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The extensive deployment of wind farms has significantly impacted the detection capabilities of space–air bistatic radar (SABR) systems. Although space–time adaptive processing techniques are available, their performance is significantly degraded, and even unable to suppress clutter. This paper explores the geometric configuration of the SABR system and the selection of detection areas, establishing a space–time clutter model that addresses the effects of wind turbine clutter (WTC). Expressions for spatial and Doppler frequencies have been derived to deeply analyze the characteristics of clutter spreading. Building on this, the paper extends two-dimensional space–time data to three-dimensional azimuth–elevation–Doppler data. It proposes a three-dimensional space–time multi-beam (STMB) strategy incorporating the Ordering Points to Identify the Clustering Structure (OPTICS) clustering algorithm to suppress WTC effectively. This algorithm selects WTC samples and applies OPTICS clustering to the clutter-suppressed data to achieve this effect. Simulation experiments further verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of weak maneuvering target detection in the space-based bistatic radar system through long-time coherent integration (LTCI). The space-based bistatic radar is vulnerable to the high-order range migration (RM) and Doppler frequency migration (DFM), since the target, the receiver and the transmitter all can play fast movement independently. To correct high- order RM and DFM, this usually involves joint high-dimensional parameter searching, incurring a large computational burden. In our previous work, a dual-scale (DS) decomposition of motion parameters was proposed, in which the optimal GRFT is conditionally decoupled into two cascade procedures called the modified generalized inverse Fourier transform (GIFT) and generalized Fourier transform (GFT), resulting in the DS-GRFT detector. However, even if the DS-GRFT detector preserves the superior performance and dramatically decreases the complexity, high-dimensional searching is still required. In this paper, by analyzing the structure of the DS-GRFT detector, we further designed a conditioned cubic phase function (CCPF) tailored to the range–slow-time signal after GIFT, breaking the joint high-dimensional searching into independent one-dimensional searching. Then, by connecting the proposed CCPF with the GIFT, we achieved a new LTCI detector called the DS-GIFT-CCPF detector, which obtained a significant computational cost reduction with acceptable performance loss, as demonstrated in numerical experiments. [ABSTRACT FROM AUTHOR]

The traditional sparse recovery (SR) space-time adaptive processing (STAP) algorithms are greatly affected by grid mismatch, leading to poor performance in airborne bistatic radar clutter suppression. In order to address this issue, this paper proposes an SR STAP algorithm for airborne bistatic radars based on atomic selection under the Bayesian framework. This method adopts the idea of atomic selection for the process of Bayesian inference, continuously evaluating the contribution of atoms to the likelihood function to add or remove atoms, and then using the selected atoms to estimate the clutter support subspace and perform sparse recovery in the clutter support subspace. Due to the inherent sparsity of clutter signals, performing sparse recovery in the clutter support subspace avoids using a massive number of atoms from an overcomplete space-time dictionary, thereby greatly improving computational efficiency. In airborne bistatic radar scenarios where significant grid mismatch exists, this method can mitigate the performance degradation caused by grid mismatch by encrypting grid points. Since the sparse recovery is performed in the clutter support subspace, encrypting grid points does not lead to excessive computational burden. Additionally, this method integrates out the noise term under a new hierarchical Bayesian model, preventing the adverse effects caused by inaccurate noise power estimation during iterations in the traditional SR STAP algorithms, further enhancing its performance. Our simulation results demonstrate the high efficiency and superior clutter suppression performance and target detection performance of this method. [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]

In this paper, to track maneuvering unmanned surface vehicles (USVs) in scenarios with clutter, we propose a novel method based on the probabilistic data association (PDA) algorithm and Rao-Blackwellized particle filter (RBPF) algorithm, and we further improve the tracking performance by Max-Q criterion-based waveform selection. This work develops a maneuvering target model in the context of clutter, integrating linear and nonlinear states as well as observations with false alarms. In order to jointly tackle the mixed-state tracking problem, the PDA algorithm is integrated into the RBPF framework. This allows it to be used with the complex nonlinear and linear hybrid system and helps to minimize the state dimensions of conventional particle filtering (PF). Additionally, by utilizing Q-learning principles, we provide a Max-Q-based criterion to select the waveform parameters, which guarantees low measurement errors and efficiently handles measurement uncertainties. Our simulation results show that the PDA-RBPF algorithm, which has a more appropriate tracking mechanism, produces results that are more accurate than those of the EKF or PF algorithms alone. Furthermore, the RMSE derived by the Max-Q-based criterion is smaller and more robust than that of other selection methods, as well as yielding a fixed waveform. Our proposed mechanism, which combines the concepts of PDA-RBPF and Max-Q waveform selection, performs well in target tracking tasks and exhibits relatively good performance over some existing ones. [ABSTRACT FROM AUTHOR]

This paper presents three refinements in ground-based radar interferometer (GB-radar) measurement for bridge load testing: (1) GB-radar phase jumps were detected for the first time on bridge tower displacement monitoring, and a recovery method is presented to obtain the correct unwrapped value; (2) a precise displacement projection method considering target deformation was exploited, and a case study of the Fifth Nanjing Yangtze River Bridge (FNYRB) GB-radar campaign shows that a centimeter-level compensation can be achieved; (3) a post-construction settlement phenomenon was found during the FNYRB static load tests, characterized by 0.31 mm/min, which accumulated up to 25 mm. In addition, the dynamic monitoring capabilities of GB-radar for the bridge tower and girder were verified, highlighting its potential for bridge structural health monitoring (SHM). The insights gained from this study offer valuable recommendations for future GB-radar bridge displacement monitoring. [ABSTRACT FROM AUTHOR]

The electromagnetic wave transmitted by the millimeter-wave radar can penetrate flames, smoke, and the high-temperature field, and is the main sensor for detecting disaster victims in closed spaces. However, a moving target in the closed space will produce a considerable number of false detections in the point cloud data collected by the radar due to multipath scattering. The false detections lead to false trajectories generated by multi-target tracking filters, such as the labeled multi-Bernoulli (LMB) filter, which, therefore, leads to inaccurate estimation of the number of pedestrians. Addressing this problem, in this paper, a three-class combination of the clutter point clouds model is proposed: static clutter, non-continuous dynamic clutter (NCDC), and continuous dynamic clutter (CDC). The model is based on the spatial and temporal distribution characteristics of the CDC sequence captured by a two-dimensional (2D) millimeter-wave (MMW) radar. However, in open space, CDC appears infrequently in radar tracking applications, and thus has not been considered in multi-target tracking filters such as the LMB filter. This leads to confusion between the CDC point cloud collected by the high-resolution radar in closed spaces and the real-target point cloud. To solve this problem, the impact mechanism of the LMB filter on prediction, update, and state estimation is modeled in this paper in different stages based on the temporal and spatial distribution characteristics of CDC. Finally, a hybrid clutter model-based LMB filter (HCM-LMB) is proposed, which focuses on scenes where NCDC and CDC are mixed. The filter introduces the temporal and spatial distribution characteristics of NCDC based on the original LMB filter, and improves the prediction, update, and state estimation of the original filter by combining the impact mechanism model and the new CDC prediction, CDC estimation, and false trajectory label management algorithm. Experiments were conducted on pedestrians in building corridors using 2D MMW radar perception. The experimental results show that under the influence of CDC, the total number of pedestrians estimated by the HCM-LMB filter was reduced by 22.5% compared with that estimated by the LMB filter. [ABSTRACT FROM AUTHOR]

