34_CA in Ubiquitous Radar.
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Song, Qiang, Huang, Shilin, Zhang, Yue, Chen, Xiaolong, Chen, Zebin, Zhou, Xinyun, and Deng, Zhenmiao
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- *
RADAR targets , *SPECTROGRAMS , *RADAR , *CLASSIFICATION , *AIRPORTS - Abstract
Ubiquitous Radar has become an essential tool for preventing bird strikes at airports, where accurate target classification is of paramount importance. The working mode of Ubiquitous Radar, which operates in track-then-identify (TTI) mode, provides both tracking information and Doppler information for the classification and recognition module. Moreover, the main features of the target's Doppler information are concentrated around the Doppler main spectrum. This study innovatively used tracking information to generate a feature enhancement layer that can indicate the area where the main spectrum is located and combines it with the RGB three-channel Doppler spectrogram to form an RGBA four-channel Doppler spectrogram. Compared with the RGB three-channel Doppler spectrogram, this method increases the classification accuracy for four types of targets (ships, birds, flapping birds, and bird flocks) from 93.13% to 97.13%, an improvement of 4%. On this basis, this study integrated the coordinate attention (CA) module into the building block of the 34-layer residual network (ResNet34), forming ResNet34_CA. This integration enables the network to focus more on the main spectrum information of the target, thereby further improving the classification accuracy from 97.13% to 97.22%. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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30. A Novel SAR Imaging Method for GEO Satellite–Ground Bistatic SAR System with Severe Azimuth Spectrum Aliasing and 2-D Spatial Variability.
- Author
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Ti, Jingjing, Suo, Zhiyong, Liang, Yi, Zhao, Bingji, and Xi, Jiabao
- Subjects
- *
BEIDOU satellite navigation system , *SYNTHETIC aperture radar , *AZIMUTH , *ACCOUNTING methods , *RADAR , *BISTATIC radar , *FLOW charts - Abstract
The satellite–ground bistatic configuration, which uses geosynchronous synthetic aperture radar (GEO SAR) for illumination and ground equipment for reception, can achieve wide coverage, high revisit, and continuous illumination of interest areas. Based on the analysis of the signal characteristics of GEO satellite–ground bistatic SAR (GEO SG-BiSAR), it is found that the bistatic echo signal has problems of azimuth spectrum aliasing and 2-D spatial variability. Therefore, to overcome those problems, a novel SAR imaging method for a GEO SG-BiSAR system with severe azimuth spectrum aliasing and 2-D spatial variability is proposed. Firstly, based on the geometric configuration of the GEO SG-BiSAR system, the time-domain and frequency-domain expressions of the signal are derived in detail. Secondly, in order to avoid the increasing cost caused by traditional multi-channel reception technology and the processing burden caused by inter-channel errors, the azimuth deramping is executed to solve the azimuth spectrum aliasing of the signal under the special geometric structure of GEO SG-BiSAR. Thirdly, based on the investigation of azimuth and range spatial variability characteristics of GEO SG-BiSAR in the Range Doppler (RD) domain, the azimuth spatial variability correction strategy is proposed. The signal corrected by the correction strategy has the same migration characteristics as monostatic radar. Therefore, the traditional chirp scaling function (CSF) is also modified to solve the range spatial variability of the signal. Finally, the two-dimensional spectrum of GEO SG-BiSAR with modified chirp scaling processing is derived, followed by the SPECAN operation to obtain the focused SAR image. Furthermore, the completed flowchart is also given to display the main composed parts for GEO SG-BiSAR imaging. Both azimuth spectrum aliasing and 2-D spatial variability are taken into account in the imaging method. The simulated data and the real data obtained by the Beidou navigation satellite are used to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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31. On Unsupervised Multiclass Change Detection Using Dual-Polarimetric SAR Data.
