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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]

Existing sea surface small target detection methods typically rely on intricate feature extraction techniques on transformed radar returns. However, these approaches suffer from issues of high computational complexity and low real-time performance. Temporal Convolutional Network (TCN) can enable direct processing of radar time-series echo data without the need for elaborate feature extraction, thus substantially improving computational efficiency. Building upon this, this paper presents a novel target detection algorithm based on Multi-layer Attention Temporal Convolutional Network (MA-TCN). The proposed algorithm processes the amplitude information in the original echo signals, and comprehensively extracts sequence feature information through the construction of stacked residual modules. Additionally, it integrates multi-layer attention mechanisms to adaptively adjust the output weights of each residual module, thereby further enhancing detection accuracy. Experimental results demonstrate that the proposed approach achieves significant improvements in both detection performance and efficiency compared to existing methods. [ABSTRACT FROM AUTHOR]

Regarding the suppression of radar mainlobe jamming, a blind extraction method for suppressing mainlobe jamming based on maximizing the signal to interference plus noise ratio (SINR) is proposed. Unlike blind separation, blind extraction can extract interested components from multiple mixed signals, making it more suitable for jamming suppression in complex electromagnetic environments with multiple sources and channels. This method establishes an optimization model for maximizing SINR in the distance domain. The target echo signal is solved and extracted using the particle swarm optimization (PSO) algorithm to achieve mainlobe jamming suppression. Simulation testing shows that the proposed method improves the jamming suppression effect compared to traditional blind separation jamming suppression methods. It does not require estimation of the number of sources and has lower requirements for the number of channels, making it applicable in underdetermined scenarios. Additionally, it reduces computational complexity, making it more suitable for complex electromagnetic environments. [ABSTRACT FROM AUTHOR]

To address the technical problems of target detection and identification of missile-borne radar in the background of strong ground clutter, we propose to combine the high resolution range profile (HRRP) technology and Doppler beam sharpening (DBS) technology to carry out two-dimensional high-resolution imaging of the ground to improve the performance of radar target detection and identification in the presence of clutter. The method uses the linear frequency modulation stepped frequency (LFM-SF) signal as the basic waveform. Firstly, the Doppler effect caused by the platform speed is discussed in detail and corrected. Then, HRRP sequences corresponding to each frame are obtained through distance profile extraction, and aiimuth high resolution is achieved using fast Fourier transform (FFT). Finally, motion compensation is applied to the errors caused by the platform in actual light conditions, completing the two-dimensional resolution of the area within the beam. The processing of measured data has verified the effectiveness and practicality of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Focusing on the issue of error detection in terminal guidance radar under sea clutter background, research on sea clutter suppression and discrimination is conducted. The target identification and classification algorithm based on Tri-feature training is adopted, and the target amplitude, peak duration range and fluctuation rate are used as feature quantities. The radial basis function (RBF) is selected as the kernel function, which has strong nonlinear mapping ability and can better represent the relationship between data in high-dimensional space. Then, the support vector machine ( SVM) target identification and classification classifier design and experimental data verification are carried out. Verified by publicly available measured data, the accuracy rate of the proposed algorithm has reached over 97%. Compared with the traditional template matching recognition method, the target identification and classification algorithm based on Tri-feature training has higher identification accuracy, which proves the effectiveness and progressiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In recent years, the application of compact and cost-effective deployable X-band polarimetric radars has gained in popularity, particularly in regions with complex terrain. The deployable radars generally use a radome constructed by joining multiple panels using metallic threads to facilitate easy transportation. As a part of the ESPOIRS project, Laboratoire de l'Atmosphεave;re et des Cyclones has acquired an X-band meteorological radar with four panel radome configuration. In this study, we investigated the effect of the radome on the measured polarimetric variables, particularly differential reflectivity and differential phase. Our observations reveal that the metallic threads connecting the radome panels introduce power loss at vertical polarization, leading to a positive bias in the differential reflectivity values. To address the spatial variability bias observed in differential reflectivity and differential phase, we have developed a novel algorithm based on the Discrete Fourier Transform. The algorithm's performance was tested during an intense heavy rainfall event caused by the Batsirai cyclone on Reunion Island. The comparative and joint histogram analysis demonstrates the algorithm's effectiveness in correcting the spatial bias in the polarimetric variables. [ABSTRACT FROM AUTHOR]

Precise radar pulse detection is the premise of electronic support measures. A constant false alarm rate (CFAR) detection algorithm based on difference of box (DOB) filter is proposed to realise low‐complexity and environment‐adaptive radar pulse detection, and the principle behind the proposed algorithm is revealed. Specifically, the DOB filter is adopted to extract the rising edges and falling edges of radar pulses, and the signal variation law of probability density functions (PDFs) during the process of DOB filter are first theoretically analysed. Based on the derived PDFs, dynamic detection threshold and fixed threshold factor for the proposed CFAR algorithm based on DOB filter are deduced to detect the presence of radar pulses. Furthermore, the closed‐form expression of the detection probability is derived. Simulations and on‐board field programmable gate arrays implementation verify the superiority of the proposed CFAR algorithm based on DOB filter. Simulation results show that the proposed algorithm performs well in detecting various types of pulse signals, the detection probability ≥99% on condition that the signal‐to‐noise ratio ≥3 dB and false alarm probability = 10−8. Moreover, the proposed CFAR algorithm based on DOB filter can deal with both singular pulses and pulse on pulse signals. [ABSTRACT FROM AUTHOR]