Spectrum aliasing occurs in signal echoes when the sampling frequency does not comply with the Nyquist Sampling Theorem. In this scenario, the extraction of micro-motion parameters becomes challenging. This paper proposes a spectral de-aliasing method for micro-motion signals based on a complex-valued U-Net network. Zero interpolation is employed to insert zeros into the echo, effectively increasing the sampling frequency. After zero interpolation, the micro-motion signal contains both real micro-motion signal frequency components and new frequency components. Short-Time Fourier Transform (STFT) is then applied to transform the zero-interpolated echo from the time domain to the time–frequency domain. Furthermore, a complex-valued U-Net training model is utilized to eliminate redundant frequency components generated by zero interpolation, thereby achieving the frequency reconstruction of micro-motion signal echoes. Finally, the training models are employed to process the measured data. The theoretical analysis, simulations, and experimental results demonstrate that this method is robust and feasible, and is capable of addressing the problem of micro-motion signal echo spectrum aliasing in narrowband radar. [ABSTRACT FROM AUTHOR]

Feature capture and recognition of sea wave components in radar systems especially in global navigation satellite system reflectometry (GNSS-R) using signal processing approaches or computer simulative methods has become a research hotspot in recent years. At the same time, parameter inversion of marine phenomena from the discovered characteristics plays a significant role in monitoring and forewarning the different components of sea waves. This paper aims to investigate the impact of multiple parameters, such as the wind speed, directionality variable, wave amplitude, wave length, and directions of sea wave components, on the delay waveform of the delay–Doppler map (DDM). Two types of wind waves and the 2-D sinusoidal sea surface are chosen to be analyzed. By comparing and analyzing the discrepancy of delay waveforms under different conditions, it can be concluded that the increased MSS which arises from the increase in the roughness of the sea surface can lead to the difference in the peak value or trial edges exhibited in delay waveforms. The values of delay waveforms at zero chip along the increasing direction of long-crest wind waves exhibit the periodic spikes shape, which is the opposite of the short-crest wind waves, and the fluctuation of the periodic profiles decreases with the increase in the amplitude of waves. The results and conclusions can provide a foundation for the parameter inversion, tracking, and early warning of anomalous formations of waves in bistatic radar configuration. [ABSTRACT FROM AUTHOR]

An airborne bistatic radar working in downward-looking mode confronts two major challenges for low-altitude target detection. One is range cell migration (RCM) and Doppler migration (DM) resulting from the relative motion of the radar and target. The other is the non-stationarity characteristic of clutter due to the radar configuration. To solve these problems, this paper proposes a joint implementation method based on sub-aperture processing to achieve clutter suppression and coherent maneuvering target detection. Specifically, clutter Doppler compensation and sliding window processing are carried out to realize sub-aperture space–time processing, removing the clutter non-stationarity resulting from the bistatic geometric configuration. Thus, the output matrix of clutter suppression in the sub-aperture could be obtained. Then, the elements with the same phase of this matrix are superimposed and rearranged to achieve the reconstructed 2-D range-pluse echo matrix. Next, the aperture division with respect to slow time is conducted and the RCM correction based on modified location rotation transform (MLRT) and coherent integration (CI) are realized within each sub-aperture. Finally, the matched filtering process (MFP) is applied to compensate for the RCM/DM among different sub-apertures to coherently integrate the maneuvering target energy of all sub-apertures. The simulation and measured data processing results prove the validity of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the possibility of improving target detection performance in passive bistatic radar by exploiting a frequency agile (FA) signal is investigated, namely frequency agile signal-based passive bistatic radar (FAPBR) coherent integration. Since the carrier frequency of each pulse signal is agile, FAPBR coherent integration suffers from the problems of random range and Doppler phase fluctuations. To tackle these challenges, a novel FA signal coherent integration target detection scheme for PBR is proposed. In particular, the phase quadratic difference principle is presented for eliminating Doppler phase hopping. Then, frequency rearrangement is adopted to compensate for random range phase fluctuation while obtaining the high-range-resolution profiles (HRRPs) of the detecting target. Further, we innovatively present a sliding-range ambiguity decoupling (S-RAD) method to remove the range ambiguity effect in the case of the high pulse repetition frequency (HPRF). Compared with the existing methods, the proposed method can effectively mitigate Doppler phase hopping without requiring prior target velocity information, offering improved coherent integration performance in frequency agile signals with reduced computational complexity. Moreover, it successfully corrects the range ambiguity issue caused by HPRF. Finally, a series of simulation results are presented to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The bistatic Interferometric Synthetic Aperture Radar (InSAR) system can overcome the physical limitations imposed by the baseline of monostatic dual-antenna InSAR. It provides greater flexibility and can enhance elevation measurement accuracy through a well-designed baseline configuration. Unmanned aerial vehicles (UAVs) equipped with bistatic InSAR, having relatively low cost and high flexibility, are useful for mapping and land resource exploration. However, due to challenges including spatiotemporal synchronization and motion errors, there are limited reports on UAV-borne bistatic InSAR. This paper proposes a comprehensive method for processing data from small UAV-borne bistatic InSAR by integrating two-way synchronization chain signals. The proposed method includes compensation for time and phase synchronization errors, trajectory refinement with synchronized chain and Position and Orientation System (POS) data, high-precision bistatic InSAR imaging, and interferometric processing. Height inversion results based on the proposed method are also provided, which demonstrate the effectiveness of the proposed method in improving the accuracy of interferometric measurement at calibration points from 0.66 m to 0.42 m. [ABSTRACT FROM AUTHOR]

The purpose of this study is to analyze the applicability conditions for the significant wave height (SWH) measurement approach based on measuring the cross-correlation function of two signals with similar frequencies reflected by the sea surface in the bistatic problem statement (the transmitting antenna and the receiving antenna are separated in space). When implementing this approach, the modulus of the normalized cross-correlation function for several pairs of signals with different frequency bases will be measured in the experiment. The advantage of this approach over the traditional method for radar altimetry, based on the analysis of the shape of the reflected pulse, is the high accuracy in measuring the SWH for weak waves. In the bistatic formulation of the problem, an important advantage of the approach under study is the possibility of obtaining analytical formulas for solving the direct problem. This paper presents the derivation of a formula for the modulus of the normalized cross-correlation function of reflected signals, which expresses an explicit relationship with the parameters of sea waves and the measurement geometry in the bistatic formulation of the problem. This paper considers the influence on the modulus of the normalized cross-correlation function of the antenna patterns of the transmitting and receiving antennas, the distances to the sea surface, the wave slope variances, the SWH and the frequency base of the transmitted signals. The optimal variants of the measurement scheme are discussed. The results and conclusions obtained can be easily expanded to underwater acoustic sounding. [ABSTRACT FROM AUTHOR]

