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An absorptive coding metasurface (ACM) is proposed to achieve radar cross section (RCS) reduction in this paper. In the design progress of the ACM, two different lossy coding elements are proposed at first, which can both be regarded as a composite composed of a two-layer resistive frequency selective surface (RFSS) and a polarization conversion metasurface (PCM). The two-layer RFSS has a certain wave-absorbing property due to ohmic loss. In addition, the PCM can achieve ultra-wideband linear polarization conversion, and the polarization-converted reflected waves in the two coding elements under the same incidence will differ by nearly 180° in phase because the sub-unit-cell structures in them are perpendicular to each other. Thus, based on the two lossy coding elements, the ACM is proposed, which can achieve ultra-wideband RCS reduction due to absorption and phase cancelation. Numerical simulations demonstrate that the RCS of the ACM under normal incidence can be reduced more than 10 dB in the ultra-wide frequency band from 6.9 to 41.2 GHz with a relative bandwidth of 142.6%. Moreover, it has the advantages of polarization-insensitivity and wide incident angle. Finally, one effective experimental verification is carried out, and a reasonable agreement is observed between the simulation and experimental results. [ABSTRACT FROM AUTHOR]

Contrasting the X-band phased array radar (XPAR) with the conventional S-Band dual-polarization mechanical scanning radar (SMSR), the XPAR offers superior temporal and spatial resolution, enabling a more refined depiction of the internal dynamics within convective systems. While both SMSR and XPAR data are extensively used in monitoring and alerting for severe convective weather, their comparative application in numerical weather prediction through data assimilation remains a relatively unexplored area. This study harnesses the Weather Research and Forecasting Model (WRF) and its data assimilation system (WRFDA) to integrate radial velocity and reflectivity from the Guangzhou SMSR and nine XPARs across Guangdong Province. Utilizing a three-dimensional variational approach at a 1 km convective-scale grid, the assimilated data are applied to forecast a rainstorm event in the Pearl River Delta (PRD) on 6 June 2022. Through a comparative analysis of the results from assimilating SMSR and XPAR data, it was observed that the assimilation of SMSR data led to more extensive adjustments in the lower- and middle-level wind fields compared to XPAR data assimilation. This resulted in an enlarged convergence area at lower levels, prompting an overdevelopment of convective systems and an excessive concentration of internal hydrometeor particles, which in turn led to spurious precipitation forecasts. However, the sequential assimilation of both SMSR and XPAR data effectively reduced the excessive adjustments in the wind fields that were evident when only SMSR data were used. This approach diminished the generation of false echoes and enhanced the precision of quantitative precipitation forecasts. Additionally, the lower spectral width of XPAR data indicates its superior detection accuracy. Assimilating XPAR data alone yields more reasonable adjustments to the low- to middle-level wind fields, leading to the formation of small-to-medium-scale horizontal convergence lines in the lower levels of the analysis field. This enhancement significantly improves the model's forecasts of composite reflectivity and radar echoes, aligning them more closely with actual observations. Consequently, the Threat Score (TS) and Equitable Threat Score (ETS) for heavy-rain forecasts (>10 mm/h) over the next 5 h are markedly enhanced. This study underscores the necessity of incorporating XPAR data assimilation in numerical weather prediction practices and lays the groundwork for the future joint assimilation of SMSR and XPAR data. [ABSTRACT FROM AUTHOR]

Traditional radar systems use fixed patterns and constant electromagnetic wave transmission to illuminate targets, but they often do not effectively use prior information about targets and consume significant radar resources. Cognitive radar has emerged as a way to improve resource efficiency and address these shortcomings. A joint waveform design and resource allocation strategy for cognitive radar that incorporates target situational awareness is proposed. This method integrates the interacting multiple model algorithm and the Unscented Kalman Particle Filter to achieve target situation awareness as prior knowledge. By combining the target attitude and the frequency response function of the target radar cross section at different time points in the prior knowledge, a joint beam control and power allocation strategy is formulated and transformed into an optimization problem. In addition, a cognitive pulse‐to‐pulse frequency agile waveform design method is proposed to support multiple target tracking under complex motion models. Simulation experiments demonstrate the effectiveness of this approach in obtaining accurate target situation information, achieving beam control, and optimizing power allocation. The designed waveforms can enhance radar target detection performance and improve low probability of intercept characteristics by adjusting the pulse repetition interval. This method has significant technical value. [ABSTRACT FROM AUTHOR]

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]

Forward‐looking radar imaging is a top priority due to a variety of applications. An airborne forward‐looking radar imaging approach via a modified propagator method (MPM) based on the planar phased array is proposed. It can obtain a better focusing effect without range migration correction. Through the MPM algorithm, the azimuth resolution can be enhanced greatly. Compared with the conventional synthetic aperture radar imaging algorithm, the left‐right ambiguity process can be avoided by generating two‐dimensional spatial spectra. Furthermore, the imaging results of different numbers of elements are given to provide an assessment of the imaging performance. Additionally, the authors proposed to apply the scanning planar phased array configuration to airborne forward‐looking two‐dimensional imaging for the first time. The validity of the method is proven by experiments. The image is more focused. Furthermore, the results of the experiments verify that the proposed method is fit for high‐speed situation and aircraft height variation condition. [ABSTRACT FROM AUTHOR]

