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The wideband absorption and transmission of frequency-selective rasorber (FSR) remain a persistent challenge in the application of radar devices. In this article, a novel high performance wideband FSR design based on cross bend resonators was proposed. The FSR consists of an upper absorption lossy layer, which offers broad absorption and transmission bands, and a lower bandpass frequency-selective surface that enables a highly selective transmission of incident electromagnetic wave. Full wave simulation results showed that this novel design achieves an absorption bandwidth of 83.7% with more than 90% absorptivity in the frequency range of 5.2–12.7 GHz. Furthermore, the passband's fractional bandwidth for the insertion loss (IL) less than −3 dB is 33.9%, ranging from 14.9 to 21 GHz, with the minimum IL recorded at 0.69 dB at 17.7 GHz. To further verify the proposed method, a prototype FSR with 10 × 10 units of 120 mm × 120 mm was fabricated and the performance of the FSR was tested. The experiment results were in good agreement with the simulated results, and it showed a significant monostatic radar cross-section reduction in the frequency range of 5.3 GHz to 18.3 GHz compared with a metallic plane of the same size. [ABSTRACT FROM AUTHOR]

The satellite–ground bistatic configuration, which uses geosynchronous synthetic aperture radar (GEO SAR) for illumination and ground equipment for reception, can achieve wide coverage, high revisit, and continuous illumination of interest areas. Based on the analysis of the signal characteristics of GEO satellite–ground bistatic SAR (GEO SG-BiSAR), it is found that the bistatic echo signal has problems of azimuth spectrum aliasing and 2-D spatial variability. Therefore, to overcome those problems, a novel SAR imaging method for a GEO SG-BiSAR system with severe azimuth spectrum aliasing and 2-D spatial variability is proposed. Firstly, based on the geometric configuration of the GEO SG-BiSAR system, the time-domain and frequency-domain expressions of the signal are derived in detail. Secondly, in order to avoid the increasing cost caused by traditional multi-channel reception technology and the processing burden caused by inter-channel errors, the azimuth deramping is executed to solve the azimuth spectrum aliasing of the signal under the special geometric structure of GEO SG-BiSAR. Thirdly, based on the investigation of azimuth and range spatial variability characteristics of GEO SG-BiSAR in the Range Doppler (RD) domain, the azimuth spatial variability correction strategy is proposed. The signal corrected by the correction strategy has the same migration characteristics as monostatic radar. Therefore, the traditional chirp scaling function (CSF) is also modified to solve the range spatial variability of the signal. Finally, the two-dimensional spectrum of GEO SG-BiSAR with modified chirp scaling processing is derived, followed by the SPECAN operation to obtain the focused SAR image. Furthermore, the completed flowchart is also given to display the main composed parts for GEO SG-BiSAR imaging. Both azimuth spectrum aliasing and 2-D spatial variability are taken into account in the imaging method. The simulated data and the real data obtained by the Beidou navigation satellite are used to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Nowadays, it is still a great challenge to detect and locate indoor humans using a frequency-modulated continuous-wave radar accurately. Due to the interference of the indoor environment and complex objects such as green plants, the radar signal may penetrate, reflect, refract, and scatter, and the echo signals will contain noise, clutter, and multipath of different characteristics. Therefore, a method combined with comprehensive non-target signal removal and human localization is proposed to achieve position estimation of a human target. Time-variant clutter is innovatively mitigated through time accumulation using point clustering. Ghost targets are reduced according to propagation path matching. The experimental results show that the method can locate the real target human within an average error of 0.195 m in multiple complex environments with green plants, curtains, or furniture using a 77 GHz millimeter-wave radar. Meanwhile, the proposed method performs better than conventional methods. The detection probability is 81.250% when the human is behind a potted plant and is 90.286% when beside it. [ABSTRACT FROM AUTHOR]