Ground-based radar has been used for Moon imaging for more than 60 years. Five-hundred-meter Aperture Spherical radio Telescope (FAST), as the largest radio telescope on Earth, holds significant potential for celestial imaging missions with its exceptional sensitivity. A bistatic Synthetic Aperture Radar (SAR) Moon imaging model that incorporates FAST and other transmitting radars is presented. The objective of this paper is to design the imaging parameters of this bistatic configuration based on the required resolution, and to estimate the resolution performance based on a given bistatic system capability. Considering the ultra-far range and the ultra-long observation time between the radars and the Moon, the geometric relationship involved in this bistatic configuration is significantly distinct from the bistatic configuration of airborne and spaceborne radars. Therefore, this paper accurately derives the two-dimensional resolution on the Moon's surface. First of all, the models of the Earth's surface and the Moon's surface, and the celestial motion of the Earth and Moon are established using WGS-84 and JPL-DE421, given by STK. Secondly, the bistatic range history within the observation time is calculated in terms of continuous celestial motion instead of the popular 'stop-and-go' assumption. Thirdly, no approximation is used in the resolution derivation process, and, in addition to the two-dimensional resolutions, the incident angle and the included angle are also given to describe the imaging performance. This method can also be extended to other bistatic-station and single-station celestial imaging, providing support for radar location and parameters design, for observation time span selection, for observation area selection, and for imaging performance estimation. The echo generation and imaging for point targets set on the Moon are shown. The simulation results prove the validity and accuracy of the proposed method in the paper. [ABSTRACT FROM AUTHOR]

The non-stationary clutter of space-based early warning radar (SBEWR) is more serious than that of airborne early warning radar. This phenomenon is primarily attributed to the Earth's rotation and range ambiguity. The increase in clutter degrees of freedom (DOFs) and the significant widening of the clutter suppression notch are not conducive to moving target detection near main lobe clutter. This paper proposes an effective approach to suppress non-stationary clutter based on an interpulse multi-frequency mode for SBEWR. Using the orthogonality of the uniform stepping frequency signal, partial range ambiguity can be effectively suppressed, and the clutter DOFs will be reduced. Subsequently, joint pitch-azimuth-Doppler three-dimensional spacetime adaptive processing and slant range preprocessing are used to perform clutter suppression. This combination not only curtails the estimation error associated with the clutter covariance matrix but also enhances the overall detection capabilities of the system. The simulation results verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper deals with adaptive moving target detection for a forward scatter radar in complex Gaussian noise. The echoes received by the forward scatter radar include not only the noise and the possible target signals but also the direct signals. To suppress the direct signals and detect the target signals, Rao and Wald tests are derived in two cases: the secondary data which contain no target signal are available or not available. Different from monostatic radar, it is proved that the derived Rao and Wald detectors for the forward scatter radar have the same test statistics as the generalized likelihood ratio test-based detector in the complex Gaussian noise both when the secondary data are available or not available. The numerical evaluation further demonstrates the equivalence and the effectiveness of the proposed detectors. [ABSTRACT FROM AUTHOR]

In this work, we present the preliminary results of radar observations of Near-Earth Objects (NEOs) carried out by European radio telescopes in the framework of the European Space Agency (ESA) project "NEO observation concepts for radar systems", aimed at deriving the functional requirements of a planetary radar system, evaluating the available European assets to perform NEO radar observations, and carrying out test radar campaigns. In the first part of the project, we executed the performance analysis of a possible European planetary radar system. Instrumental features, as much as issues like the impact of weather conditions on signal propagation at different radio frequencies, were considered. This paper focused on the test campaigns, performed in the years 2021–2022 in collaboration with the Jet Propulsion Laboratory (JPL), which led to the observation of several asteroids including 2021 AF8, (4660) Nereus, and 2005 LW3, which allowed us to derive astrometric measurements, as well as to measure physical properties, such as rotation periods, and observe how one of the targets is actually a binary asteroid. The obtained results demonstrated that European radio astronomical dishes, although employed only as receivers (in bistatic or multistatic configurations) and for a limited amount of time, are able to provide a significant contribution to the constitution of a European network to increase the opportunities for NEO monitoring and studies, if a transmitting antenna—equipped with a suitable high-power transmitter—were made available. [ABSTRACT FROM AUTHOR]

At the present time, most of existing security systems only detect and track targets in line-of-sight (LOS). However, in practice, the locations of targets are often out of the line of sight. This article focuses on the non-line-of-sight (NLOS) moving target detection with low-power transmission signals by reflection. There are two key problems, the weak target echo signal and the multipath effect. In terms of the issues, this paper constructs the echo signal model of the NLOS target. On the basis of the echo model, the detection method of NLOS moving target based on millimeter-wave radar comes up, which is of great theoretical value and important practical significance for indoor security. This paper innovatively applies the polynomial fitting method to suppress static noise and range gating method to suppress noise from other range gates. Then, the location and velocity of the target are estimated by two-dimensional fast Fourier transform (FFT) and the multiple signal classification (MUSIC) method. Furthermore, in order to verify the accuracy of the NLOS target echo signal model proposed in this paper, we respectively simulated two important parts of the signal in the model, the target echo signal and the direct backscattered signal of the intermediate interface, both of which are multipath signals. We counted the echo path length distribution in these two parts, and applied the NLOS target detection method to process them respectively. In addition, we also simulated the NLOS target echo signal and obtained actual data in the actual scene, and processed both the simulated data and the actual data. Comparing the results of target detection with and without denoising methods, the effectiveness of the two denoising methods proposed in this paper is verified. [ABSTRACT FROM AUTHOR]

Navigation satellite signals have the advantages of all-day, all-weather, and global coverage, and the use of navigation signals for the detection of moving targets has significant application prospects. However, the GNSS signal is very weak, and the signal power is greatly attenuated after being scattered by the target. In order to detect the echo signal, a long integration time is required. However, the movement of the target will cause the echo signal to produce unpredictable range migration and a Doppler frequency shift, which will weaken the cumulative effect of long-term integration. This paper proposes a weak signal processing method with amplitude and phase self-correction for moving target detection in the GNSS-S radar. First, the phase consistency of the echoes of a single GNSS satellite is realized by the block expansion compression and phase differential correction method to improve the coherent accumulation gain; then, multi-star joint accumulation is carried out after the signal amplitudes of multiple satellites are corrected by the improved keystone method, so as to obtain a stable echo signal track. This method can effectively improve the integral gain of the scattering signal of the moving target and realize target detection. The simulation results and field tests show that this method can effectively improve the SNR of the GNSS-S signal and can realize the detection of small moving targets such as cars with GNSS-S radar. [ABSTRACT FROM AUTHOR]