- Author
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Kim, Minhwa, Lee, Seung-Jae, and Park, Sang-Eun
- Subjects
- *
SYNTHETIC aperture radar , *SIGNAL detection , *INFORMATION resources , *RADAR , *CLASSIFICATION - Abstract
Change detection using SAR data has been an active topic in various applications. Because conventional change detection identifies signal changes in single-pol radar observations, they cannot separately detect different kinds of change associated with different ground parameters. In this study, we investigated the comprehensive use of dual-pol parameters and proposed a novel dual-pol-based change detection framework utilizing different dual-pol scatter-type indicators. To optimize the exploitation of dual-pol change information, we presented a two-step processing strategy that divides the multiclass change detection process into a binary detection step that identifies the presence of changes and the classification step that distinguishes the types of change. In the detection stage, each dual-pol parameter was considered as an independent information source. Assuming potential conflict between dual-pol parameters, a disjunctive combination of detection results from different dual-pol parameters was applied to obtain the final detection result. In the classification step, an unsupervised change classification strategy was proposed based on the change direction and magnitude of the dual-pol parameters within the change class. Experimental results exhibited significantly improved detectability across a wide change spectrum compared with previous dual-pol-based change detection approaches. They also demonstrated the possibility of distinguishing different semantic changes without in situ ground data. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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32. Interrupted-Sampling Repeater Jamming Countermeasure Based on Intrapulse Frequency–Coded Joint Frequency Modulation Slope Agile Waveform.
- Author
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Wang, Xiaoge, Li, Binbin, Chen, Hui, Liu, Weijian, Zhu, Yongzhe, Luo, Jun, and Ni, Liuliu
- Subjects
- *
INTERFERENCE suppression , *ELECTRONIC countermeasures , *COMPRESSED sensing , *FOURIER transforms , *RADAR , *RADAR interference - Abstract
Interrupted-sampling repeater jamming (ISRJ) is widely used in the field of electronic countermeasures, and can severely affect radar detection. Therefore, the problem of ISRJ suppression is a compelling task. In this paper, we propose an ISRJ suppression method based on an intrapulse frequency-coded joint frequency modulation (FM) slope agile waveform. The intrapulse frequency-coded joint FM slope agile waveform is first designed. The delay inserted between subpulses makes the waveform easy to implement in engineering, and the ambiguity function diagram of the waveform approximates the ideal thumbtack type. Next, the echo slices are classified in the fractional domain utilizing the discontinuity of ISRJ and the focusing property of fractional Fourier transform for chirp signals. Then, the target and interference in the interfered echo slices are reconstructed by compressed sensing, and a time-domain filter is constructed based on interference-free echo slices. Finally, the echo signal after interference suppression is further filtered in the time domain to degrade range sidelobes. Simulation results show that the proposed method can effectively suppress three typical types of ISRJ. Moreover, the probability of target detection after interference suppression exceeds 90% when the jamming-to-signal ratio equals 50 dB. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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33. Investigating Intra-Pulse Doppler Frequency Coupled in the Radar Echo Signal of a Plasma Sheath-Enveloped Target.
- Author
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Bai, Bowen, Pu, Bailiang, Zhang, Ke, Yang, Yilin, Li, Xiaoping, and Liu, Yanming
- Subjects
- *
PLASMA sheaths , *RELATIVE motion , *PLASMA frequencies , *PARAMETER estimation , *RADAR , *TRACKING radar , *DOPPLER effect - Abstract
In detecting hypersonic vehicles, the radar echo signal is coupled with an intra-pulse Doppler frequency (I-D frequency) component caused by relative motion of a plasma sheath (PSh) and the vehicle, which can induce the phenomenon of a ghost target in a one-dimensional range profile. In order to investigate the I-D frequency generated by the relative motion of a PSh, this study transforms a linear frequency modulated (LFM) signal into a single carrier frequency signal based on echo signal equivalent time delay-dechirp processing and realizes high resolution and fast extraction of the I-D frequency coupled with the frequency-domain echo signal. Furthermore, by relying on the computation of the surface flow field of the RAMC-II Blunt Cone Reentry Vehicle, the coupled I-D frequency in the radar echo signal of a PSh-enveloped target under circumstances of typical altitudes and carrier frequencies is extracted and further investigated, revealing the variation law of I-D frequency. The key findings of this study provide a novel approach for suppressing anomalies in radar detection of PSh-enveloped targets as well as effective detecting and as robust target tracking. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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34. A Waveform Design for Integrated Radar and Jamming Based on Smart Modulation and Complementary Coding.