Gait recognition is to recognise different individuals based on their faint differences of gait characteristics, which is different from and more challengeable than the recognition of human activities based on relatively bigger differences between different motions. Existing millimetre‐wave Multiple Input Multiple Output radar point cloud data contains time‐varying three‐dimensional spatial positions, velocity, and intensity information. How to enhance the accuracy of gait recognition by effectively utilising the available radar point cloud data has become an attractive research topic in recent years. A velocity‐depth‐time (VDT) based point cloud construction method for millimetre‐wave Multiple Input Multiple Output radar is proposed for gait recognition application, which can not only alleviate the sparsity problem of mmWave point cloud but also make the constructed point cloud to exhibit temporal structural features of micro‐motions, and therefore enable the successful application of PointNet++ to mmWave‐MIMO point cloud gait recognition. New point clouds are constructed by the proposed method using public gait recognition datasets of 10 and 20 individuals from mmWave‐MIMO radar, which are used to conduct gait recognition experiments using PointNet++. The results show that the point clouds constructed based on VDT are more conducive to the gait recognition task. Even using the classic PointNet++ model, which is not specially designed for radar point clouds, high recognition accuracy can be achieved for gait recognition tasks. The recognition accuracies are improved by 11% and 12% in this work for datasets of 10 and 20 individuals, respectively, compared with the 84% and 80% achieved by the traditional method using the same dataset and the same PointNet++ model, while the accuracies are improved by 5% and 12%, respectively, compared with the 90% and 80% achieved by the original dataset thesis method corresponding to 10‐individual and 20‐individual datasets. [ABSTRACT FROM AUTHOR]

Weather radar is an active remote sensing device used to monitor the full lifecycle changes in severe convective weather with high spatial and temporal resolution. Effective radar calibration is a crucial foundation for ensuring the high-quality application of observational data. This paper utilizes a UAV platform equipped with a high-precision RTK system and standard metal spheres to study the principles and methods of metal sphere calibration, constructing a complete calibration process and calibration accuracy evaluation metrics. Additionally, a collocated radar comparison observation experiment was conducted for cross-validation, and metal sphere calibration tests were performed on problematic radars. The experimental results indicate the following: (1) The combined application of a high-precision RTK system and a laser range camera can provide real-time position information on the metal sphere, improving the efficiency of radar target acquisition. (2) The calibration method based on UAV-suspended metal spheres can periodically conduct the quantitative calibration of Z and ZDR, achieving calibration accuracies within 0.5 dB and 0.2 dB, respectively, and supports the qualitative inspection of key parameters such as beamwidth and pulse width. (3) During field tests, a high success rate "coarse adjustment + fine adjustment + staring" sphere-finding technique was established, based on automatic switching between RHI, PPI, and FIX scanning modes. This method directs the UAV to adjust the metal sphere to the center of the radar distance bin, reducing the impact of uneven beam filling and bin crossing, ensuring the accuracy of scattering characteristic measurements. [ABSTRACT FROM AUTHOR]

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]

Radar technology is critical for subsurface sensing applications since it enables the extraction of surface information remotely without being affected by environmental factors such as illumination and weather conditions. Hence, their usage on drones has become extremely essential as these vehicles need to accumulate data while flying over different and possibly difficult-to-access terrains regardless of visibility conditions. A frequency-modulated continuous-wave (FMCW) single input multiple output (SIMO) radar system is developed to provide near real-time radar images onboard. The system is comprised of a transceiver module, specifically designed for synthetic aperture radar (SAR) applications, to produce images in real-time. GPU-based computation is utilized to optimize processing power, while enabling range profiles to be generated and SIMO SAR images to be created during flight. The system can be easily mounted on UAV or multirotor systems. The detection and ranging performance of the system are examined by performing several experiments at the campus. Objects up to 100 metres are successfully detected by the radar system. The system demonstrates excellent ability to generate 2D SIMO SAR images, effectively identifying building and guard-box exteriors that are consistent with images from Google Earth. [ABSTRACT FROM AUTHOR]

In this letter, a novel algorithm is proposed to design symbol‐level waveform for dual‐functional radar‐communication (DFRC) systems with low range sidelobe. Different from current schemes design waveform at the block level, a phase response constraint is introduced at the symbol level to provide an additional degree of freedom to decrease the range sidelobe in the pulse compression procedure, which is highly desired in radar systems. In particular, the phase response at the target direction is constrained to be similar to the given reference phase, and the matching error between the designed beampattern and the desired one is minimized subject to the constant modulus constraint. Furthermore, to guarantee the quality of service for communication, constructive interference is exploited at the symbol level for each communication user. An alternating direction method of multipliers algorithm is also proposed to solve the resulting nonconvex optimization problem with tractable subproblems solved sufficiently by the manifold optimization and standard quadratic problem. Numerical simulation results demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

The authors investigate the performance of an original multifunction system with two in‐band full‐duplex capable joint radar, communications and security (JRCS) transceivers in the presence of an eavesdropper. The system concept can be generalised to a network of more than two JRCS transceivers and multiple eavesdroppers, despite the present study focusing on the three‐node scenario. By combining a bandlimited pseudonoise waveform with a data‐containing orthogonal frequency‐division multiplexing (OFDM) waveform, the authors are able to ensure a certain jamming‐to‐signal power ratio (JSR) at the eavesdropper, whilst ideal synchronisation ensures that jamming causes no deterioration to their own data transfer or radar sensing performance as it is possible to remove just the known pseudonoise waveform. To validate the system, the authors investigate through simulations the OFDM symbol error rates of all the receivers, radar target signal‐to‐interference‐plus‐noise power ratios as well as receiver operating characteristic curves, and eavesdropper's detection signal‐to‐noise power ratios through two‐branch receiver cross‐correlation. The results show that already a very low JSR of −20 dB can improve physical‐layer security without a significant increase in friendly symbol error rate or deterioration in radar performance. Additionally, other full‐duplex transceivers potentially occupying the same radio resources improve the secrecy even further. [ABSTRACT FROM AUTHOR]