Recent advances in radar seeker technologies have considerably improved missile precision and efficacy during target interception. This is especially concerning in the arenas of protection and safety, where appropriate countermeasures against enemy missiles are required to ensure the protection of naval facilities. In this study, we present a reinforcement-learning-based strategy for deploying decoys to enhance the survival probability of a target ship against a missile threat. Our approach involves the coordinated operation of three decoys, trained using the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) algorithms. The decoys operate in a leader–follower dynamic with a circular formation to ensure effective coordination. We evaluate the strategy across various parameters, including decoy deployment regions, missile launch directions, maximum decoy speeds, and missile speeds. The results indicate that, decoys trained with the MATD3 algorithm demonstrate superior performance compared to those trained with the MADDPG algorithm. Insights suggest that our decoy deployment strategy, particularly when utilizing MATD3-trained decoys, significantly enhances defensive measures against missile threats. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An improved SAF-FCOS radar-vision fusion target detection network is proposed to address the difficulty in effectively utilizing radar point cloud information and image features, as well as the issue of false or missed detections in harsh weather environments. The backbone network structure of SAF-FCOS is improved, and multi-scale fusion of radar feature information is carried out at C3 and C4 feature layers, so that the network model can make full use of radar information. Using improved LNblock module--LNblcok_GAM before detection layers can extract image features at a lower computational cost while improving the detection performance of the network. In terms of regression loss, the improved CEIOU based on EIOU and GIOU is used to replace the GIOU in the original network, improving the detection accuracy of the network and enhancing the robustness of the model. On the NuScenes dataset, the improved network achieves 70.7% mAP0.5:0.95 and 90.5% AP50, respectively, which are 1.7 percentage points and 0.9 percentage points higher than the original network SAF-FCOS. The missed and false detections are effectively reduced, and the overall detection performance of the improved network is better than other classic visual target detection algorithms. [ABSTRACT FROM AUTHOR]

In the increasingly complex electromagnetic environment, main lobe jamming significantly degrades the performance of a wideband radar system. To mitigate this issue, this paper developed a wideband main lobe jamming suppression algorithm based on a distributed array radar. Firstly, this algorithm utilizes eigen-projection matrix processing in the main array to cancel out the main lobe jamming for main lobe maintenance, and then suppresses side lobe jamming through null constraint beamforming. Subsequently, the large aperture of the full array is leveraged to form a narrow beam directed toward the main lobe interference. Finally, joint beamforming using the minimum mean square error criterion is employed. In scenarios where both main lobe jamming and side lobe jamming exist, this algorithm can adaptively cancel main lobe wideband jamming, suppress side lobe wideband jamming, and effectively control the significant loss of the desired wideband signals caused by main lobe jamming within a smaller angular range. Simulation results validate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Acta Scientiarum Naturalium Universitatis Sunyatseni / Zhongshan Daxue Xuebao is the property of Sun-Yat-Sen University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a polar-format algorithm (PFA)-based synthetic-aperture radar (SAR) processor that can be mounted on a small drone to support video SAR (ViSAR) imaging. For drone mounting, it requires miniaturization, low power consumption, and high-speed performance. Therefore, to meet these requirements, the processor design was based on a field-programmable gate array (FPGA), and the implementation results are presented. The proposed PFA-based SAR processor consists of both an interpolation unit and a fast Fourier transform (FFT) unit. The interpolation unit uses linear interpolation for high speed while occupying a small space. In addition, the memory transfer is minimized through optimized operations using SAR system parameters. The FFT unit uses a base-4 systolic array architecture, chosen from among various fast parallel structures, to maximize the processing speed. Each unit is designed as a reusable block (IP core) to support reconfigurability and is interconnected using the advanced extensible interface (AXI) bus. The proposed PFA-based SAR processor was designed using Verilog-HDL and implemented on a Xilinx UltraScale+ MPSoC FPGA platform. It generates an image 2048 × 2048 pixels in size within 0.766 s, which is 44.862 times faster than that achieved by the ARM Cortex-A53 microprocessor. The speed-to-area ratio normalized by the number of resources shows that it achieves a higher speed at lower power consumption than previous studies. [ABSTRACT FROM AUTHOR]

Noise radar waveform, as an important waveform in the field of low-probability-of-intercept radars, has been widely studied. However, the general noise waveform has a high peak average power ratio (PAPR), which leads to a reduction of signal-to-noise ratio. Thus, it is necessary to control the noise waveform PAPR to a suitable range. In this paper, the PAPR value reduction problem of low sidelobe noise waveform is studied. First, the proposed new algorithm is given a general overview. Then, the Combined Spectral Shaping and Peak-to-Average Power Ratio Reduction (COSPAR) algorithm is adjusted according to the alternate projection method and phase retrieval algorithm, and an improved COSPAR (ICOSPAR) algorithm with a faster execution speed and higher waveform generation efficiency is derived. The specific method performs waveform projection between the spectral constraint domain and the PAPR constraint domain and then evaluates the computational load of the ICOSPAR algorithm. Simulation results show that the ICOSPAR algorithm is reliable and efficient in generating low PAPRs and low sidelobe noise waveforms. [ABSTRACT FROM AUTHOR]

With the advent of Mobile Edge Computing (MEC), shifting data processing from cloud centers to the network edge presents an advanced computational paradigm for addressing latency-sensitive applications. Specifically, in radar systems, the real-time processing and prediction of radar echo data pose significant challenges in dynamic and resource-constrained environments. MEC, by processing data near its source, not only significantly reduces communication latency and enhances bandwidth utilization but also diminishes the necessity of transmitting large volumes of data to the cloud, which is crucial for improving the timeliness and efficiency of radar data processing. To meet this demand, this paper proposes a model that integrates a spatiotemporal Attention Module (STAM) with a Long Short-Term Memory Gated Recurrent Unit (ST-ConvLSGRU) to enhance the accuracy of radar echo prediction while leveraging the advantages of MEC. STAM, by extending the spatiotemporal receptive field of the prediction units, effectively captures key inter-frame motion information, while optimizations to the convolutional structure and loss function further boost the model's predictive performance. Experimental results demonstrate that our approach significantly improves the accuracy of short-term weather forecasting in a mobile edge computing environment, showcasing an efficient and practical solution for processing radar echo data under dynamic, resource-limited conditions. [ABSTRACT FROM AUTHOR]