- Author
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Yan, Huabin, Zhang, Shiyuan, Lu, Xingyu, Yang, Jianchao, Duan, Lunhao, Tan, Ke, and Gu, Hong
- Subjects
- *
BINARY sequences , *MODULATION coding , *PHASE coding , *SIGNAL detection , *RADAR - Abstract
Waveform design for integrated radar and jamming is generally based on the concept of shared waveform, which uses jamming signals without typical radar signal characteristics for detection. Existing waveforms have shown limited design flexibility, high levels of sidelobe in detection results, and overall ordinary performance. We propose an integrated radar and jamming waveform based on smart modulation and complementary coding. Unlike traditional integrated radar and jamming waveform based on smart modulation, the phase angle of the binary phase-coded sequence is adjustable in this smart modulation method, allowing for a controllable jamming effect, achieving true smart modulation. However, this smart modulation waveform also suffers from high sidelobes in detection. To address this issue, we take a complementary coding approach and design a smart modulation waveform with complementary characteristics. This waveform can synthesize a complete linear frequency modulation (LFM) signal by adding two pulses together, thereby reducing the sidelobes in the smart modulation waveform and enhancing its detection performance. Theoretical analysis indicates that the detection and jamming effects of this integrated waveform can be flexibly controlled by adjusting the phase angles of the coding sequences. Simulation analysis and experimental results confirm the significant advantages of this waveform. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
35. Realizing Small UAV Targets Recognition via Multi-Dimensional Feature Fusion of High-Resolution Radar.
- Author
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Jiang, Wen, Liu, Zhen, Wang, Yanping, Lin, Yun, Li, Yang, and Bi, Fukun
- Subjects
- *
RADAR targets , *DRONE aircraft , *TRACKING radar , *DEEP learning , *RADAR , *SPECTROGRAMS - Abstract
For modern radar systems, small unmanned aerial vehicles (UAVs) belong to a typical types of targets with 'low, slow, and small' characteristics. In complex combat environments, the functional requirements of radar systems are not only limited to achieving stable detection and tracking performance but also to effectively complete the recognition of small UAV targets. In this paper, a multi-dimensional feature fusion framework for small UAV target recognition utilizing a small-sized and low-cost high-resolution radar is proposed, which can fully extract and combine the geometric structure features and the micro-motion features of small UAV targets. For the performance analysis, the echo data of different small UAV targets was measured and collected with a millimeter-wave radar, and the dataset consists of high-resolution range profiles (HRRP) and micro-Doppler time–frequency spectrograms was constructed for training and testing. The effectiveness of the proposed method was demonstrated by a series of comparison experiments, and the overall accuracy of the proposed method can reach 98.5%, which demonstrates that the proposed multi-dimensional feature fusion method can achieve better recognition performance than that of classical algorithms and higher robustness than that of single features for small UAV targets. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
36. Power Allocation Scheme for Multi-Static Radar to Stably Track Self-Defense Jammers.
- Author
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Zhang, Gangsheng, Xie, Junwei, Zhang, Haowei, Feng, Weike, Liu, Mingjie, and Qin, Cong
- Subjects
- *
RADAR targets , *TRACKING radar , *POWER resources , *RADAR , *RESOURCE allocation , *SELF-defense , *ECHO - Abstract
Due to suppression jamming by jammers, the signal-to-interference-plus-noise ratio (SINR) during tracking tasks is significantly reduced, thereby decreasing the target detection probability of radar systems. This may result in the interruption of the target track. To address this issue, we propose a multi-static radar power allocation algorithm that enhances the detection and tracking performance of multiple radars in relation to their targets by optimizing power resource allocation. Initially, the echo signal model and measurement model of multi-static radar are formulated, followed by the derivation of the Bayesian Cramér–Rao lower bound (BCRLB). The multi-objective optimization method is utilized to establish the objective function for joint tracking and detection, with dynamic adjustment of the weight coefficient to balance the tracking and detection performance of multiple radars. This ensures the reliability and anti-jamming capability of the multi-static radar system. Simulation results indicate that the proposed algorithm can prevent the interruption of jammer tracking and maintain robust tracking performance. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
37. A New Perspective on the Scattering Mechanism of S-Band Weather Radar Clear-Air Echoes Based on Communication Models.