A novel method is proposed for rail fastener detection based on millimetre‐wave (mmWave) radar, mmWave radar fastener counter (MMW‐FC), which can accurately detect and record the fasteners in real‐time as the train traverses its route. Under circumstances where GNSS signals remain unavailable for prolonged durations, precise train localisation can be accomplished by correlating the number of fasteners derived from this method with the corresponding track map. Initially, MMW‐FC utilises fast Fourier transform and adaptive beamforming to focus the energy reflected from fasteners. Subsequently, it applies an adaptive template‐matching algorithm to detect each fastener. Furthermore, by leveraging known fastener spacing and the average time for trains to pass adjacent fasteners, the Kalman filter can execute precise speed tracking, used as a speed reference when adjusting the matching template adaptively. The experimental results indicate that the proposed method can precisely count the fasteners the train encounters in diverse road and speed conditions. The fastener counter maintains the Counting Error less than 0.067%, the speed error stays below 1.8 km/h, and the maximum values of the mean absolute error and root mean square error for speed are 0.7337 and 0.9584 km/h, respectively. [ABSTRACT FROM AUTHOR]

Efficient and effective radar emitter recognition is critical for electronic support measurement (ESM) systems. However, in complex electromagnetic environments, intercepted pulse trains generally contain substantial data noise, including spurious and missing pulses. Currently, radar emitter recognition methods utilizing traditional artificial neural networks (ANNs) like CNNs and RNNs are susceptible to data noise and require intensive computations, posing challenges to meeting the performance demands of modern ESM systems. Spiking neural networks (SNNs) exhibit stronger representational capabilities compared to traditional ANNs due to the temporal dynamics of spiking neurons and richer information encoded in precise spike timing. Furthermore, SNNs achieve higher computational efficiency by performing event-driven sparse addition calculations. In this paper, a lightweight spiking neural network is proposed by combining direct coding, leaky integrate-and-fire (LIF) neurons, and surrogate gradients to recognize radar emitters. Additionally, an improved SNN for radar emitter recognition is proposed, leveraging the local timing structure of pulses to enhance adaptability to data noise. Simulation results demonstrate the superior performance of the proposed method over existing methods. [ABSTRACT FROM AUTHOR]

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]

A dual-polarized compact Vivaldi antenna with high gain performance is proposed for tree radar applications. The proposed design introduces an array configuration consisting of two pairs of two Vivaldi elements to optimize the operating bandwidth and gain while providing dual-polarization capability. To enhance the gain of the proposed antenna over a certain frequency range of interest, directors and edge slots are incorporated into each Vivaldi element. To further enhance the overall antenna gain, a metal back reflector is used. The measurement results of the fabricated antenna show that the proposed antenna achieves a high gain of 5.5 to 14.8 dBi over broadband from 0.5 GHz to 3 GHz. Moreover, it achieves cross-polarization discrimination larger than 20 dB, ensuring high polarization purity. The fabricated antenna is used to detect and image the defects inside tree trunks. The results show that the proposed antenna yields a better-migrated image with a clear defect region compared to that obtained by a commercial Horn antenna. [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]

Beamforming is an effective way of resolving target direction and anti‐jamming in short wave (SW) radar systems. In conventional beamforming (CBF) at a certain frequency, to get high resolution, the array aperture should be increased, and this is often not allowed in practical applications. A new narrow beam forming (NBF) method for beam sharpening based on the generalised oblique projection (GOP) filter with a flexible parameter is proposed. This method uses a GOP filter bank to form deep nulls in the undesired azimuth range on the pattern and utilises the logic product process to synthesise the GOP filters' outputs and thus obtains a narrow beam. Compared to traditional beamforming methods, the result of NBF has the characteristics of narrower beam width and bigger side lobe suppression ratio (SLSR). Especially, a narrower beam can be obtained in the case of a small array aperture, which is valuable for practical applications. Experimental results of the range‐Doppler spectrum of short wave radar show that this narrow beam forming method can achieve super resolution of targets within a wide beam and greatly suppress clutter. Therefore, NBF can improve the azimuth resolution and achieve interference suppression in a conventional beam. [ABSTRACT FROM AUTHOR]

The measurement of the target azimuth angle using forward-looking radar (FLR) is widely applied in unmanned systems, such as obstacle avoidance and tracking applications. This paper proposes a semi-supervised support vector regression (SVR) method to solve the problem of small sample learning of the target angle with FLR. This method utilizes function approximation to solve the problem of estimating the target angle. First, SVR is used to construct the function mapping relationship between the echo and the target angle in beamspace. Next, by adding manifold constraints to the loss function, supervised learning is extended to semi-supervised learning, aiming to improve the small sample adaptation ability. This framework supports updating the angle estimating function with continuously increasing unlabeled samples during the FLR scanning process. The numerical simulation results show that the new technology has better performance than model-based methods and fully supervised methods, especially under limited conditions such as signal-to-noise ratio and number of training samples. [ABSTRACT FROM AUTHOR]

Holographic synthetic aperture radar (HoloSAR) introduces a cutting-edge three-dimensional (3-D) imaging mode to the field of synthetic aperture radar (SAR), enriching the scattering information of targets by observing them across multiple spatial dimensions. However, independent phase errors among baselines, such as those caused by platform jitter and measurement inaccuracies, pose significant challenges to imaging quality. The phase gradient autofocus (PGA) method effectively estimates phase errors, but struggles to accurately estimate the linear component, causing vertical shift in HoloSAR subaperture imaging result. Therefore, this paper proposes a PGA-based phase error compensation method for HoloSAR to address the vertical shift issue caused by linear phase errors. This method can achieve phase error correction in both the echo domain and image domain with enhanced efficiency. Experimental results of simulated targets and real data from the GOTCHA system demonstrate the effectiveness and practicality of the proposed method. [ABSTRACT FROM AUTHOR]