Inverse synthetic-aperture radar (ISAR) can achieve precise imaging of targets, which enables precise perception of battlefield information, and it has become one of the most important tasks for radar systems. In multi-target scenarios, a resource scheduling method is required to improve the sensing ability and the overall efficiency of a radar system due to the limited resources. Considering the motion state of the target will change as the observation distance increases and image defocusing can occur due to the prolonged coherence accumulation time and significant changes in the target's motion state, the optimal observation period should be an important consideration factor in the resource scheduling method to further improve the imaging efficiency of radar system, which has not yet been involved in existing research. In this paper, we first derive the expressions of the target's effective rotation angle and the equivalent rotation angular velocity and then define the target's optimal observation period. Then, for multi-target imaging scenarios, we allocate pulse resources within a given time period based on sparse-aperture ISAR imaging technology. An adaptive radar resource scheduling algorithm for multi-target ISAR imaging is proposed, which prioritizes allocating resources based on the optimal observation periods for the targets. In the algorithm, a radar resource scheduling model for multi-target ISAR imaging is established, and a feedback-based closed-loop search optimization method is proposed to solve the model. Finally, the best scheduling strategy can be obtained, which includes imaging task duration and the pulse allocation sequence for each target. Simulation results validate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

We propose a robust super-resolution algorithm for vital sign radar in a low signal-to-noise ratio (SNR) environment. Conventional approaches, such as fast Fourier transform and super-resolution based algorithms, suffered to provide reliable results due to the limited data length and high noise level. To overcome these limitations, our proposed algorithm utilizes a low-complexity least mean square (LMS) filter and relaxation (RELAX) techniques to achieve robust performance in low SNR environments. To evaluate the effectiveness of our algorithm, we conducted both simulation and experimental studies. Our results show that the proposed method significantly outperforms conventional methods, with Monte-Carlo simulations of respiration and heartbeat achieving an RMSE approximately 7 and 120 times lower than that of the conventional method, respectively. Overall, our algorithm provides a promising solution for robust vital sign detection in challenging low SNR environments [ABSTRACT FROM AUTHOR]

This paper focuses on the intelligent recognition problem of radar detection targets. Aiming at the low accuracy and slow speed of millimeter-wave radar clustering point cloud information, a feature algorithm of millimeter-wave radar suitable for detecting targets is proposed. In the detection of targets by millimeter-wave radar, distance is the biggest factor affecting the number and degree of sparsity. A method that combines the feature information of the point cloud with the KD tree proximity search algorithm and the DBSCAN clustering algorithm is proposed, which can adapt to the problems of uneven target point cloud, small amount of data and slow clustering speed. The improved algorithm can use the KD tree to quickly find adjacent points and calculate the distance between adjacent points. The corresponding number of thresholds is set according to the distance where the target is located, and the radius of the target area reflected by the millimeter-wave radar plus the distance of the last threshold point is used as the neighborhood radius of the improved algorithm. Therefore, fast and adaptive parameter adjustment of the millimeter-wave radar can be realized. Simulation tests show that the improved clustering algorithm has better parameters. The accuracy of the improved algorithm is increased by 4.2%, and it also greatly improves the clustering speed. [ABSTRACT FROM AUTHOR]

In this paper, a power allocation algorithm of dual-function radar–communication system with limited power is proposed to obtain better overall system performance measured by the weighted summation of radar signal to interference plus noise ratio (SINR) and communication channel capacity. First, a power allocation model is established to maximize the radar SINR and communication channel capacity with limited transmitted power. Then, the Karush–Kuhn–Tucker (KKT) conditions are used to solve the optimal objective function under the condition that only radar SINR or communication channel capacity is considered, respectively. Finally, the optimal value is combined with the original model and transformed into a single objective optimization model, and the optimal power is obtained by solving the model through the iterative optimization algorithm. Simulation results show that, compared with other power allocation algorithms, the proposed algorithm can achieve better radar-communication integration performance under the same transmit power. [ABSTRACT FROM AUTHOR]

In the application of Compressive Sensing (CS) theory for sidelobe suppression in Random Frequency and Pulse Repetition Interval Agile (RFPA) radar, the off−grid issues affect the performance of target parameter estimation in RFPA radar. Therefore, to address this issue, this paper presents an off−grid CS algorithm named Refinement and Generalized Double Pareto (GDP) distribution based on Sparse Bayesian Learning (RGDP−SBL) for RFPA radar that utilizes a coarse−to−fine grid refinement approach, allowing precise and cost−effective signal recovery while mitigating the impact of off−grid issues on target parameter estimation. To obtain a high-precision signal recovery, especially in scenarios involving closely spaced targets, the RGDP−SBL algorithm makes use of a three−level hierarchical prior model. Furthermore, the RGDP−SBL algorithm efficiently utilizes diagonal elements during the coarse search and exploits the convexity of the grid energy curve during the fine search, therefore significantly reducing computational complexity. Simulation results demonstrate that the RGDP−SBL algorithm significantly improves signal recovery performance while maintaining low computational complexity in multiple scenarios for RFPA radar. [ABSTRACT FROM AUTHOR]