In this paper, a feedback training approach for efficiently dealing with distribution shift in synthetic aperture radar target detection using a Bayesian convolutional neural network is proposed. After training the network on in-distribution data, it is tested on out-of-distribution data. Samples that are classified incorrectly with high certainty are fed back for a second round of training. This results in the reduction of false positives in the out-of-distribution dataset. False positive target detections challenge human attention, sensor resource management, and mission engagement. In these types of applications, a reduction in false positives thus often takes precedence over target detection and classification performance. The classifier is used to discriminate the targets from the clutter and to classify the target type in a single step as opposed to the traditional approach of having a sequential chain of functions for target detection and localisation before the machine learning algorithm. Another aspect of automated synthetic aperture radar detection and recognition problems addressed here is the fact that human users of the output of traditional classification systems are presented with decisions made by "black box" algorithms. Consequently, the decisions are not explainable, even to an expert in the sensor domain. This paper makes use of the concept of explainable artificial intelligence via uncertainty heat maps that are overlaid onto synthetic aperture radar imagery to furnish the user with additional information about classification decisions. These uncertainty heat maps facilitate trust in the machine learning algorithm and are derived from the uncertainty estimates of the classifications from the Bayesian convolutional neural network. These uncertainty overlays further enhance the users' ability to interpret the reasons why certain decisions were made by the algorithm. Further, it is demonstrated that feeding back the high-certainty, incorrectly classified out-of-distribution data results in an average improvement in detection performance and a reduction in uncertainty for all synthetic aperture radar images processed. Compared to the baseline method, an improvement in recall of 11.8%, and a reduction in the false positive rate of 7.08% were demonstrated using the Feedback-assisted Bayesian Convolutional Neural Network or FaBCNN. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of covariance matrix estimation for radar adaptive detection under non-Gaussian clutter. Traditional model-based estimators may suffer from performance loss due to the mismatch between real data and assumed models. Therefore, we resort to a data-driven deep-learning method and propose a covariance matrix estimation method based on a complex-valued convolutional neural network (CV-CNN). Moreover, a real-valued (RV) network with the same framework as the proposed CV network is also constructed to serve as a natural competitor. The obtained clutter covariance matrix estimation based on the network is applied to the adaptive normalized matched filter (ANMF) detector for performance assessment. The detection results via both simulated and real sea clutter illustrate that the estimator based on CV-CNN outperforms other traditional model-based estimators as well as its RV competitor in terms of probability of detection (PD). [ABSTRACT FROM AUTHOR]

Multistatic airborne SAR (MuA-SAR) benefits from the ability to flexibly adjust the positions of multiple transmitters and receivers in space, which can shorten the synthetic aperture time to achieve the required resolution. To ensure both imaging efficiency and quality of different system spatial configurations and trajectories, the fast factorized back projection (FFBP) algorithm is proposed. However, if the FFBP algorithm based on polar coordinates is directly applied to the MuA-SAR system, the interpolation in the recursive fusion process will bring the problem of redundant calculations and error accumulation, leading to a sharp decrease in imaging efficiency and quality. In this paper, a unified Cartesian fast factorized back projection (UCFFBP) algorithm with a multi-level regional attention strategy is proposed for MuA-SAR fast imaging. First, a global Cartesian coordinate system (GCCS) is established. Through designing the rotation mapping matrix and phase compensation factor, data from different bistatic radar pairs can be processed coherently and efficiently. In addition, a multi-level regional attention strategy based on maximally stable extremal regions (MSER) is proposed. In the recursive fusion process, only the suspected target regions are paid more attention and segmented for coherent fusion at each fusion level, which further improves efficiency. The proposed UCFFBP algorithm ensures both the quality and efficiency of MuA-SAR imaging. Simulation experiments verified the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a new two-stage approach for suppressing strong clutter and detecting moving targets using scatterers' spatial structure and signal similarity. Compared with the traditional strong clutter suppression methods, the proposed method considers both the spatial similarity and the channel correlation of the scatterers, effectively alleviating the false alarm probability and avoiding the missed detection problem caused via identifying strong moving targets as strong stationary clutter. Additionally, a detector is presented based on the linear degree of the radial velocity interferometric phase (LDRVP) to eliminate false alarms from isolated strong scatter points and the edges of strong scatterers. The experimental results of the X-band radar indicate the presented approach's lower false alarm probability and superior robustness. [ABSTRACT FROM AUTHOR]

The GNSS-S radar utilizes the signals of a global navigation satellite system (GNSS) to carry out target detection. Due to the very low power of GNSS signals, long-term accumulation is needed to improve the gain of the echo signals. However, when it is used for moving object detection, the random movement of the target will cause residual Doppler frequency after the echoes are correlated and compressed through the direct signal. The residual Doppler frequency will cause two problems: on the one hand, the signal coherence will deteriorate, affecting the coherent accumulation gain; on the other hand, the amplitude of the signal after compression will decrease due to the sensitivity of GNSS signals to Doppler frequency. Therefore, how to increase the signal amplitude and eliminate the phase fluctuation caused by the Doppler frequency shift in the GNSS echoes of moving targets is an important issue for GNSS-S radar to detect moving targets. This paper proposes a dual-frequency GNSS echo enhancement method that uses the dual-frequency signals transmitted by the GNSS satellites to enhance and regularize the target echo. First, the phase relationship model of the GNSS dual-frequency echo is constructed, and the phase difference is made to the compressed dual-frequency echo signal to obtain the differential phase without fluctuation; then, the amplitudes of the dual-frequency echo signals are added together; and finally, a new signal with enhanced amplitude and consistent phase is constructed by using the dual-frequency additive amplitude and differential phase, and the long-term coherent accumulation of the signal is carried out, which can improve the processing gain of the weak echo signal of the moving target. The simulation and field experiments show that this method makes full use of the energy of the GNSS dual-frequency signal and eliminates the phase fluctuation in the echo signal of the moving target so that the compressed signal energy remains consistent in the slow-time dimension. After long-term coherent accumulation, the echo SNR was greatly improved, which enabled the detection of two high-speed cars by GNSS-S radar in the experiment. [ABSTRACT FROM AUTHOR]