In recent years, the high-frequency hybrid sky-surface wave radar (HSSWR) has been increasingly used for target detection applications. Nevertheless, the specific bistatic system layout and the phase path disturbances induced by the ionospheric propagation channel may severely spread the sea clutter spectrum, thereby deteriorating the detection ability of the HSSWR for slow-moving targets. In this work, a novel subspace method based on the hybrid use of the amplitude and phase estimator (APES) and the orthogonal projection (OP) in the spatial–temporal domain, denoted as the APES-OP method, is proposed to suppress the spreading first-order sea clutter of the HSSWR. The distribution characteristics of targets and first-order sea clutter in the spatial–temporal domain were investigated, and a time-domain subspace signal model was adopted to describe targets perturbed by ionospheric phase path modulation. An APES filter was adopted to filter out the potential targets with a traversal approach to avoid attenuating desired signals while suppressing sea clutter. After that, sampling data from multi-channels and slow-time domains at the cell under test were employed to construct a spatial–temporal matrix, which was then utilized to obtain the sea clutter subspace by singular value decomposition. Simulation results indicate that the proposed algorithm can suppress sea clutter while retaining the target, even if the target is buried by sea clutter. The processing results of measured data further demonstrate the efficiency of the proposed algorithm. After sea clutter suppression, the target obscured by clutter can be revealed, and the signal-to-clutter ratio of the target is greatly improved. [ABSTRACT FROM AUTHOR]

A high‐efficiency polarization conversion metasurface (PCM) is proposed and applied to radar cross section (RCS) reduction. The PCM can achieve cross‐polarization conversion with a polarization conversion ratio of over 97% from 13.9 to 30.3 GHz, covering K‐band. The low‐RCS antenna is designed from a circularly polarized patch antenna loading with the PCM. The antennas with and without PCM are fabricated and measured. The results show that the radiation performance is well preserved and the scattering performance is greatly improved. The average monostatic RCS reduction value reaches 14.6 dB in an ultra‐wideband from 12.2 to 30.2 GHz under normal incidence. What's more important, bistatic RCS is researched in detail and evident RCS reduction is achieved at different incidence angles. This research has potential applications in the field of stealth platform. [ABSTRACT FROM AUTHOR]

Radar systems are a topic of great interest, especially due to their extensive range of applications and ability to operate in all weather conditions. Modern radars have high requirements such as its resolution, accuracy and robustness, depending on the application. Noise Radar Technology (NRT) has the upper hand when compared to conventional radar technology in several characteristics. Its robustness to jamming, low Mutual Interference and low probability of intercept are good examples of these advantages. However, its signal processing is more complex than that associated to a conventional radar. Artificial Intelligence (AI)‐based signal processing is getting increasing attention from the research community. However, there is yet not much research on these methods for noise radar signal processing. The aim of the authors is to provide general information regarding the research performed on radar systems using AI and draw conclusions about the future of AI in noise radar. The authors introduce the use of AI‐based algorithms for NRT and provide results for its use. [ABSTRACT FROM AUTHOR]

Noise impairs the performance of inverse synthetic aperture radar (ISAR) motion compensation, which induces severe defocusing under low signal‐to‐noise ratio environments. To overcome this issue, a novel similarity‐oriented (SO) method with a two‐domain denoising strategy is proposed. A PIxEl similarity‐oriented (PIE‐SO) denoising method designed for range‐Doppler (RD) domain and a modified RAnge Profile Similarity‐Oriented (RAP‐SO) denoising method designed for high‐resolution range profile (HRRP) matrix are included in the presented framework. Firstly, the PIE‐SO method directly performs a two‐dimensional fast Fourier transform on dechirp processed echo data to form a coarsely focusing ISAR image in the RD domain. Then the focusing image is separated from the noise background by virtue of pixel similarity, after which the noise is preliminarily removed. Subsequently, the coarsely denoised image is transformed into the HRRP matrix. Considering the range profile similarity impaired by noise is restored by the PIE‐SO denoising, a Laplacian regularised‐weighted nuclear norm proximal (LR‐WNNP) operator is proposed. The proposed modified RAP‐SO method, that is, the LR‐WNNP operator, exploits the low‐rank property of the HRRP matrix and the local similarity of adjacent HRRPs to reduce the residual noise. As a result, ISAR imaging quality is significantly improved. Comprehensive experiments illustrate the effectiveness and superiority of the presented method. [ABSTRACT FROM AUTHOR]

Ultra-wideband antennas that operate at low frequencies usually have a large geometry, which makes them very limited in practical engineering application environment where space is often constrained. In this paper, a novel low-frequency low-profile ultra-wideband magneto-electric (ME) dipole antenna is designed for the application in ground penetrating radar (GPR) to detect deeply buried targets. The antenna is designed at the operation frequency of 230 MHz, so that the objects that are buried as deep as 1.5 m can be detected. Compared to the traditional low-frequency _antennas, it has a profile as low as 740 mm $ \times $ × 140 mm $ \times $ × 150 mm ($0.49\lambda \times 0.09\lambda \times 0.10\lambda $ 0.49 λ × 0.09 λ × 0.10 λ). Based on the simulation analysis and validation measurements, the proposed ME dipole antenna has a wide impedance bandwidth for ${S_{11}} \lt - 10{\rm{dB}}$ S 11 < − 10 dB from 135 MHz to 382 MHz even when environmental influence is considered. The antenna is further validated with a GPR experiment, where a pipeline with a diameter of about 200 mm at 1200 mm beneath the road surface is successfully detected by an impulse GPR system where the proposed antennas are integrated. [ABSTRACT FROM AUTHOR]