Modern radar systems often face various interference signals in complex and rapidly changing electronic environments. The task of suppressing this interference in the radar echo signal to extract vital information is challenging. A radar interference suppression method is proposed based on a generative adversarial network (GAN). This method effectively recovers the target signal from the echo signal, which contains interference and noise, by leveraging the powerful fitting ability of GAN. Specifically, this method was tested using coherent suppression interference, smart noise interference, and noise frequency modulation suppression interference. We compared the proposed GAN method with recurrent neural network, short‐time Fourier transform time‐varying filtering, short‐time fractional Fourier transform time‐varying filtering algorithms and RNN approach. The results show that the interference suppression algorithm based on GAN is superior to the other three algorithms. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) significantly hampers the target detection performance of frequency-modulated continuous-wave radar. To address the problem and maintain the target echo signal, this paper proposes a priori assumption on the interference component nature in the radar received signal, as well as a method for interference estimation and mitigation via time–frequency analysis. The solution employs Fourier synchrosqueezed transform to implement the radar's beat signal transformation from time domain to time–frequency domain, thus converting the interference mitigation to the task of time–frequency distribution image restoration. The solution proposes the use of image processing based on the dual-tree complex wavelet transform and combines it with the spatial domain-based approach, thereby establishing a dual-domain fusion interference filter for time–frequency distribution images. This paper also presents a convolutional neural network model of structurally improved UNet++, which serves as the interference estimator. The proposed solution demonstrated its capability against various forms of RFI through the simulation experiment and showed a superior interference mitigation performance over other CNN model-based approaches. [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 considers theoretical and observed uncertainties in the estimates of ZDR and ρHV(0) using data from an operational S-band radar and a mobile X-band radar. Cases of widespread uniform precipitation including bright-band, clear air, and ash echoes from forest fires are all considered in order to obtain a wide range of ρHV(0) values as this along with the radar frequency and spectrum width determines the uncertainties. The theoretical uncertainties in these parameters provide a good estimate of the lower bound of the standard deviations of the observed values where these have been estimated using the adjacent data to the target pixel. The implications for the accuracy of precipitation estimation, particle identification, and estimates of drop-size distributions are discussed. Significance Statement: High-quality quantitative precipitation and particle size/classification retrievals using weather radar are strongly dependent on the accuracy of ZDR and ρHV(0). This paper examines the theoretical limits to the measurement accuracy and verifies these limits with radar data at 10- and 3-cm wavelengths. [ABSTRACT FROM AUTHOR]

The joint transmit and receive beamforming algorithm has been envisioned to optimise the target's signal‐to‐interference‐plus‐noise ratio by co‐designing the transmit and receive beamforming vectors simultaneously. However, the traditional design concepts for this algorithm only consider the monostatic radar system, which may not work as well for the multistatic radar system. The authors present a novel transmit and receive beamforming algorithm for the multistatic radar system that includes a common transmitter and multiple receivers. The transmit beamforming vector can directly influence the output signal‐to‐interference‐plus‐noise ratio of each radar subsystem. To ensure the balanced performance of the subsystems, the authors propose a weighted sum of the signal‐to‐interference‐plus‐noise ratio optimisation problem to co‐design the transmit and receive beamforming. The proposed problem is non‐convex, and the authors construct an iterative algorithm to solve it using semi‐definite relaxation and slack‐variable replacement techniques. By using pre‐determined weights, the output performance of each radar subsystem can be effectively regulated. The simulation results confirm that the proposed algorithm can ensure better output signal‐to‐interference‐plus‐noise ratio for the entire multistatic radar system than the conventional joint transmit and receive beamforming algorithm. [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]

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]