Passive bistatic radar (PBR)-based moving target detection (MTD) has benefited greatly from multi-frequency (MF) integration, which can effectively improve the detection capability of weak targets. However, with the increase in the coherent processing interval (CPI) and carrier-frequency separation, Doppler spread will appear in the range-Doppler maps (RDMs) over different frequency bands, which severely limits the processing gain of MF integration. In this paper, a novel MTD algorithm is proposed to achieve both long-time integration and quasi-coherent MF integration. More specifically, the proposed method consists of two main steps, where a modified Radon–Fourier transform (RFT), termed as MF-based RFT (MF-RFT), is, firstly, used to eliminate the Doppler spread via designing a sequential of MF-based Doppler filter banks. Following the MF-RFT, a phase-compensation-based method is also developed to further remove the residual phase errors. This method involves formulating an optimization problem based on the minimum-entropy criterion and employing a particle swarm optimization (PSO) algorithm to solve it, after which quasi-coherent MF integration can be achieved with robustness. Both numerical results and field test results based on digital video broadcasting-satellite (DVB-S) signals demonstrate that the proposed algorithm outperforms the existing methods in the scenario of weak MTD. [ABSTRACT FROM AUTHOR]

Moving target detection plays a pivotal role in many applications, especially in radar systems. In addition, time-reversal (TR) technology can improve radar detection probability by effectively solving the problem of multipath signals. However, detection performance can be affected when clutter exists and target Doppler shifts are unknown. For this reason, we propose a novel moving target detection algorithm that leverages TR technology and a novel waveform optimization method in clutter and multipath signal environments. In this paper, we establish a TR signal model for a moving target in such environments and derive the TR average likelihood ratio test (TR-ALRT) detector. Additionally, we introduce a waveform optimization method that adapts to clutter and unknown Doppler information further to enhance the performance of the TR-ALRT detector. Consequently, a TR-ALRT waveform optimization (TR-ALRT-WO) detector is derived to adapt to the unknown Doppler and environment information, thereby improving the detection probability. Comparative analysis with other detectors under the same conditions validates the effectiveness of our proposed TR-ALRT-WO detector. Furthermore, numerical experiments demonstrate the superior performance of the proposed detection algorithm. [ABSTRACT FROM AUTHOR]

High-frequency surface wave radar (HFSWR) is an important marine monitoring technology, and this new regime of radar plays an important role in large-scale, continuous early-warning monitoring at sea. In particular, shipborne HFSWR has wider applications in detecting interesting sea areas, with the advantages of flexible deployment and extended detection capability. Due to the large amount of sea clutter accompanying the echo signals of shipborne HFSWR and the spread of sea clutter due to platform motion, the detection of targets in clutter regions is extremely difficult. In this paper, a multi-frame time-frequency (TF) analysis–based target-detection method is proposed. First, the sea clutter spreading area in the HFSWR echo signal is modeled, and the effects of platform motion and currents on the sea clutter spread are analyzed to determine the sea clutter coverage area; this paper focuses on frequency modeling. Then the TF image (TFI) of each range cell is obtained by TF analysis of the cells within a certain range of the echo signal, and the range cells of possible target points are determined by binary classification of the TFI through a convolutional neural network. Finally, the location of the final target point is obtained by correlation of multi-frame TFIs. Shipborne HFSWR field experiments show that the proposed detection method performs well in detecting targets concealed by sea clutter. [ABSTRACT FROM AUTHOR]

When the transmitter is in motion, the airborne passive bistatic radar (PBR) has a complex clutter geometry and lacks independent and identically distributed training samples in clutter estimation and suppression. In order to solve these problems, this paper proposes a space–time adaptive processing (STAP) algorithm based on root off-grid sparse Bayesian learning. The proposed algorithm first models the space–time base of the dictionary as an adjustable state. Then, the positions of those dynamic bases are optimized by iterating a maximum expectation algorithm. In this way, the off-grid error in clutter estimation can be eliminated even when the modeling grid is wide. To further improve the accuracy of clutter estimation, the proposed algorithm eliminates the error caused by samples with singular values in the root off-grid sparse Bayes learning by artificially adding pseudorandom noise and using hypothesis testing. The simulation results show that the proposed algorithm achieves better performance than the existing algorithms. [ABSTRACT FROM AUTHOR]

Hitchhiking bistatic radar system takes the direct wave signal that is transmitted by the non-cooperative radar emitter as the reference to detect and analyze the target echo signal, so as to realize the positioning and tracking of the target. This radar system has the advantages of low cost and strong survivability. Aiming at the problem of passive radar to covert the detection of maritime targets, this paper develops a hitchhiking bistatic radar system for maritime target detection, which uses the shore-based radar as the non-cooperative radar emitter. By continuously collecting the direct wave and target echo data of the non-cooperative radar, the direct wave reference signal reconstruction, pulse compression, interference suppression and synchronization processing, non-coherent integration, MTI (moving target indication), clutter map processing, and adaptive CFAR (constant false alarm rate) detection are completed to obtain the azimuth, bistatic range, and Doppler frequency of the target, and finally realize the positioning of non-cooperative maritime targets. This paper first introduces and demonstrates the composition principle of the system, introduces the signal processing implementation method of the system in detail, and tests and analyzes the key algorithms. The experimental results show that the system can realize the passive coherent detection of maritime moving targets and locate multiple targets at the same time. The experiment obtains a very clear PPI (plane position indicator) display picture of the hitchhiking bistatic radar system, and the radar detection data of the experimental system is in good agreement with the AIS (automatic identification system) data. [ABSTRACT FROM AUTHOR]

In this paper, in order to reduce the probability of the radar waveform intercepted by the passive detection system, the time-bandwidth product of the radar waveform is increased, and the detection probability of the radar waveform to the target is improved. This paper tackles the holographic RF stealth radar and proposes a joint coding waveform based on the linear frequency modulation (LFM) waveform. Joint coding uses complementary codes to perform phase-coding, and combines the codewords optimized by genetic algorithm in order to perform discrete frequency-coding waveform. The joint coding waveform model is theoretically analyzed, and the ambiguity function, pulse compression and target detection probability of the joint coding waveform are obtained by numerical simulation. In addition, the complexity of the algorithm and the low probability of intercept (LPI) characteristic of the joint coding waveform are analyzed. The results show that the joint coding waveform has an approximate "pushpin" ambiguity function, ultra-low sidelobe characteristics, better RF stealth and target detection performance. Finally, it has good application prospects in the current battlefield environment. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of the reference signal being contaminated by target echoes due to factors such as wide beamwidths, beam pointing errors, or site errors in passive bistatic radar (PBR). Such contamination can lead to reference signal impurity and subsequently degrade the performance of traditional object detection methods. To overcome this challenge, a novel target detection method centered around the reference signal's purification is presented. The proposed method involves the modeling, analysis, localization, and purification of the impure reference signal. First, a new signal model incorporating the impure reference signal is established, followed by a comprehensive analysis of its impact on target detection. Then, leveraging the valuable information hidden in the impure reference signal, target echoes within the impure reference signal are localized and reconstructed. Finally, the reconstructed target echoes are subtracted from the original impure reference signal to purify the reference signal. Based on the purified reference signal, traditional target detection is performed to detect the expected targets. The core advantage of this method lies in its efficient reconstruction and removal of the impure terms within the reference signal. The effectiveness of the proposed method is demonstrated through a series of simulation results. [ABSTRACT FROM AUTHOR]