- Author
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Teng, Yupeng, Li, Tianyan, Chen, Hongbin, Ma, Shuqing, Wu, Lei, Xia, Yunjie, and Li, Siteng
- Subjects
- *
TURBULENCE , *COMMUNICATION models , *RADAR , *RADAR meteorology - Abstract
Clear-air echo studies are usually based on isotropic turbulence theory. But the theory has been considered incomplete by modern turbulence theory. The intermittence of turbulence can reveal obvious shortcomings in the existing studies of clear-air echoes. The mechanism of clear-air echo scattering needs to be supplemented. This paper introduces the troposcatter theory, normally used in over-the-horizon communication, to fill the gap left by Bragg scattering. By treating radar as a self-transmitted and self-received device, the equivalent transmission loss of weather radar is established and compared with the recommendations of the Radiocommunication Sector of the International Telecommunication Union (ITU-R). The results show that the S-band radar transmission loss aligns with ITU-R recommendations. There is also a linear regression relationship between the radar transmission loss and height, which conforms to the troposcatter theory. This means that the theory of troposcatter scattering is a supplement to the theory of Bragg scattering. Tropospheric scattering can be thought of as general Bragg scattering. Meanwhile, based on ITU-R recommendations, this study also provides a new way for the recognition of biological echoes. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
38. Spatiotemporal Predictive Learning for Radar-Based Precipitation Nowcasting.
- Author
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Wang, Xiaoying, Zhao, Haixiang, Zhang, Guojing, Guan, Qin, and Zhu, Yu
- Subjects
- *
RADAR meteorology , *DEEP learning , *PERCEPTUAL learning , *FALSE alarms , *RADAR - Abstract
Based on C-band weather radar and ground precipitation data from the Helan Mountain area in Yinchuan between 2017 to 2020, we evaluated the forecasting performances of 15 mainstream deep learning models used in recent years, including recurrent-based and recurrent-free models. The critical success index (CSI), probability of detection (POD), false alarm rate (FAR), mean square error (MSE), mean absolute error (MAE), and learned perceptual image patch similarity (LPIPS), were used to evaluate the forecasting abilities. The results showed that (1) recurrent-free models have significant parameter quantity and computing power advantages, especially the SimVP model. Among the recurrent-based models, PredRNN and PredRNN++ demonstrate good predictive capabilities for changes in echolocation and intensity, PredRNN++ performs better in predicting long sequences (1 h); (2) SimVP uses Inception to extract temporal features, which cannot capture the complex physical changes in radar echo images and fails to extract spatial–temporal correlations and accurately predict heavy rainfall areas effectively. Therefore, we constructed the SimVP-GMA model, replacing the temporal prediction module in SimVP and modifying the spatial encoder part. Compared with SimVP, the MSE and LPIPS indicators were improved by 0.55 and 0.0193, respectively. It can be seen from the forecast images that the forecast details have been significantly improved, especially in the forecasting of heavy rainfall weather. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
39. Extracting Vehicle Trajectories from Partially Overlapping Roadside Radar.
- Author
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Schrader, Maxwell, Hainen, Alexander, and Bittle, Joshua
- Subjects
- *
RADAR , *AUTONOMOUS vehicles , *ROADSIDE improvement , *SIGNALIZED intersections , *KALMAN filtering , *TRACKING radar , *TRAFFIC safety - Abstract
This work presents a methodology for extracting vehicle trajectories from six partially-overlapping roadside radars through a signalized corridor. The methodology incorporates radar calibration, transformation to the Frenet space, Kalman filtering, short-term prediction, lane-classification, trajectory association, and a covariance intersection-based approach to track fusion. The resulting dataset contains 79,000 fused radar trajectories over a 26-h period, capturing diverse driving scenarios including signalized intersections, merging behavior, and a wide range of speeds. Compared to popular trajectory datasets such as NGSIM and highD, this dataset offers extended temporal coverage, a large number of vehicles, and varied driving conditions. The filtered leader–follower pairs from the dataset provide a substantial number of trajectories suitable for car-following model calibration. The framework and dataset presented in this work has the potential to be leveraged broadly in the study of advanced traffic management systems, autonomous vehicle decision-making, and traffic research. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
40. E-BDL: Enhanced Band-Dependent Learning Framework for Augmented Radar Sensing.
- Author
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Cai, Fulin, Wu, Teresa, and Lure, Fleming Y. M.