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]

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]

Radar High‐Resolution Range Profile (HRRP) has great potential for target recognition because it can provide target structural information. Existing work commonly applies deep learning to extract deep features from HRRPs and achieve impressive recognition performance. However, most approaches are unable to distinguish between the target and non‐target regions in the feature extraction process and do not fully consider the impact of background noise, which is harmful to recognition, especially at low signal‐to‐noise ratios (SNR). To tackle these problems, the authors propose a radar HRRP target recognition framework termed Adaptive Soft Threshold Transformer (ASTT), which is composed of a patch embedding (PE) layer, ASTT blocks, and Discrete Wavelet Patch Merging (DWPM) layers. Given the limited semantic information of individual range cells, the PE layer integrates nearby isolated range cells into semantically explicit target structure patches. Thanks to its convolutional layer and attention mechanism, the ASTT blocks assign a weight to each patch to locate the target areas in the HRRP while capturing local features and constructing sequence correlations. Moreover, the ASTT block efficiently filters noise features in combination with a soft threshold function to further enhance the recognition performance at low SNR, where the threshold is adaptively determined. Utilising the reversibility of the discrete wavelet transform, the DWPM layer efficiently eliminates the loss of valuable information during the pooling process. Experiments based on simulated and measured datasets show that the proposed method has excellent target recognition performance, noise robustness, and small‐scale range shift robustness. [ABSTRACT FROM AUTHOR]

Measuring heart rate is a critical component of assessing cardiac health and detecting potential heart diseases at an early stage. Various sensors, including electrocardiogram and photoplethysmograph, are commonly employed for this purpose. However, these conventional methods necessitate direct contact with the patient's skin which may be impractical or uncomfortable in situations involving patients with skin diseases or burn injuries. To address these limitations, a concerted effort has been to develop non‐contact methods leveraging frequency‐modulated continuous‐wave multiple‐input multiple‐output radar systems. Recent studies have illustrated the sensitivity of the phase component of received signals to micro‐motion, presenting a promising avenue for effective heart rate estimation. However, existing literature predominantly focuses on single‐antenna setups, overlooking the potential benefits offered by multiple‐input multiple‐output systems, which provide diverse channels with varying precision levels. This correspondence introduces a novel phase extraction model grounded in the argument of the analytic signal derived from both in‐phase and quadrature channels. Furthermore, leveraging the normal equation, we establish the feasibility of optimizing weights assigned to individual virtual antennas to achieve a robust approximation of ground truth data. [ABSTRACT FROM AUTHOR]

Near-range radar imaging (NRRI) has evolved into a vital technology with diverse applications spanning fields such as remote sensing, surveillance, medical imaging and non-destructive testing. The Pseudopolar Format Matrix (PFM) has emerged as a promising technique for representing radar data in a compact and efficient manner. In this paper, we present a comprehensive PFM description of near-range radar imaging. Furthermore, this paper also explores the integration of the Fractional Fourier Transform (FrFT) with PFM for enhanced radar signal analysis. The FrFT—a powerful mathematical tool for signal processing—offers unique capabilities in analysing signals with time-frequency localization properties. By combining FrFT with PFM, we have achieved significant advancements in radar imaging, particularly in dealing with complex clutter environments and improving target detection accuracy. Meanwhile, this paper highlights the imaging matrix form of FrFT under the PFM, emphasizing the potential for addressing challenges encountered in near-range radar imaging. Finally, numerical simulation and real-world scenario measurement imaging results verify optimized accuracy and computational efficiency with the fusion of PFM and FrFT techniques, paving the way for further innovations in near-range radar imaging applications. [ABSTRACT FROM AUTHOR]

This paper presents a novel, cost-effective technique for estimating the Doppler effect in the time domain using a single pulse and subsequently leveraging the precise Doppler value to recover the radar waveform. The proposed system offers several key advantages over existing techniques, including the ability to calculate the target speed without any frequency ambiguity and the ability to detect a wide range of target speeds. These two features are not available in any existing techniques, including the conventional moving target detection (MTD) processor. To ensure improved accuracy and robust estimation, the system employs ensemble and iterative techniques by recursively and efficiently reducing the Doppler residues from the signal. Furthermore, the proposed system demonstrates effective signal recovery of a well-known bi-phase code shape at low signal-to-noise ratios in just a few iterations. The performance evaluation of the new algorithm demonstrates its practicability and its superiority over traditional radar systems. Implementation on software-defined radio (SDR) reveals that the proposed system excels in Doppler estimation and signal recovery at low SNRs, demonstrating promising results. [ABSTRACT FROM AUTHOR]

To enhance the capability of identifying unknown emitters in open spaces, an open‐multiscale attention kernel (MSAK)‐convolutional neural network‐long short‐term memory (CNN‐LSTM) structure is proposed. To this end, first, a MSAK module and CNN‐LSTM structure are introduced, and then, the depth and complexity of the feature extraction network are improved to enhance its representation capability. To classify unknown emitters accurately, the MSAK‐CNN‐LSTM model is improved to obtain an open‐MSAK‐CNN‐LSTM model with open‐set recognition capability. Additionally, the two preprocessing procedures are summarised, and their strengths and weaknesses are compared. Experimental results show that the proposed open‐MSAK‐CNN‐LSTM model achieves satisfactory accuracy in identifying unknown emitters in open space. In addition, it has significant advantages in low signal‐to‐noise ratio (SNR) scenarios. [ABSTRACT FROM AUTHOR]