This paper proposes a fast phase-only beamforming algorithm for frequency diverse array multiple-input multiple-output radar systems. Specifically, we use the Kronecker decomposition to decompose the desired phase-only weight vector into phase-only transmit and receive weight vectors and to decompose the target steering vector into transmit and receive steering vectors. By using the properties of the Kronecker product, the transmit and receive steering vectors and the transmit and receive weight vectors with the Vandermonde structure are decomposed into Kronecker factors with uni-modulus vectors, respectively. On this basis, in order to maintain the mainlobe gain and form a deep null at the desired position, the Kronecker factors are divided into two parts.The first component, referred to as the interference suppression factors, is responsible for creating deep nulls. The second component, known as the signal enhancement factor, maintains the mainlobe gain. We provide an analytical solution with low complexity for the Kronecker factors. This strategy can obtain the phase-only weights while effectively forming a deep null at the desired position. Numerical experiments are conducted to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this study, we investigate the synergy of elastic backscatter lidar, Ku-band radar, and submillimeter-wave radiometer measurements in the retrieval of ice from satellite observations. The synergy is analyzed through the generation of a large dataset of ice water content (IWC) profiles and simulated lidar, radar and radiometer observations. The characteristics of the instruments (frequencies, sensitivities, etc.) are set based on the expected characteristics of instruments of the Atmosphere Observing System (AOS) mission. A hold-out validation methodology is used to assess the accuracy of the IWC profiles retrieved from various combinations of observations from the three instruments. Specifically, the IWC and associated observations are randomly divided into two datasets, one for training and the other for evaluation. The training dataset is used to train the retrieval algorithm, while the evaluation dataset is used to assess the retrieval performance. The dataset of IWC profiles is derived from CloudSat reflectivity and CALIOP lidar observations. The retrieval of the ice water content IWC profiles from the computed observations is achieved in two steps. In the first step, a class, of 18 potential classes characterized by different vertical distribution of IWC, is estimated from the observations. The 18 classes are predetermined based on the k-means clustering algorithm. In the second step, the IWC profile is estimated using an ensemble Kalman smoother algorithm that uses the estimated class as a priori information. The results of the study show that the synergy of lidar, radar, and radiometer observations is significant in the retrieval of the IWC profiles. Nevertheless, it should be mentioned that this synergy was found under idealized conditions, and additional work might be required to materialize it in practice. The inclusion of the lidar backscatter observations in the retrieval process has a larger impact on the retrieval performance than the inclusion of the radar observations. As ice clouds have a significant impact on atmospheric radiative processes, this work is relevant to ongoing efforts to reduce uncertainties in climate analyses and projections. [ABSTRACT FROM AUTHOR]

Radar profiling algorithms are widely used for precipitation‐induced path‐attenuation correction. In this study, we investigate the impact of the four well‐known profiling algorithms on the efficacy of quantitative precipitation estimation retrievals. The first method, named the "simple PhiDP‐based method", distributes precipitation‐induced path attenuation, or PhiDP (differential phase shift), of a rain segment to each radar gate according to radar reflectivity (Zh). The second one is a linear version of the ZPHI method derived from an approximate linear relationship between specific attenuation (Ah) and specific differential phase (KDP). The third one is a nonlinear version of the ZPHI that respects the nonlinearity of the Ah–KDP relationship. The fourth method is inspired from the self‐consistent method of Bringi. The two‐moment normalized raindrop size distribution (DSD) with two variables, scaled drop concentration Nw$$ {N}_{\mathrm{w}} $$ and mean drop diameter Dm$$ {D}_{\mathrm{m}} $$, is used to represent natural variability of DSD. Two configurations are considered: a constant Nw$$ {N}_{\mathrm{w}} $$ over a segment and a variable Nw$$ {N}_{\mathrm{w}} $$ over a segment. The Ah, KDP, and rain intensity R$$ R $$ are estimated by the four profiling algorithms. The sensitivity of each algorithm to radar measurements uncertainty is also investigated. The evaluation of the estimates shows the following. First, for the range‐constant Nw$$ {N}_{\mathrm{w}} $$ situation, the nonlinear ZPHI and the self‐consistent method yield the best estimates if the measurements errors are not considered. However, the nonlinear ZPHI and the self‐consistent method are significantly impacted by the Zh bias and the measurements noises respectively. Second, for the range‐variable Nw$$ {N}_{\mathrm{w}} $$ situation, the self‐consistent method does not have a good performance even when radar measurements are accurate. The performance of the nonlinear ZPHI method is similar to the simple PhiDP‐based method, whereas the latter is immune to the Zh calibration error. These results suggest that the benefit of introducing Nw$$ {N}_{\mathrm{w}} $$ in quantitative precipitation estimation algorithms can be significantly reduced if Nw$$ {N}_{\mathrm{w}} $$ is not constant in reality and/or if the radar measurements are biased and noisy. [ABSTRACT FROM AUTHOR]