- Subjects
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DOPPLER effect , *RADAR , *ALZHEIMER'S disease , *MOTION capture (Human mechanics) , *DEEP learning , *ADAPTIVE filters , *MULTISPECTRAL imaging - Abstract
Radar sensors, leveraging the Doppler effect, enable the nonintrusive capture of kinetic and physiological motions while preserving privacy. Deep learning (DL) facilitates radar sensing for healthcare applications such as gait recognition and vital-sign measurement. However, band-dependent patterns, indicating variations in patterns and power scales associated with frequencies in time–frequency representation (TFR), challenge radar sensing applications using DL. Frequency-dependent characteristics and features with lower power scales may be overlooked during representation learning. This paper proposes an Enhanced Band-Dependent Learning framework (E-BDL) comprising an adaptive sub-band filtering module, a representation learning module, and a sub-view contrastive module to fully detect band-dependent features in sub-frequency bands and leverage them for classification. Experimental validation is conducted on two radar datasets, including gait abnormality recognition for Alzheimer's disease (AD) and AD-related dementia (ADRD) risk evaluation and vital-sign monitoring for hemodynamics scenario classification. For hemodynamics scenario classification, E-BDL-ResNet achieves competitive performance in overall accuracy and class-wise evaluations compared to recent methods. For ADRD risk evaluation, the results demonstrate E-BDL-ResNet's superior performance across all candidate models, highlighting its potential as a clinical tool. E-BDL effectively detects salient sub-bands in TFRs, enhancing representation learning and improving the performance and interpretability of DL-based models. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
41. FMCW Radar Human Action Recognition Based on Asymmetric Convolutional Residual Blocks.
- Author
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Zhang, Yuan, Tang, Haotian, Wu, Ye, Wang, Bolun, and Yang, Dalin
- Subjects
- *
HUMAN activity recognition , *DEEP learning , *FEATURE extraction , *RADAR , *MACHINE learning , *MULTISPECTRAL imaging - Abstract
Human action recognition based on optical and infrared video data is greatly affected by the environment, and feature extraction in traditional machine learning classification methods is complex; therefore, this paper proposes a method for human action recognition using Frequency Modulated Continuous Wave (FMCW) radar based on an asymmetric convolutional residual network. First, the radar echo data are analyzed and processed to extract the micro-Doppler time domain spectrograms of different actions. Second, a strategy combining asymmetric convolution and the Mish activation function is adopted in the residual block of the ResNet18 network to address the limitations of linear and nonlinear transformations in the residual block for micro-Doppler spectrum recognition. This approach aims to enhance the network's ability to learn features effectively. Finally, the Improved Convolutional Block Attention Module (ICBAM) is integrated into the residual block to enhance the model's attention and comprehension of input data. The experimental results demonstrate that the proposed method achieves a high accuracy of 98.28% in action recognition and classification within complex scenes, surpassing classic deep learning approaches. Moreover, this method significantly improves the recognition accuracy for actions with similar micro-Doppler features and demonstrates excellent anti-noise recognition performance. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
42. Sub-Aperture Synthetic Aperture Radar Imaging of Fixed-Platform Beam-Steering Radar for Blast Furnace Burden Surface Detection.
- Author
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Deng, Lifu, Chen, Xianzhong, and Hou, Qingwen
- Subjects
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SYNTHETIC aperture radar , *BLAST furnaces , *SYNTHETIC apertures , *RADAR - Abstract
Due to the scheme of fixed-platform beam-steering radar and the space of the blast furnace being subjected to harsh environmental influences, the traditional detection methods of burden surface are limited by geometric distortion, noncoherent clutter, and noise interference, which leads to an increase in the image entropy value and the equivalent number of views, makes the density distribution of burden surface show a diffuse state, and greatly affects the stability and accuracy. In this paper, a new fixed-platform beam-steering radar synthetic aperture radar imaging method (FPBS-SAR) is proposed in the sensory domain of the blast furnace environment. From the perspective of fixed-platform beam-steering radar motion characteristics, the target range–azimuth coupled distance history model under the sub-aperture is established, the azimuthal Doppler variation characteristics of the fixed-platform beam-steering process are analyzed, and the compensation function of the transform domain for geometric disturbance correction is proposed. For noncoherent noise suppression in blast furnaces, the trimmed geometric mean-order-likelihood CFAR method is proposed to take into account the information of burden surface and clutter suppression. To verify the method, point target simulation and imaging for the industrial field measurement data are carried out. The results indicate that geometric distortion is well eliminated, the image entropy value and the equivalent number of views have decreased, and noncoherent noise in blast furnaces is suppressed. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