Compared with traditional active detection radar, non-cooperative bistatic radar has a series of advantages, such as a low cost and low detectability. However, in real-life scenarios, it is limited by the non-cooperation of the radiation source and the bistatic geometric model, resulting in a low target signal-to-noise ratio (SNR) and unstable detection between frames in the radar scanning cycle. The traditional detect-before-track (DBT) method fails to exploit adequately the target information and is incapable of achieving consistent and effective tracking. Therefore, in this paper, we propose a two-stage track-before-detect (TBD) method based on deep learning. This method employs a low-threshold detection network to identify the target initially, followed by utilizing the model method to ascertain potential tracks. Subsequently, a diverse range of network structures are employed to extract and integrate position information, innovation score, and target structural information from the track in order to obtain the target track. Experimental results demonstrate the method's ability to achieve multi-target tracking in highly cluttered environments, where the higher the number of frames processed, the better the target tracking effect. Moreover, the method exhibits real-time processing capabilities. Hence, this method provides an effective solution for target tracking in non-cooperative bistatic radar systems. [ABSTRACT FROM AUTHOR]

The conventional method of centralizing information fusion is commonly employed for sorting radar signals in reconnaissance receivers. However, challenges arise when the distance between reconnaissance receivers and the fusion center is distant, or when the fusion center is compromised by hostile forces. To address these issues, this paper proposes a novel distributed information fusion method. In this method, each reconnaissance receiver is restricted to accessing adjacent nodes within an undirected graph for information transmission and local computation. The distributed Dempster's combination rule and the cautious conjunctive rule are implemented using weight functions and consensus algorithms. Furthermore, an innovative outlier detection algorithm is incorporated into the fusion process to enhance its robustness. Experimental results demonstrate that the proposed method effectively improves the accuracy of radar signal sorting. When the sorting accuracy of a single reconnaissance receiver is equal to or higher than 60%, both fusion rules achieve a sorting accuracy of 100%. Even when the sorting accuracy of a single reconnaissance receiver is as low as 50%, the fused result still maintains a sorting accuracy of over 97%. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System Reflectometry (GNSS-R) is an emerging earth observation method for remote sensing of feature parameters using reflected signals from navigation satellites, and is a purely specular bistatic forward scattering observation means with special right-handed circular polarization incident wave. In this paper, the small-slope approximation model of non-Gaussian sea surface is used as the basis to construct the scattering model for the observation geometry of GNSS-R as well as the L-band characteristics, and the fully-polarization normalized bistatic radar scattering cross section (NBRCS) are simulated by the method of polarization synthesis to analyze the scattering characteristics under different wind speeds and directions on the ocean surface, which highlights the variation of NBRCS with wind direction, and the scattering modeling accuracy is improved by comparing with the data of CYGNSS. In addition, we adopt the observation geometry deviating from purely specular geometry, discuss the scattering azimuth angle, scattering influence, and the relative relationship between different polarizations of the scattering angle under the non-specular geometry. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) has been extensively utilized in deep-space exploration. However, GPR modeling commonly employs simplified antenna models and carrier-free impulse signals, resulting in reduced accuracy and interpretability. In this paper, we addressed these limitations by combining a tilted monopole antenna and linear frequency modulation continuous wave (LFMCW) to simulate real conditions. Additionally, a radiation-pattern-compensation back-propagation (RPC-BP) algorithm was developed to improve the illumination of the right-inclined structure. We first introduced the LFMCW used by the Mars Rover Penetrating Radar (RoPeR) onboard the Zhurong rover, where frequencies range from 15 to 95 MHz. Although the LFMCW signal improves radiation efficiency, it increases data processing complexity. Then, the radiation patterns and response of the tilted monopole antenna were analyzed, where the radiated signal amplitude varies with frequency. Finally, a series of numerical and laboratory experiments were conducted to interpret the real RoPeR data. The results indicate that hyperbolic echoes tilt in the opposite direction of the survey direction. This study demonstrates that forward modeling considering real transmit signals and complex antenna models can improve modeling accuracy and prevent misleading interpretations on deep-space exploration missions. Moreover, the migration process can improve imaging quality by considering radiation pattern compensation. [ABSTRACT FROM AUTHOR]

This paper deals with the Dual-Function Radar and Communication (DFRC) signal design for a Multiple-Input–Multiple-Output (MIMO) system, considering the presence of signal-dependent clutter. A modulation methodology called Spectral Position Index and Amplitude (SPIA) modulation is proposed, which involves selecting passband and stopband positions and applying amplitude modulation. Signal to Interference plus Noise Ratio (SINR) is maximized to enhance radar detectability. Meanwhile, variable modulus and communication modulation constraints are enforced to ensure compatibility with the current hardware techniques and communication demand, respectively. In addition, the mainlobe width and sidelobe level constraints used to concentrate energy in a specific area of space are enforced. To tackle the resulting nonconvex and NP-hard optimization problem, an Iterative Block Enhancement (IBE) framework that alternately updates each signal in each emitting antenna is exploited to monotonically increase SINR. Each block involves the Dinkelbach's Iterative Procedure (DIP), Sequential Convex Approximation (SCA) and Alternating Direction Method of Multipliers (ADMM) to obtain a single signal. The computational complexity and convergence of the algorithm are analyzed. Finally, the numerical results highlight the effectiveness of the proposed dual-function scheme in sidelobe signal-dependent clutter. [ABSTRACT FROM AUTHOR]

Radar target echoes undergo fading in the presence of specular reflection, which is adverse to radar detection. To address this problem, this paper proposes a radar detection method that uses a single transmitting antenna and three receiving antennas. The proposed method uses the maximum absolute value of the difference in the radar received signal power among the three receiving antennas as the test statistic. First, the target echo in the presence of specular reflection is analyzed. Then, selection of the required number of radar antennas and the heights at which they must be situated are discussed. Subsequently, analytical expressions of the radar detection probability and the false alarm probability are derived. Finally, simulation results are presented, which show that the proposed method improves radar detection performance in the presence of specular reflection. [ABSTRACT FROM AUTHOR]

The new mode of Circular Synthetic Aperture Radar (CSAR) has several advantages including multi-aspect and long-time observation, which can generate high-frame-rate image sequences to detect moving targets with a single-channel system. Nonetheless, due to CSAR being sensitive to 3D structures, static high targets are observed in scene display rotational motion within CSAR subaperture image sequences. Such motion can cause false alarms rising when utilizing image sequence-based moving target detection methods like logarithm background subtraction (LBS). To address this issue, this paper first thoroughly analyzes the moving target and static high target's difference for the trajectory in an image sequence. Two new trajectory features of the rotation angle and moving distance are proposed to differentiate them. Based on the features, a new false alarm suppression method is proposed. The method first utilizes LBS to obtain coarse binary detection results comprising both moving and static high targets, then employs morphological filtering to eliminate noise. Next, DBSCAN and target tracking steps are employed to extract the trajectory features of the target and false alarm. Finally, false alarms are suppressed with trajectory-based feature discriminators to output detection results. The W-band CSAR open dataset is used to validate the proposed method's effectiveness. [ABSTRACT FROM AUTHOR]