NASA's multisatellite precipitation product from the Global Precipitation Measurement (GPM) mission, the Integrated Multi-satellitE Retrievals for GPM (IMERG) product, is validated over tropical and high-latitude oceans from June 2014 to August 2021. This oceanic study uses the GPM Validation Network's island-based radars to assess IMERG when the GPM Core Observatory's Microwave Imager (GMI) observes precipitation at these sites (i.e., IMERG-GMI). Error tracing from the Level 3 (gridded) IMERG V06B product back through to the input Level 2 (satellite footprint) Goddard Profiling Algorithm GMI V05 climate (GPROF-CLIM) product quantifies the errors separately associated with each step in the gridding and calibration of the estimates from GPROF-CLIM to IMERG-GMI. Mean relative bias results indicate that IMERG-GMI V06B overestimates Alaskan high-latitude oceanic precipitation by +147% and tropical oceanic precipitation by +12% with respect to surface radars. GPROF-CLIM V05 overestimates Alaskan oceanic precipitation by +15%, showing that the IMERG algorithm's calibration adjustments to the input GPROF-CLIM precipitation estimates increase the mean relative bias in this region. In contrast, IMERG adjustments are minimal over tropical waters with GPROF-CLIM overestimating oceanic precipitation by +14%. This study discovered that the IMERG V06B gridding process incorrectly geolocated GPROF-CLIM V05 precipitation estimates by 0.1° eastward in the latitude band 75°N–75°S, which has been rectified in the IMERG V07 algorithm. Correcting for the geolocation error in IMERG-GMI V06B improved oceanic statistics, with improvements greater in tropical waters than Alaskan waters. This error tracing approach enables a high-precision diagnosis of how different IMERG algorithm steps contribute to and mitigate errors, demonstrating the importance of collaboration between evaluation studies and algorithm developers. Significance Statement: Evaluation of IMERG's oceanic performance is very limited to date. This study uses the GPM Validation Network to conduct the first extensive assessment of IMERG V06B at its native resolution over both high-latitude and tropical oceans, and traces errors in IMERG-GMI back through to the input GPROF-CLIM GMI product. IMERG-GMI overestimates tropical oceanic precipitation (+12%) and strongly overestimates Alaskan oceanic precipitation (+147%) with respect to the island-based radars studied. IMERG's GMI estimates are assessed as these should be the optimal estimates within the multisatellite product due to the GMI's status as calibrator of the GPM passive microwave constellation. [ABSTRACT FROM AUTHOR]

To find a trajectory with low radar detection probability for stealth aircraft under the assumption of 2D space, performing a rapid turning maneuver is a useful way to reduce the radar detection probability of an aircraft by changing the azimuth angle rapidly to reduce the time of high radar cross-section (RCS) exposure to radar. However, in real flight, not only does the azimuth angle to the radar change rapidly but the elevation angle also changes rapidly, and the change in the radar cross-section is also significant in this process. Based on this premise, this paper established a trajectory planning method based on the sparse A* algorithm in a 3D complex, dynamic environment, called the 3D sparse A* method, based on a log-normal radar model (the 3D-SASLRM method), which considers the RCS statistical uncertainty and the statistical characteristics of the radar signals. Simulations were performed in both simple and complex scenarios. It was concluded that the established 3D-SASLRM method can significantly reduce the radar detection probability. And the essence of reducing under the assumption of 3D space is also to reduce the time of high radar cross-section exposure to radar. [ABSTRACT FROM AUTHOR]

In high-traffic harbor waters, marine radar frequently encounters signal interference stemming from various obstructive elements, thereby presenting formidable obstacles in the precise identification of ships. To achieve precise pixel-level ship identification in the complex environments, a customized neural network-based ship segmentation algorithm named MrisNet is proposed. MrisNet employs a lightweight and efficient FasterYOLO network to extract features from radar images at different levels, capturing fine-grained edge information and deep semantic features of ship pixels. To address the limitation of deep features in the backbone network lacking detailed shape and structured information, an adaptive attention mechanism is introduced after the FasterYOLO network to enhance crucial ship features. To fully utilize the multi-dimensional feature outputs, MrisNet incorporates a Transformer structure to reconstruct the PANet feature fusion network, allowing for the fusion of contextual information and capturing more essential ship information and semantic correlations. In the prediction stage, MrisNet optimizes the target position loss using the EIoU function, enabling the algorithm to adapt to ship position deviations and size variations, thereby improving segmentation accuracy and convergence speed. Experimental results demonstrate MrisNet achieves high recall and precision rates of 94.8% and 95.2%, respectively, in ship instance segmentation, outperforming various YOLO and other single-stage algorithms. Moreover, MrisNet has a model parameter size of 13.8M and real-time computational cost of 23.5G, demonstrating notable advantages in terms of convolutional efficiency. In conclusion, MrisNet accurately segments ships with different spot features and under diverse environmental conditions in marine radar images. It exhibits outstanding performance, particularly in extreme scenarios and challenging interference conditions, showcasing robustness and applicability. [ABSTRACT FROM AUTHOR]

In order to achieve miniaturization, short-range radar (SRR) generally adopts millimeter-wave (MMW) radar with a frequency-modulated continuous-wave (FMCW) system, which may make the stop–go–stop assumption in traditional synthetic aperture radar (SAR) imaging algorithms invalid. In addition, in order to observe a large enough area, SRR often needs a wide radar beam, which may cause serious range–azimuth coupling when using SRR for SAR imaging. The above two problems may make the traditional SAR imaging algorithm invalid in SRR SAR imaging. Taking the SRR SAR imaging application into account, traditional frequency domain SAR imaging algorithms are analyzed and improved in this paper. Firstly, the intra-pulse motion (IPM) caused by the FMCW system and the two-dimensional coupling (TDC) in the case of a wide beam are analyzed. Subsequently, the applicability of the range Doppler algorithm (RDA), the frequency scaling algorithm (FSA) and the range migration algorithm (RMA) for SRR SAR is analyzed. Then, improvement measures are put forward to address the aliasing and folding phenomena caused by the wide-beam problem in the FSA and RMA, respectively. Finally, the effectiveness of the proposed algorithm is verified using simulation data and real measured data collected using an MMW radar fixed on a slide rail. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When radar receives target echoes to form plots, it is inevitably affected by clutter, which brings a lot of imprecise and uncertain information to target recognition. Traditional radar plot recognition algorithms often have poor performance in dealing with imprecise and uncertain information. To solve this problem, a radar plot recognition algorithm based on adaptive evidence classification (RPREC) is proposed in this paper. The RPREC can be considered as the evidence classification version under the belief functions. First, the recognition framework based on the belief functions for target, clutter, and uncertainty is created, and a deep neural network model classifier that can give the class of radar plots is also designed. Secondly, according to the classification results of each iteration round, the decision pieces of evidence are constructed and fused. Before being fused, evidence will be corrected based on the distribution of radar plots. Finally, based on the global fusion results, the class labels of all radar plots are updated, and the classifier is retrained and updated so as to iterate until all the class labels of radar plots are no longer changed. The performance of the RPREC is verified and analyzed based on the real radar plot datasets by comparison with other related methods. [ABSTRACT FROM AUTHOR]