43. Spatial–Temporal Joint Design and Optimization of Phase-Coded Waveform for MIMO Radar.
- Author
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Lei, Wei, Zhang, Yue, Chen, Zengping, Chen, Xiaolong, and Song, Qiang
- Subjects
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MIMO radar , *LEAST squares , *DEGREES of freedom , *NONLINEAR equations , *RADAR , *PROBLEM solving - Abstract
By simultaneously transmitting multiple different waveform signals, a multiple-input multiple-output (MIMO) radar possesses higher degrees of freedom and potential in many aspects compared to a traditional phased-array radar. The spatial–temporal characteristics of waveforms are the key to determining their performance. In this paper, a transmitting waveform design method based on spatial–temporal joint (STJ) optimization for a MIMO radar is proposed, where waveforms are designed not only for beam-pattern matching (BPM) but also for minimizing the autocorrelation sidelobes (ACSLs) of the spatial synthesis signals (SSSs) in the directions of interest. Firstly, the STJ model is established, where the two-step strategy and least squares method are utilized for BPM, and the L2p-Norm of the ACSL is constructed as the criterion for temporal characteristics optimization. Secondly, by transforming it into an unconstrained optimization problem about the waveform phase and using the gradient descent (GD) algorithm, the hard, non-convex, high-dimensional, nonlinear optimization problem is solved efficiently. Finally, the method's effectiveness is verified through numerical simulation. The results show that our method is suitable for both orthogonal and partial-correlation MIMO waveform designs and efficiently achieves better spatial–temporal characteristic performances simultaneously in comparison with existing methods. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
44. Concept-Based Explanations for Millimeter Wave Radar Target Recognition.
- Author
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Shang, Qijie, Zheng, Tieran, Zhang, Liwen, Zhang, Youcheng, and Ma, Zhe
- Subjects
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RADAR targets , *NUMBER concept , *MILLIMETER waves , *RADAR , *PROBLEM solving - Abstract
This paper presents exploratory work on the use of Testing with Concept Activation Vectors (TCAV) within a concept-based explanation framework to provide the explainability of millimeter-wave (MMW) radar target recognition. Given that the radar spectrum is difficult for non-domain experts to understand visually, defining concepts for radar remains a significant challenge. In response, drawing from the visual analytical experience of experts, some basic concepts based on brightness, striping, size, and shape are adopted in this paper. However, the simplicity of basic concept definitions sometimes leads to vague correlations with recognition targets and significant variability among individuals, limiting their adaptability to specific tasks. To address these issues, this study proposes a Basic Concept-Guided Deep Embedding Clustering (BCG-DEC) method that can effectively discover task-specific composite concepts. BCG-DEC methodically analyzes the deep semantic information of radar data through four distinct stages from the perspective of concept discovery, ensuring that the concepts discovered accurately conform to the task-specific property of MMW radar target recognition. The experimental results show that the proposed method not only expands the number of concepts but also effectively solves the problem of difficulty in annotating basic concepts. In the ROD2021 MMW radar explainability experiments, the concepts proved crucial for recognizing specific categories of radar targets. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
45. Winter Precipitation Detection Using C- and X-Band Radar Measurements.
- Author
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Ueki, Ayano, Teshiba, Michihiro S., Schvartzman, David, Kirstetter, Pierre-Emmanuel, Palmer, Robert D., Osa, Kohei, Yu, Tian-You, Cheong, Boonleng, and Bodine, David J.