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]

Objectives: Mine slope instability is one of the main factors restricting the safety production of open-pit mines in China. Ground-based synthetic aperture radar interferometry technology has been gradually introduced into the application of slope safety monitoring and early warning prediction in open-pit mines. However, the high-frequency rolling update characteristics of ground radar interferometry data lead to large data error accumulation and unobvious curve mutation characteristics. Methods: Processing the original data by dislocation subtraction and velocity reciprocal method can effectively reduce the vibration of high-frequency data, improve the readability of critical sliding data. After data processing, it can highlight the trend characteristics of key deformation data. The research is based on the analysis of cumulative displacement curve, velocity curve and reciprocal velocity curve group treated with different periods. Results: It is found that there are three characteristic points in the curve group: Sudden deformation increase point, velocity increase point and stable vibration point. Through these characteristic points, the slope landslide disaster can be predicted. The trend of key deformation data can be highlighted by using the three feature points of deformation sudden increase point, velocity growth point and stable vibration point. Conclusions: Through the identification of three feature points, the possible landslide can be effectively identified in advance and the landslide time can be predicted, which provides a new technical path and solution for landslide early warning and prediction analysis based on ground-based interferometric radar [ABSTRACT FROM AUTHOR]

The integration of both sensing and communication functions is a crucial feature for future communication systems. This paper considers a novel scenario where a radar covers multiple small-cell base stations which operate in different spectra. Due to the limited spectra resource, the radar needs to reuse the spectra of one BS for tracking. To suppress interference and improve performance, we propose a framework including two stages and the corresponding communication and sensing algorithms. Specifically, at the detection stage, we aim to minimize the system transmit power while maintaining the performance of both radar and communication. At the tracking stage, our goal is to maximize the radar sensing performance under a given power budget, while ensuring communication quality. Due to the complexity of the original problem, we address it by introducing auxiliary variables and proposing a penalty dual decomposition-based algorithm, enabling us to solve a more tractable form of the problem. In the inner loop, we propose a concave–convex procedure-based algorithm to handle the optimization problem, while adopting the block coordinate descent algorithm to iteratively update the variables. In the outer loop, we update the penalty term or Lagrange multipliers. Furthermore, we propose a radar target parameter estimation algorithm based on successive interference cancellation, which aims to mitigate communication interference. Finally, numerical simulations demonstrate the effectiveness of the proposed system, and the superior performance of our proposed algorithms over benchmark algorithms. [ABSTRACT FROM AUTHOR]

Aiming at the large-scale application of metasurface in the field of radar stealth, we present a hybrid resonance-based and dispersion substrate integrated checkerboard metasurface (CMS) for reducing the radar cross section (RCS) of flat metallic targets. Considering the frequency-dependent characteristics of such a dispersion material, a pair of single and dual resonant artificial magnetic conductor meta-atoms with the modified "crusades-like" cell topologies is employed to maximize the operating bandwidth; besides, a comprehensive and thorough investigation on the resonance mechanism is conducted in this paper to provide an intuitive physical insight of meta-atoms' reflection responses. By comparing the predicted results with simulations, the quasi-periodic effect is introduced to explain the frequency shift of 10 dB RCS reduction bandwidth. In the implementation procedure, a prototype of the designed RCS reducer with a total dimension of 180 × 180 mm2 is fabricated and measured, the 10 dB RCS reduction bandwidth of theoretical simulation and experimental measurement are basically consistent, and the performance improvement of 8 dB RCS reduction in the experimental results can be attributed to the dispersion effects of the dielectric substrate. With a better figure of merit, our efforts may serve as a useful exemplar for the economical CMS architecture in radar evasive applications. [ABSTRACT FROM AUTHOR]

The authors deal with the problem of the design and manufacture of a high‐resolution FMCW radar for drone detection and classification. The difficulties of the problem are discrete clutter reduction and spurious phase noise mitigation. The discrete clutter is due to the reflected signals from land vehicles, birds etc., while the spurious phase noise is inherent in the radar signal due to the phase‐locked loop component and leakage between the transmitting and receiving paths. Both spurious phase noise and clutter will increase the system noise level and hence reduce the probability of detection of small targets such as drones and induce false alarms on the radar screen. In order to reduce discrete clutter, the authors propose a method to separate a drone from discrete clutter based on the design of the radar system parameters for a drone and its propeller detection, target's Doppler dispersion and moving characteristics. For spur mitigation, a method that focuses on the design of the isolation coefficient between transmitting and receiving paths to decrease the power of spurs below the minimum power requirement at the input of the analogue‐to‐digital converter is introduced. The results were applied by the authors to the development and manufacture of a radar with the given specifications for drone detection and classification. Different laboratory and field tests show that the spurs are mitigated and the drones are separated from discrete clutter with a range and accuracy better than the one recently published. [ABSTRACT FROM AUTHOR]

The application and uses of drones in all areas are continuously rising, especially in civilian use cases. This increasing threat requires reliable drone surveillance in urban environments. Radar is the obvious candidate with its ability to detect small objects at range, in all weather conditions. The use of an L‐band networked radar for urban sensing of S‐UAS targets is explored. Small echoes from S‐UAS places a premium on synchronisation, which is the fundamental key for high performance networked radar. The effect of timing errors on the operation of the network radar is investigated theoretically and experimentally, and the processing tools for synchronising data based on the direct signal returns of the transmitter are developed. Also, drone detection using bistatic L‐band staring radar is achieved both in simulation and then in real field trials where the SNR and detection performance are computed and analysed. The updated direct signal synchronisation method for bistatic staring radar is shown to provide comparable SNR and positional accuracy for S‐UAS targets as the monostatic staring radar. [ABSTRACT FROM AUTHOR]