Realization and enhancement of detection techniques for multiple-input–multiple-output (MIMO) radar systems require polyphase code sequences with excellent orthogonality characteristics. Therefore, orthogonal waveform design is the key to realizing MIMO radar. Conventional orthogonal waveform design methods fail to ensure acceptable orthogonal characteristics by individually optimizing the autocorrelation sidelobe peak level and the cross-correlation sidelobe peak level. In this basis, the multi-objective Archimedes optimization algorithm (MOIAOA) is proposed for orthogonal waveform optimization while simultaneously minimizing the total autocorrelation sidelobe peak energy and total cross-correlation peak energy. A novel optimal individual selection method is proposed to select those individuals that best match the weight vectors and lead the evolution of these individuals to their respective neighborhoods. Then, new exploration and development phases are introduced to improve the algorithm's ability to increase its convergence speed and accuracy. Subsequently, novel incentive functions are formulated based on distinct evolutionary phases, followed by the introduction of a novel environmental selection method aimed at comprehensively enhancing the algorithm's convergence and distribution. Finally, a weight updating method based on the shape of the frontier surface is proposed to dynamically correct the shape of the overall frontier, further enhancing the overall distribution. The results of experiments on the orthogonal waveform design show that the multi-objective improved Archimedes optimization algorithm (MOIAOA) achieves superior orthogonality, yielding lower total autocorrelation sidelobe peak energy and total cross-correlation peak energy than three established methods. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of precise classification of vegetation points and ground points using LiDAR point cloud data in dense forest terrain, five traditional filtering methods were used to evaluate the filtering accuracy of forest area point cloud, and the corresponding initial filtering was carried out with the help of cloth simulation filtering algorithm. After accurate processing, the initial ground points with relatively complete terrain features were obtained. Then, the iterative opening calculation was carried out on the initial ground points with prominent vegetation points, and the new ground points were obtained. And with the aid of a series of indicators such as Class Ⅰ error and Class Ⅱ error, the accuracy of scientific evaluation was determined. Compared with the traditional filtering algorithm of Class Ⅱ, the total error of the cloth simulation algorithm and simple morphology algorithm decreased significantly, and the Kappa coefficient showed an increasing trend. The results showed that this method was suitable for point cloud classification in forest area and can obtain better filtering effect. [ABSTRACT FROM AUTHOR]

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The Dual-Frequency Precipitation Radar (DPR), which consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR) on board the GPM Core Observatory, cannot observe precipitation at low altitudes near the ground contaminated by surface clutter. This near-surface region is called the blind zone. DPR estimates the clutter-free bottom (CFB), which is the lowest altitude not included in the blind zone, and estimates precipitation at altitudes higher than the CFB. High CFBs, which are common over mountainous areas, represent obstacles to detection of shallow precipitation and estimation of low-level enhanced precipitation. We compared KuPR data with rain gauge data from Da-Tun Mountain of northern Taiwan acquired from March 2014 to February 2020. A total of 12 cases were identified in which the KuPR missed some rainfall with intensity of .10 mm h21 that was observed by rain gauges. Comparison of KuPR profile and ground-based radar profile revealed that shallow precipitation in the KuPR blind zone was missed because the CFB was estimated to be higher than the lower bound of the range free from surface echoes. In the original operational algorithm, CFB was estimated using only the received power data of the KuPR. In this study, the CFB was identified by the sharp increase in the difference between the received powers of the KuPR and the KaPR at altitude affected by surface clutter. By lowering the CFB, the KuPR succeeded in detection and estimation of shallow precipitation. SIGNIFICANCE STATEMENT: The Dual-Frequency Precipitation Radar (DPR) on board the GPMCore Observatory cannot capture precipitation in the low-altitude region near the ground contaminated by surface clutter. This region is called the blind zone. The DPR estimates the clutter-free bottom (CFB), which is the lower bound of the range free from surface echoes, and uses data higher than CFB. DPR consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR). KuPR missed some shallow precipitation more than 10 mm h21 in the blind zone over Da-Tun Mountain of northern Taiwan because of misjudged CFB estimation. Using both the KuPR and the KaPR, we improved the CFB estimation algorithm, which lowered the CFB, narrowed the blind zone, and improved the capability to detect shallow precipitation. [ABSTRACT FROM AUTHOR]

In radar detection of multiple targets, the large difference in echo energy of coexisting targets leads to the phenomenon that strong targets cover weak targets. To tackle this problem, a peak self-seeking track before detect (TBD) algorithm based on parallel line coordinate transformation is proposed. By comparing the sum of the maximum energy of measurement point in the parameter unit where each measurement point is located after accumulation, the unit with the maximum energy accumulation value is found, by which a new binary accumulation result is formed. Then peak value extraction, track back-tracking, and track correction are performed with the threshold. The simulation results show that the improved algorithm has a high global detection probability for multiple targets, and can initiate multiple targets with different echo energies. [ABSTRACT FROM AUTHOR]