- Subjects
- *
MIE scattering , *ROAD maintenance , *TRAFFIC accidents , *RADAR , *MELTING , *RADAR meteorology , *HAIL - Abstract
Winter continues to witness numerous automobile accidents attributed to graupel and hail precipitation in Japan. Detecting these weather phenomena using radar technology holds promise for reducing the impact of such accidents and improving road maintenance operations. Weather radars operating at different frequencies, such as C- and X-band, prove effective in graupel detection by analyzing variations in backscattered signals within the same radar volume. When particle diameters exceed 5 mm, the study of Mie scattering characteristics across different melting ratios reveals insights. The dual frequency ratio (DFR) shows potential for graupel detection. The DFR presents wider variations with ten-times difference in melting ratios with increased density, offering opportunities for precise detection. Additionally, the DFR amplitude rises with temperature changes. However, for hydrometeor diameters below approximately 3 mm, and within the Rayleigh region, the DFR exhibits minimal fluctuations. Hence, this technique is best suited for diameters exceeding 3 mm for optimal efficacy. Additionally, a "detection alert" for graupel/hail has been proposed. Based on this alert, and with realistic rain/graupel size distributions, graupel/hail can be detected with an approximate probability of 70%. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
46. A Hybrid Integration Method Based on SMC-PHD-TBD for Multiple High-Speed and Highly Maneuverable Targets in Ubiquitous Radar.
- Author
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Chen, Zebin, Peng, Xiangyu, Yang, Junyao, Zhong, Zhanming, Song, Qiang, and Zhang, Yue
- Subjects
- *
RADAR targets , *MATCHED filters , *CLUSTERING of particles , *TRACKING radar , *RADAR , *VELOCITY - Abstract
Based on the characteristic of ubiquitous radar emitting low-gain wide beam, a method of long-time coherent integration (LTCI) is required to enhance target detection capability. However, high-speed and highly maneuverable targets can cause Doppler frequency migration (DFM), range migration (RM), and velocity ambiguity (VA), severely degrading the performance of LTCI. Additionally, the number of targets is unknown and variable, and the presence of clutter further complicates the target tracking problem. To address these challenges, we propose a hybrid integration method to achieve joint detection and estimation of multiple high-speed, and highly maneuverable targets. Firstly, we compensate for first-order RM using the keystone transform (KT) and generate corresponding sub-range-Doppler (SRD) planes with different folding factors to achieve VA compensation. These SRD planes are then stitched together to form an extended range-Doppler (ERD) plane, which covers a broader velocity range. Secondly, during the track-before-detect (TBD) process, tracking is performed directly on the ERD plane. We use the sequential Monte Carlo (SMC) approximation of the probability hypothesis density (PHD) to propagate multi-target states. Additionally, we propose an amplitude-based adaptive prior distribution method and a line spread model (LSM) observation model to compensate for DFM. Since the acceleration of the target is included in the particle state, using particles to search for DFM does not increase the computational load. To address the issue of misclassifying mirror targets as real targets in the SRD plane, we propose a particle space projection method. By stacking the SRD planes to create a folding range-Doppler (FRD) space, particles are projected along the folding factor dimension, and then, the particles are clustered to eliminate the influence of the mirror targets. Finally, through simulation experiments, the superiority of the LSM for targets with acceleration was demonstrated. In comparative experiments, the proposed method showed superior performance and robustness compared to traditional methods, achieving a balance between performance and computational efficiency. Furthermore, the proposed method's capability to detect and track multiple high-speed and highly maneuverable targets was validated using actual data from a ubiquitous radar system. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
47. Validation of GPM DPR Rainfall and Drop Size Distributions Using Disdrometer Observations in the Western Mediterranean.
- Author
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Peinó, Eric, Bech, Joan, Polls, Francesc, Udina, Mireia, Petracca, Marco, Adirosi, Elisa, Gonzalez, Sergi, and Boudevillain, Brice
- Subjects
- *
DROP size distribution , *PRECIPITATION (Chemistry) , *RADAR , *DIAMETER - Abstract
Dual-frequency precipitation radar (DPR) on the Core GPM satellite provides spaceborne three-dimensional observations of precipitation fields and surface rainfall rate with quasi-global coverage. The present study evaluates the behavior of liquid precipitation intensity, radar reflectivity factor (ZKu and ZKa) and drop size distribution (DSD) parameters (weighted mean diameter Dm and intercept parameter Nw) of the GPM DPR-derived products, version 07, from 2014 to 2023. Observations from seven Parsivel disdrometers located in different topographic zones in the Western Mediterranean are taken as ground references. Four matching techniques between satellite estimates and ground level observations were tested, and the best results were found for the so-called optimal comparison approach. Overall, GPM DPR products captured the variability of the observed DSD well at different rainfall intensities. However, overestimation of the mean Dm and underestimation of the mean Nw were observed, being much more sensitive to errors in drop diameters larger than 1.5 mm. Moreover, the lowest errors were found for radar reflectivity factor and Dm, and the highest for Nw and rainfall rate. In addition, the GPM DPR convective and stratiform classification was tested, and a substantial overestimation of stratiform cases compared to disdrometer observations were found. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