Inverse Synthetic Aperture Radar (ISAR) is a promising technique for air target imaging and recognition. However, the traditional monostatic ISAR only can provide partial features of the observed target, which is a challenge for high-accuracy recognition. In this paper, to improve the recognition accuracy of air targets, we propose a novel recognition network based on multi-view ISAR imaging and fusion, called Multi-View Fusion Recognition network (MVFRnet). The main structure of MVFRnet consists of two components, the image fusion module and the target recognition module. The fusion module is used for multi-view ISAR data and image preprocessing and mainly performs imaging spatial match, image registration, and weighted fusion. The recognition network consists of the Skip Connect Unit and the Gated Channel Transformation (GCT) attention module, where the Skip Connect Unit ensures the extraction of global depth features of the image and the attention module enhances the perception of shallow contour features of the image. In addition, MVFRnet has a strong perception of image details and suppresses the effect of noise. Finally, simulated and real data are used to verify the effectiveness of the proposed scheme. Multi-view ISAR echoes of six types of aircraft are produced by electromagnetic simulation software. In addition, we also build a millimeter wave ground-based bistatic ISAR experiment system and collect multi-view data from an aircraft model. The simulation and experiment results demonstrate that the proposed scheme can obtain a higher recognition accuracy compared to other state-of-the-art methods. The recognition accuracy can be improved by approximately 30% compared with traditional monostatic recognition. [ABSTRACT FROM AUTHOR]

This paper presents a target tracking algorithm based on 4D millimeter-wave radar point cloud information for autonomous driving applications, which addresses the limitations of traditional 2 + 1D radar systems by using higher resolution target point cloud information that enables more accurate motion state estimation and target contour information. The proposed algorithm includes several steps, starting with the estimation of the ego vehicle's velocity information using the radial velocity information of the millimeter-wave radar point cloud. Different clustering suggestions are then obtained using a density-based clustering method, and correlation regions of the targets are obtained based on these clustering suggestions. The binary Bayesian filtering method is then used to determine whether the targets are dynamic or static targets based on their distribution characteristics. For dynamic targets, Kalman filtering is used to estimate and update the state of the target using trajectory and velocity information, while for static targets, the rolling ball method is used to estimate and update the shape contour boundary of the target. Unassociated measurements are estimated for the contour and initialized for the trajectory, and unassociated trajectory targets are selectively retained and deleted. The effectiveness of the proposed method is verified using real data. Overall, the proposed target tracking algorithm based on 4D millimeter-wave radar point cloud information has the potential to improve the accuracy and reliability of target tracking in autonomous driving applications, providing more comprehensive motion state and target contour information for better decision making. [ABSTRACT FROM AUTHOR]

Distributed radar systems promise to significantly enhance target localization by virtue of the superiority of multi-view observations from widely separated radars, compared to their monostatic counterparts. Nevertheless, when the radar number is limited, performing target localization bears the brunt of the parameter identifiability requirement that the parameter number must be no less than the number of independent measurements. In this way, the canonical two-stage target localization method, as well as its developments, is no longer appropriate for direct application. Hence, in this paper, we propose a novel target localization method using time-difference-of-arrival (TDOA) measurements with the minimum number of radars under platform position uncertainties. The referred distributed system is a bistatic multi-receiver system, where the primary signal is transmitted by a geostationary Earth orbit (GEO) satellite while receivers are equipped on several unmanned aerial vehicles (UAVs). In the first stage, the reference range from the reference radar to the target is estimated by a quadratic function, and then the weighted least squares (WLS) solution of the target location is updated by substituting the range estimate back into it. In the second stage, we invoke the Taylor series approximation to further refine the target localization obtained by the first stage. It can be foreseen that the developed method is beneficial for scenarios with a limited number of radars, including engineering projects such as fire control, surveillance, and guidance, to support high-accuracy target localization. The simulation results show the superiority of the localization performance of the proposed method over other existing methods. [ABSTRACT FROM AUTHOR]

Space-time adaptive processing (STAP) is an important method of clutter suppression that requires adequate training samples. For an airborne conformal array radar, conventional STAP methods do not have enough training samples to acquire good performance due to the range dependent clutter caused by geometry and the problem of polarization. Sparse-recovery-based STAP (SR-STAP) methods have garnered significant attention in the past few decades because they only require a small number of training samples. Sparse Bayesian Learning (SBL) methods have seen increasing amounts of development due to its robust, self-regularizing nature and because it is not sensitive to user parameters, but it converges slowly. In this paper, a novel fast SBL (NFSBL) method is put forward to increase the rate of convergence. To minimize the SBL penalty function, the proposed method introduces the conjugate function to construct a surrogate function. Additional solution sparsity will be achieved through iteratively minimizing the surrogate function. Then, from the proposed method, we could obtain a more accurate clutter plus noise covariance matrix. Numerical simulation results express that this method could acquire better performance of STAP and improvement in convergence and computational complexity for a conformal array. [ABSTRACT FROM AUTHOR]

High frequency radar has a wide monitoring range and low range resolution. It may contain multiple targets or outlier interference phenomena in different clutter regions of the range–Doppler (RD) spectrum in detected background. The key to the performance of target detection in multi target backgrounds is the ability to determine the attributes of targets or outliers. Our previous research shows that the number of targets belongs to an absolute minority compared to the number of background units. In this paper, we propose a new method for multi-target detection building on the ordered statistics constant false alarm detector (OS-CFAR). The new method fully utilizes the sparse characteristics of the target and uses the idea of introducing regularization processing to eliminate interfering targets, and obtain an estimate of shape parameters for target detection. To further improve the performance of the algorithm, a correction method is proposed for the inaccurate selection of the k value. Upon estimating the distribution parameters, the detection threshold is calculated, and the target's constant false alarm detection is completed. Simulation and measured data show that our algorithm can effectively counter the interference of multiple targets and maintain a constant false alarm characteristic under different conditions, providing a reliable target detection method. [ABSTRACT FROM AUTHOR]