Long baseline bistatic radar systems herald enhanced sensitivity and metric accuracy for space objects in geosynchronous orbits and beyond. Radio telescopes are ideal participants in such a system; in particular, they often feature large apertures with low‐noise temperatures and have stable, synchronised clocks. Pairing radio telescopes with high‐power radars creates new methodologies for Space Domain Awareness. This paper describes long baseline bistatic measurements using the Millstone Hill Radar in the USA, the Tracking and Imaging Radar in Germany, multiple receivers of the enhanced multi‐element remotely linked interferometer network array in the United Kingdom, and the Westerbork Synthesis Radio Telescope in the Netherlands. The authors, a Research Task Group formed by the NATO Science and Technology Organisation Sensors and Electronic Technology Panel (SET‐293), performed novel bistatic and monostatic radar imaging experiments with real on‐orbit tumbling rocket bodies. These experiments on tumbling objects at near‐geosynchronous orbits highlight successful demonstrations of advanced bistatic Doppler characterisation across diverse imaging geometries. Specialised Doppler processing on tumbling targets, such as the Doppler superpulse algorithm, enables high‐fidelity rotation period estimation and determination of minimum target size. [ABSTRACT FROM AUTHOR]

Detection of space objects is a key component of space situational awareness, which could help prevent and minimise space collisions. While there have been lots of radar systems designed to detect space objects, few of them have dealt with long baseline distributed bistatic pairs. The authors focus on the feasibility of long baseline bistatic radars, which can be extended for the multistatic case; and the performance of the multistatic system for a target at different altitudes assuming one transmitter over three different scenarios: a cluster of receivers, receivers spread throughout the world and the combination of the two previous cases. To analyse the performance the multiple‐input‐multiple‐output (MIMO) ambiguity function (AF) will be employed. The results of the MIMO AF show how the fusion of different bistatic pairs improves the detection capabilities. Moreover, when the different radar measurements are coherently summed in the MIMO AF, the uncertainty on the location of the target is reduced. [ABSTRACT FROM AUTHOR]

Developing a photonics radar to detect the combined multiple simultaneous-behaviours/postures of low-slow-small (LSS) aerial targets and appropriately extracting their Doppler profile have become significant to enable air surveillance/security and guidance for unmanned aviation. A CW photonic radar of 5.3 GHz, and an empirical wavelet transform (EWT) based radar signal processing algorithm are developed and their sensing/measurement capabilities are tested with representative LSS target models: two/three blades propeller systems, a warhead-like cone-shaped target and a bionic bird. Open field experiments with different simultaneous-behaviours of these LSS targets are conducted, and performance validation of radar, in terms of micro-Doppler signature extractions, cadence frequency imaging and targets' behavioural parameters measurement, is analysed, quantitatively investigated and the results are presented. All the measurement results keep an average of 99% of agreement with the standard measurement values that evidence the compatibility of developed photonics radar for precision sensing of multiple simultaneous-behaviours/postures of LSS aerial targets. [ABSTRACT FROM AUTHOR]

P-band radar interferometry is a promising tool for digital terrain modelling in forested areas such as the Amazon due to the ability of P-band to penetrate densely forested areas. The quality of Digital Terrain Models (DTMs) is a primary requirement for many geoscience applications because DTM accuracy is an essential variable for characterizing landform shapes. This article presents a multicriteria evaluation of the overall quality of a digital terrain model produced using P-band SAR interferometric processing. The evaluation considers behaviours related to elevation and slope and analyses the impact of landscape properties such as slope and aspect. The multicriteria accuracy of the study showed promising results about the realism of landforms, according to applied geomorphological criteria, and also in the visual comparison between the produced DTM and the reference. The study provided an estimate of the accuracy of P-band radar DTM in terms of elevation with a standard deviation of 2.36 m and in terms of slope with a mean error of approximately −4°, a standard deviation of 4.74°, and a RMSE (Root Mean Square Error) slope error of 6.14°, using a reference DTM derived from an airborne LiDAR (Light Detection and Ranging) survey. The spatial behaviour of these errors and their sensitivity to slope and aspect were analysed. The investigation of hydrographic inconsistencies in the DTM was conducted based on various criteria, including identifying depressions along watercourses and adhering to Horton's law. Finally, the effects of the acquisition, processing, and resampling processes are revealed through indicators such as the rose histogram. This multicriteria study demonstrates the suitability of airborne P-band radar interferometry for digital elevation modelling in tropical rainforest environments. [ABSTRACT FROM AUTHOR]

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]

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]

Aiming at the requirement of radar wave absorption in automotive radar tests for both the 24 GHz and 77 GHz vehicle radar bands, two ultra-thin dual-band circuit analog absorber designs based on frequency-selective conductive film have been proposed. They are obtained by first optimising the equivalent circuit parameters to achieve the desired reflection response, then determining the physical parameters (geometrical size and square resistance) of unit cells from the equivalent circuit parameters, and finally fine-tuning these physical parameters. Both designs employ unit cell based on the nested structure of conductive square ring and a conductive square patch, which has the advantages of simple structure, ease of processing, high design freedom, and good polarisation and angular stability. The 1st design employs different square resistance values for the conductive ring and patch, while the 2nd design employs a single square resistance value for both conductive shapes. Simulation results show that, for each of the two designs, the lower and upper 70% absorption or −10 dB reflection bands simultaneously cover the 24 GHz and 77 GHz vehicle radar bands, respectively, with good angular stability. The dual-band coverage keeps stable until the elevation incident angle reaches 20° and 50° for the 1st and 2nd designs, respectively. A realistic sample is processed regarding the 2nd design and its dual-band absorption performance has been verified by experimental results, which have good agreement with the simulated counterparts. [ABSTRACT FROM AUTHOR]