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Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Noncontact heart rate monitoring techniques based on millimeter-wave radar have advantages in unique medical scenarios. However, the accuracy of the existing noncontact heart rate estimation methods is still limited by interference, such as DC offsets, respiratory harmonics, and environmental noise. Additionally, these methods still require longer observation times. Most deep learning methods related to heart rate estimation still need to collect more heart rate marker data for training. To address the above problems, this paper introduces a radar signal-based heart rate estimation network named the "masked phase autoencoders with a vision transformer network" (MVN). This network is grounded on masked autoencoders (MAEs) for self-supervised pretraining and a vision transformer (ViT) for transfer learning. During the phase preprocessing stage, phase differencing and interpolation smoothing are performed on the input phase signal. In the self-supervised pretraining step, masked self-supervised training is performed on the phase signal using the MAE network. In the transfer learning stage, the encoder segment of the MAE network is integrated with the ViT network to enable transfer learning using labeled heart rate data. The innovative MVN offers a dual advantage—it not only reduces the cost associated with heart rate data acquisition but also adeptly addresses the issue of respiratory harmonic interference, which is an improvement over conventional signal processing methods. The experimental results show that the process in this paper improves the accuracy of heart rate estimation while reducing the requisite observation time. [ABSTRACT FROM AUTHOR]

Millimeter-wave FMCW radar has been widely used in joint range-velocity estimation of multiple targets. However, most existing algorithms are unable to estimate the range-velocity information with high accuracy simultaneously and fail to discriminate the targets with either closely spaced ranges or closely spaced velocities in the 2D range-Doppler spectrum. In order to deal with these problems, this paper proposes a 2D-Unitary ESPRIT-based joint range and velocity estimation algorithm of multiple targets for FMCW radar. Firstly, The 1D-IF signal is constructed into a 2D virtual array signal, the virtual array signals are preprocessed by a 2D-spatial smoothing technique to generate a new matrix signal. Then, according to the 2D-Unitary ESPRIT algorithm, the 2D real-valued information of the target parameters is obtained from this matrix signal, and then a new complex-value matrix is constructed. Finally, the eigenvalue decomposition of this new complex-value matrix is performed, and the range-velocity estimates of multiple targets are, respectively, calculated from the real and imaginary parts of the eigenvalues, and paired automatically. The simulation results illustrate that the proposed algorithm not only provides highly accurate range-velocity estimates but also has high-resolution performance and achieves automatic pairing of the range-velocity estimates in multi-target scenarios, thus effectively improving the multi-target joint range and velocity estimation performance of FMCW radar. [ABSTRACT FROM AUTHOR]

An environment-aware millimeter wave radar (EMWR) is one of the most important sensors in an autonomous driving system. With the increasing penetration of an EMWR on an autonomous vehicle, the possibility of radar interference increases accordingly. Interference may seriously affect the detection performance of an EMWR. When the transmitted signals from other EMWRs are received by the EMWR on an autonomous vehicle, the accuracy of target detection will be affected, which may affect the environment-aware performance. Therefore, more and more attention is paid to how to restrain the interference between EMWR signals. Radar interference can be divided into two types: same-frequency interference (SFI) and different-frequency interference (DFI). In this paper, an anti-DFI algorithm of an EMWR is proposed. Firstly, the causes and signal characteristics of DFI are analyzed. Secondly, the signal amplitude is obtained via Hilbert transform to locate the interference according to the signal characteristics. Then, the Lagrange interpolation based on empirical-mode decomposition (EMD) is used to reconstruct the interference signal to reduce the influence of DFI. Finally, the feasibility of the proposed algorithm is verified by the simulation results. The simulation results validate that the interference signal after EMD filtering and Lagrange interpolation can repair the interference region and achieve the purpose of anti-DFI. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the face of smart and varied jamming, intelligent radar anti-jamming technologies are urgently needed. Due to the variety of radar electronic counter-countermeasures (ECCMs), it is necessary to efficiently optimize ECCMs in the high-dimensional knowledge base to ensure that the radar achieves the optimal anti-jamming effect. Therefore, an intelligent radar anti-jamming decision-making method based on the deep deterministic policy gradient (DDPG) and the multi-agent deep deterministic policy gradient (MADDPG) (DDPG-MADDPG) algorithm is proposed. Firstly, by establishing a typical working scenario of radar and jamming, we designed the intelligent radar anti-jamming decision-making model, and the anti-jamming decision-making process was formulated. Then, aiming at different jamming modes, we designed the anti-jamming improvement factor and the correlation matrix of jamming and ECCM. They were used to evaluate the jamming suppression performance of ECCMs and to provide feedback for the decision-making algorithm. The decision-making constraints and four different decision-making objectives were designed to verify the performance of the decision-making algorithm. Finally, we designed a DDPG-MADDPG algorithm to generate the anti-jamming strategy. The simulation results showed that the proposed method has excellent robustness and generalization performance. At the same time, it has a shorter convergence time and higher anti-jamming decision making accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes an improved multi-frame coherent integration algorithm to improve the detection performance of weak targets in heterogeneous radar. In the detection of weak targets, integration within a single frame may fail to provide sufficient signal-to-noise ratio (SNR) gain. In this case, multi-frame coherent integration is an effective solution. However, radar parameters may be different across frames (i.e., heterogeneous radar) in some practical situations, leading to a mismatch of Doppler frequencies and the fixed phases, which poses difficulties to multi-frame coherent integration. To calibrate the ranges and Doppler frequencies of heterogenous multi-frame echoes, this paper firstly employs an improved Keystone Transform (KT). Compared to conventional KT, the improved KT aligns inter-frame carrier frequencies by applying varying degrees of slow-time rescaling based on the carrier frequencies of each frame, and aligns inter-frame Pulse Repetition Frequencies (PRF) through a unified global slow-time resampling. Secondly, this paper derives the explicit expressions of the fixed-phase terms and adopts a method based on fractional range bins, thus achieving explicit compensation for mismatched phases. Finally, heterogenous multi-frame coherent integration is achieved through slow-time fast Fourier transform. The effectiveness of the proposed algorithm is validated by simulation analyses. Compared to existing entropy-based methods, the proposed algorithm demonstrates higher robustness and lower computational complexity, making it more effective in detecting weak targets under low SNR conditions. [ABSTRACT FROM AUTHOR]