Radar emitter identification (REI) aims to extract the fingerprint of an emitter and determine the individual to which it belongs. Although many methods have used deep neural networks (DNNs) for an end-to-end REI, most of them only focus on a single view of signals, such as spectrogram, bi-spectrum, signal waveforms, and so on. When the electromagnetic environment varies, the performance of DNN will be significantly degraded. In this paper, a multi-view adaptive fusion network (MAFN) is proposed by simultaneously exploring the signal waveform and ambiguity function (AF). First, the original waveform and ambiguity function of the radar signals are used separately for feature extraction. Then, a multi-scale feature-level fusion module is constructed for the fusion of multi-view features from waveforms and AF, via the Atrous Spatial Pyramid Pooling (ASPP) structure. Next, the class probability is modeled as Dirichlet distribution to perform adaptive decision-level fusion via evidence theory. Extensive experiments are conducted on two datasets, and the results show that the proposed MAFN can achieve accurate classification of radar emitters and is more robust than its counterparts. [ABSTRACT FROM AUTHOR]

The traditional power allocation method for multi-target localization adopts the Robust Chance Constrained Power Allocation Scheme (RCC-PA), which does not consider the strong clutter characteristics in the current radar detection environment. However, how to reasonably allocate the power required for the radar to locate the target in the strong clutter is a fundamental challenge for improving the combat capability of the radar. Since Simultaneous Multi-beam Digital Array Radar (SM-DAR) can provide high-resolution information on targets in strong clutter, this paper extends the RCC-PA scheme to strong clutter and introduce an extended target model suitable for high-resolution SM-DAR. At the same time, the Gamma distribution is used to reflect the statistical characteristics of the Radar Cross Section (RCS) so that the extended RCC-PA scheme (ERCC-PA) can be suitable for all the scatterers whose RCS belong to Gamma distribution families. In the ERCC-PA scheme, the Strong Clutter Information Reduction Factor (SCIRF) of the extended target is first derived. Then, a Gamma Chance-constraint Programming Model (Γ -CCP model) is constructed to optimize the power allocation for locating multiple extended targets in strong clutter. The extended dichotomy method for power allocation is also given. Theoretical analysis shows that the power of multi-target localization in strong clutter can still be reasonably allocated under the target with multi-measurement characteristics. In addition, the power distribution of multi-target localization in the strong clutter is negatively correlated with the shape parameter of the Gamma distribution. Specifically, the larger the shape parameter characterizes the larger individual strong scatterer, resulting in the SM-DAR requiring less positioning power. The experimental results verify the theoretical analysis and show that the ERCC-PA scheme can improve the utilization of power compared with the benchmark and has the advantage of robustness to the fluctuation of the target RCS. [ABSTRACT FROM AUTHOR]

This paper proposes a cooperated range ambiguous clutter suppression method for moving target detection in the background of range-ambiguous clutter with a phased array (PA)–frequency diverse array (FDA) dual-mode radar. With the FDA mode, the range-ambiguous clutters are discriminated in the transmit spatial frequency domain, and thus the clutter covariance matrixes (CCMs) corresponding to unambiguous and ambiguous regions can be independently estimated. Therefore, the enhanced CCM can be reconstructed by using a linear combination of these distinguished CCMs from different range regions. With the PA mode, the enhanced CCM is applied, thus taking advantages of its high beampattern gain as well as alleviating the range ambiguous clutter suppression problem. Simulation results are presented to verify the effectiveness of the proposed method in range-ambiguous clutter scenarios. [ABSTRACT FROM AUTHOR]

Traditional target tracking is carried out based on the point measurements extracted from the radar resolution cells. This is not suitable for situations of low signal-to-noise ratio (SNR). In this paper, we aim to investigate the problem of the joint detection and tracking (JDT) of a target by directly using the received signals of passive multi-static radar without feeding the signals to matched filters. To this end, a novel likelihood function is proposed exploiting the statistical properties of coherent processing between the reference and surveillance signals. With such a likelihood function, the particle Bernoulli filter is employed to perform direct JDT (DJDT) of the target. A remarkable feature of the proposed method is that it is able to achieve satisfactory performance when the SNR of received signals is low. Furthermore, the proposed method cannot only achieve the existence and kinematic state of the target, but also the time-varying SNR of each receiver, which serves as an important input for sensor adjustment. The performance of the proposed method is verified via simulations. [ABSTRACT FROM AUTHOR]

For the DOA (direction of arrival) estimation of a low-elevation-angle target under the influence of a multipath effect, this paper proposes a DOA estimation method based on CAE (convolutional autoencoder) and CNN (convolutional neural network). The algorithm firstly inputs the signal covariance matrix of the array of the low-elevation target containing direct and reflected waves into the convolutional autoencoder to realize the de-multipath, and uses the spatial features extracted by the convolutional autoencoder as the input of the extreme learning machine to realize the DOA preclassification of direct waves; based on the preclassification results, one branch of the three parallel convolutional neural nets is selected, and the output of the convolutional autoencoder is used as the input of this branch to realize DOA estimation. The simulation results show that the algorithm has better estimation accuracy and efficiency than the conventional algorithms, especially when the DOA of the target is in the lower range. The analysis of the simulation results shows that the algorithm is effective, in which the convolutional autoencoder can effectively realize the de-multipath, and the use of parallel convolutional neural networks can avoid overfitting and underfitting and realize DOA estimation more accurately. [ABSTRACT FROM AUTHOR]

Non-imaging radar can achieve the detection and localization of moving targets, but it faces difficulties in the classification of targets. Aiming for the application of moving target classification by low/medium resolution non-imaging radar, this paper proposes a novel feature extraction method, based on the statistical characteristics of echo amplitude with the L-band fully polarimetric radar. A feature plane, composed of the 3rd-order central moment (skewness) and 4th-order central moment (kurtosis) as the statistical characteristics of the echo envelope, is established. In addition, two types of moving targets, pedestrians and non-motorized vehicles, are able to be effectively distinguished according to whether the echo signals of different polarizations have overlapping areas in the feature plane. The L-band fully polarimetric radar has been developed and field experiments have been conducted. The experimental results verify that the kurtosis and skewness of the fully polarimetric echo amplitude of the pedestrians are very close, and there is an overlapping area in the feature plane, while the statistical characteristics of the fully polarimetric echo signal of non-motor vehicles are different, and there is no overlapping area in the feature plane. This proposed feature extraction method has the advantage of being simple and robust, compared to the traditional imaging approach. [ABSTRACT FROM AUTHOR]

This paper presents the first successful drone detection results using a 5G network as a source of illumination in a passive radar system. Furthermore, a novel adaptive strategy for signal integration is shown. The proposed approach is based on the Rényi entropy. It allows one to select time frames with a densely allocated downlink channel both in the time and frequency domains. The resource allocation is strongly related to a network load and has a crucial influence on 5G-based passive radar range resolution and detection capabilities. The proposed technique was validated using simulated and real-life signals, confirming the possibility of detecting unmanned aerial vehicles (UAVs) in 5G-network-based passive radars. Moreover, the proposed methodology can be directly used in passive radar systems where the illuminating signal duration and bandwidth are content-dependent, and the radar resolution may vary significantly. [ABSTRACT FROM AUTHOR]