48. Intrapulse Modulation Radar Signal Recognition Using CNN with Second-Order STFT-Based Synchrosqueezing Transform.
- Author
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Dong, Ning, Jiang, Hong, Liu, Yipeng, and Zhang, Jingtao
- Subjects
- *
CONVOLUTIONAL neural networks , *SIGNAL classification , *FOURIER transforms , *SIGNAL-to-noise ratio , *RADAR , *PHOTOPLETHYSMOGRAPHY - Abstract
Intrapulse modulation classification of radar signals plays an important role in modern electronic reconnaissance, countermeasures, etc. In this paper, to improve the recognition rate at low signal-to-noise ratio (SNR), we propose a recognition method using the second-order short-time Fourier transform (STFT)-based synchrosqueezing transform (FSST2) combined with a modified convolution neural network, which we name MeNet. In particular, the radar signals are first preprocessed via the time–frequency analysis and STFT-based FSST2. Then, the informative features of the time–frequency images (TFIs) are deeply learned and classified through the MeNet with several specific convolutional blocks. The simulation results show that the overall recognition rate for seven types of intrapulse modulation radar signals can reach 95.6%, even when the SNR is −12 dB. Compared with other networks, the excellent recognition rate proves the superiority of our method. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
49. Millimeter-Wave Radar Detection and Localization of a Human in Indoor Complex Environments.
- Author
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Xing, Zhixuan, Chen, Penghui, Wang, Jun, Bai, Yujing, Song, Jinhao, and Tian, Liuyang
- Subjects
- *
POTTED plants , *RADAR , *TIME management , *BISTATIC radar , *FURNITURE , *DRAPERIES , *LOCALIZATION (Mathematics) - Abstract
Nowadays, it is still a great challenge to detect and locate indoor humans using a frequency-modulated continuous-wave radar accurately. Due to the interference of the indoor environment and complex objects such as green plants, the radar signal may penetrate, reflect, refract, and scatter, and the echo signals will contain noise, clutter, and multipath of different characteristics. Therefore, a method combined with comprehensive non-target signal removal and human localization is proposed to achieve position estimation of a human target. Time-variant clutter is innovatively mitigated through time accumulation using point clustering. Ghost targets are reduced according to propagation path matching. The experimental results show that the method can locate the real target human within an average error of 0.195 m in multiple complex environments with green plants, curtains, or furniture using a 77 GHz millimeter-wave radar. Meanwhile, the proposed method performs better than conventional methods. The detection probability is 81.250% when the human is behind a potted plant and is 90.286% when beside it. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
50. Optimization on the Polarization and Waveform of Radar for Better Target Detection Performance under Rainy Condition.
- Author
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Li, Xinda, Cheng, Xu, Ju, Xinjie, Peng, Yunli, Hu, Jinzhu, and Li, Jianbing
- Subjects
- *
RADAR targets , *RADAR , *ACCOUNTING methods , *AMBIGUITY - Abstract
Under rainy conditions, atmospheric settling particles have significant depolarization effects on radar waves and thus affect the target detection performance. This paper establishes an extended target detection model for fully polarized radar under complex rainy conditions and proposes two polarization and waveform optimization methods by taking into account the transmission effect of rainfall. If the rainfall information is known, the polarization and waveform for transmitting and receiving are optimized by maximizing the system Signal-to-Clutter/Noise Ratio (SCNR). While the rainfall information is absent, the optimal transmitting polarization and waveform are obtained by directly compensating the transmission effect from non-rainfall optimization results using fully polarized radar observation parameters. Using measured data from the T-72 tank, The experimental results verify the effectiveness and robustness of the two methods in real scenarios and 4 dB on the improvement of SCNR can be achieved. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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