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]

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]

Synthetic-aperture radar (SAR) can work in all weather conditions and at all times, and satellite-borne radar has the characteristics of short revisiting period and large imaging width. Therefore, satellite-borne synthetic-aperture radar has been widely deployed, and the SAR images have been widely used in geographic mapping, radar interpretation, ship detection, and other fields. Satellite-borne synthetic-aperture radar is also susceptible to various types of intentional or unintentional interference during the imaging process, and because the interference is a direct wave, its power is much stronger than the wave reflected by targets. As a common interference pattern, radio-frequency interference widely exists in various satellite-borne synthetic-aperture radars, which seriously deteriorates SAR image quality. In order to solve the above problems, this paper proposes a feature decomposition network to suppress interference based on regularization optimization. The contributions of this work are as follows: 1. By analyzing the performance limitations of the existing methods, this work proposes a novel regularization method for radio-frequency interference suppression tasks. From the perspective of data distribution histograms and residual components, the proposed method eliminates the variable components introduced by common regularization, greatly reduces the difficulty of data mapping, and significantly improves its robustness and performance. 2. This work proposes a feature decomposition network, where the feature decomposition module contains two parts; one part only represents the interference signal, and the other part only represents the radar signal. The neurons representing the interference signal are discarded, and the neurons representing the radar signal are used as input for the subsequent network. A cosine similarity constraint is used to separate the interference from the network as much as possible. Finally, this method is validated on the MiniSAR dataset and Sentinel-1A dataset. [ABSTRACT FROM AUTHOR]

The steep and unstable terrain found on debris-covered glaciers, rock glaciers, talus slopes, moraines and other proglacial features often make terrestrial ground-penetrating radar (GPR) surveys unsafe or cost-prohibitive. To address these challenges, this research introduces a novel approach for studying buried ice using multi-low-frequency drone-based GPR. Monostatic antennas of 50, 100, and 200 MHz were flown along a transect spanning a debris-covered glacier and an ice–debris complex at Shár Shaw Tagà (Grizzly Creek) in southwest Yukon, Canada. The drone-based results were compared to manual GPR at two locations along the transect. The two manual segments were conducted using the same radar system in a bi-static mode and included common mid-point (CMP) surveys. Overall, the drone-based radar successfully identified buried ice and enabled estimation of ice body thickness. Notably, CMP results confirmed layer characteristics and enabled depths to be measured across the entire drone-based transect. Discrimination of detail across a range of depths was made possible by comparing the three low frequencies, highlighting the possibility of using this method for future investigations of debris thickness in addition to quantifying buried ice. This study confirms the effectiveness of drone-based GPR combined with manual CMP for surveying ice beneath previously inaccessible terrain. [ABSTRACT FROM AUTHOR]

In the passive bistatic radar (PBR) system, methods exist to address the issue of detecting weak targets without being influenced by non-ideal factors from adjacent strong targets. These methods utilize the sparsity in the delay-Doppler domain of the cross ambiguity function (CAF) to detect weak targets. However, the modeling and solving of this method involve substantial memory consumption and computational complexity. To address these challenges, this paper establishes a target detection model for PBR based on batch processing of sparse representation and recovery. This model partitions the CAF into blocks, identifies blocks requiring processing based on the presence of targets, and improves the construction and utilization of the measurement matrix. This results in a reduction in the computational complexity and memory resource requirements for sparse representation and recovery, and provides favorable conditions for parallel execution of the algorithm. Experimental results indicate that the proposed approach increases the number of blocks by a factor of four, and reduces the number of real multiplications by approximately an order of magnitude. Hence, compared with the traditional approach, the proposed approach enables fast and stable detection of weak targets. [ABSTRACT FROM AUTHOR]

Areal rainfall is routinely estimated based on the observed rainfall data using distributed point rainfall gauges. However, the data collected are sparse and cannot represent the continuous rainfall distribution (or field) over a large watershed due to the limitations of weather station networks. Recent improvements in remote-sensing-based rainfall estimation facilitate more accurate and effective hydrological modeling with a continuous spatial representation of rainfall over a watershed of interest. In this study, we conducted a systematic stepwise comparison of the areal rainfalls estimated by a synoptic weather station and radar station networks throughout South Korea. The bias in the areal rainfalls computed by the automated synoptic observing system and automatic weather system networks was analyzed on an hourly basis for the year 2021. The results showed that the bias increased significantly for hydrological analysis; more importantly, the identified bias exhibited a magnitude comparable to that of the low flow. This discrepancy could potentially mislead the overall rainfall-runoff modeling process. Moreover, the areal rainfall estimated by the radar-based approach significantly differed from that estimated by the existing Thiessen Weighting approach by 4%–100%, indicating that areal rainfalls from a limited number of weather stations are problematic for hydrologic studies. Our case study demonstrated that the gauging station density must be within 10 km2 on average for accurate areal rainfall estimation. This study recommends the use of radar rainfall networks to reduce uncertainties in the measurement and prediction of areal rainfalls with a limited number of ground weather station networks. [ABSTRACT FROM AUTHOR]