In order to study the waveform selection of radar seeker in the terminal guidance and tracking phase, this paper proposes a waveform selection algorithm in the guidance and tracking process of cognitive radar seeker. Firstly, we establish the processing flow of radar seeker waveform selection by combining the seeker guidance and observation filtering models; Secondly, we use the interactive multiple model unscented Kalman filter (IMMUKF) algorithm to solve the nonlinear tracking problem. We minimize tracking error by searching for waveform parameters based on the maximum mutual information criterion. The simulation results show that the waveform selection algorithm can effectively improve the seeker tracking accuracy and provide stable guidance overload. [ABSTRACT FROM AUTHOR]

Unmanned aerial helicopters (UAHs) have been widely used recently for reconnaissance operations and other risky missions. Meanwhile, the threats to UAHs have been becoming more and more serious, mainly from radar and flights. It is essential for a UAH to select a safe flight path, as well as proper flying attitudes, to evade detection operations, and the stealth abilities of the UAH can be helpful for this. In this paper, a stealth–distance dynamic weight Deep Q-Network (SDDW-DQN) algorithm is proposed for path planning in a UAH. Additionally, the dynamic weight is applied in the reward function, which can reflect the priorities of target distance and stealth in different flight states. For the path-planning simulation, the dynamic model of UAHs and the guidance model of flight are put forward, and the stealth model of UAHs, including the radar cross-section (RCS) and the infrared radiation (IR) intensity of UAHs, is established. The simulation results show that the SDDW-DQN algorithm can be helpful in the evasion by UAHs of radar detection and flight operations, and the dynamic weight can contribute to better path-planning results. [ABSTRACT FROM AUTHOR]

The interleaved mode has the effect of operating multiple modes simultaneously by operating two or more single modes in a time division using the agile beam steering capability of the AESA(active electronically scanned array) radar. During the air-to-ground/sea mode operation, the air-to-air mode is simultaneously operated for quick recognition and response to air threat targets. In this study, the processing unit was set differently according to the mode for an efficient interleaved mode. It is the design of the load-based beam operating algorithm that can maintain proper performance by designing to maintain a constant load. The resource usage of the proposed and existing algorithms was compared, and the flight test results were analyzed to verify the algorithm. [ABSTRACT FROM AUTHOR]

This article presents a calibration transfer methodology that can be used between radars of the same or different frequency bands. This method enables the absolute calibration of a cloud radar by transferring it from another collocated instrument with known calibration, by simultaneously measuring vertical ice cloud reflectivity profiles. The advantage is that the added uncertainty in the newly calibrated instrument can converge to the magnitude of the reference instrument calibration. This is achieved by carefully selecting comparable data, including the identification of the reflectivity range that avoids the disparities introduced by differences in sensitivity or scattering regime. The result is a correction coefficient used to compensate measurement bias in the uncalibrated instrument. Calibration transfer uncertainty can be reduced by increasing the number of sampling periods. The methodology was applied between collocated W-band radars deployed during the ICE-GENESIS campaign (Switzerland 2020/21). A difference of 2.2 dB was found in their reflectivity measurements, with an uncertainty of 0.7 dB. The calibration transfer was also applied to radars of different frequency, an X-band radar with unknown calibration and a W-band radar with manufacturer calibration; the difference found was −16.7 dB with an uncertainty of 1.2 dB. The method was validated through closure, by transferring calibration between three different radars in two different case studies. For the first case, involving three W-band radars, the bias found was of 0.2 dB. In the second case, involving two W-band and one X-band radar, the bias found was of 0.3 dB. These results imply that the biases introduced by performing the calibration transfer with this method are negligible. [ABSTRACT FROM AUTHOR]

Developed as part of a larger effort by the National Weather Service (NWS) Radar Operations Center to modernize their suite of single-radar severe weather algorithms for the WSR-88D network, the Tornado Probability Algorithm (TORP) and the New Mesocyclone Detection Algorithm (NMDA) were evaluated by operational forecasters during the 2021 National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) Experimental Warning Program Radar Convective Applications experiment. Both TORP and NMDA leverage new products and advances in radar technology to create rotation-based objects that interrogate single-radar data, providing important summary and trend information that aids forecasters in issuing time-critical and potentially life-saving weather products. Utilizing virtual resources like Google Workspace and cloud instances on Amazon Web Services, 18 forecasters from the NOAA/NWS and the U.S. Air Force participated remotely over three weeks during the spring of 2021, providing valuable feedback on the efficacy of the algorithms and their display in an operational warning environment, serving as a critical step in the research-to-operations process for the development of TORP and NMDA. This article will discuss the details of the virtual HWT experiment and the results of each algorithm's evaluation during the testbed. Significance Statement: Before transitioning newly developed radar-based severe weather applications to forecasting operations, an experiment simulating the use of these tools by end users issuing severe weather warnings is helpful to identify both how they are best utilized and address any needed improvements to increase their operational readiness. Conducted in 2021, this study describes the forecaster evaluation of the single-radar Tornado Probability Algorithm (TORP) and the New Mesocyclone Detection Algorithm (NMDA) in one of the first completely virtual Hazardous Weather Testbed (HWT) experiments. Participants stated both TORP and NMDA offered marked improvement over the currently available algorithms by helping the operational forecaster build their confidence when issuing severe weather warnings and increasing their overall situational awareness of storms within their domain. [ABSTRACT FROM AUTHOR]