For the integration detection of near space hypersonic weak targets by high pulse repetition frequency (PRF) radar, a novel method named Multi‐Hypothesis Fuzzy‐Matching Radon transform (MHFM‐RT) is proposed for the near space hypersonic target detection and tracking. For remote hypersonic target detection, to avoid range ambiguity, current radars always use a low PRF mode, which limit the number of pulse accumulations. Using the high PRF mode, the fuzzy folding will appear in the target range measurements when target trajectory crosses range fuzzy intervals. Therefore, there is a contradiction between range ambiguity and energy accumulation. The proposed method is used to match the fuzzy measurements, so as to realise the correct integration in the condition of range ambiguity. Firstly, considering the need of range ambiguity resolution, the mode of staggered PRF is used. Secondly, the first frame measurements are periodically extended for multiple‐fuzzy hypothesis. Finally, the weak target track is accumulated in MHFM‐RT domain, and the signal integration and ambiguity resolution can be realised simultaneously. The proposed method expands the Variable‐Diameter‐Arc‐Helix Radon transform (VDAH‐RT) method to fuzzy folding conditions. Compared with the existing methods, for 7‐scan measurements non‐coherent integration, the detection sensitivity of the proposed method is about 0.5–1 dB higher than that of the IMM hybrid filter algorithm, and about 1 dB higher than that of the RHT‐TBD approach, and it needs less storage space and has higher detection probability. [ABSTRACT FROM AUTHOR]

High performance computing (HPC) electromagnetic (EM) emulators are used to simulate real-time EM wave interactions between numerous radar targets. Radar Cross Section (RCS) data stores reflection profiles of radar targets as a table; however, the needed storage size increases with frequency sampling density, aspect angle sampling density, and number of target types. The large quantity of data often exceeds storage capability and limits manipulation and representation feasibility. The spherical harmonic based monostatic anisotropic point scatterer model is proposed for HPC EM simulations where scattering responses can be computed with finite impulse response (FIR) filters. An efficient algorithm constructing this model with large scale RCS data is discussed. The scatterer position and the reflection profile of each scatterer are solved using least squares methods and particle swarm optimization (PSO). In addition, the function evaluations in PSO are accelerated by taking advantage of the matrix structure, making the algorithm 22 times faster [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]

Monostatic rapid single-view radar imaging technology is a technique that employs single incidence angle and single frequency point information to implement rapid monostatic radar imaging within a small angular field. Owing to its analytical expression, this technique can substitute the traditional frequency-angle- scanning imaging in a small angular range, facilitating the rapid generation of highly realistic radar imaging data slices for complex targets and environments. This technology has been significantly applied in scatter hotspot diagnostics and target recognition. In order to achieve the windowing effect equivalent to that of frequency-angle-scanning imaging, and to enhance the scattering feature of monostatic imaging while controlling sidelobes, this paper derives analytic windowed imaging formulas for monostatic radar. It then obtains analytical expressions for various typical monostatic windowing rapid radar imaging scenarios. This enables the monostatic rapid imaging technology to maintain high efficiency in its analytical expressions while achieving the windowing effect equivalent to traditional imaging. The validity and correctness of the analytical formula and software implementation have been confirmed through 1D, 2D, and 3D imaging verifications. This technology can provide a vast amount of training data for modern radars. [ABSTRACT FROM AUTHOR]

Monostatic rapid single-view radar imaging technology is a technique that employs single incidence angle and single frequency point information to implement rapid monostatic radar imaging within a small angular field. Owing to its analytical expression, this technique can substitute the traditional frequency-angle-scanning imaging in a small angular range, facilitating the rapid generation of highly realistic radar imaging data slices for complex targets and environments. This technology has been significantly applied in scatter hotspot diagnostics and target recognition. In order to achieve the windowing effect equivalent to that of frequency-angle-scanning imaging, and to enhance the scattering feature of monostatic imaging while controlling sidelobes, this paper derives analytic windowed imaging formulas for monostatic radar. It then obtains analytical expressions for various typical monostatic windowing rapid radar imaging scenarios. This enables the monostatic rapid imaging technology to maintain high efficiency in its analytical expressions while achieving the windowing effect equivalent to traditional imaging. The validity and correctness of the analytical formula and software implementation have been confirmed through 1D, 2D, and 3D imaging verifications. This technology can provide a vast amount of training data for modern radars. [ABSTRACT FROM AUTHOR]

The traditional algorithms for generating 3D human point clouds often face challenges in dealing with issues such as phantom targets and target classification caused by electromagnetic multipath effects, resulting in a lack of accuracy in the generated point clouds and requiring manual labeling of the position of the human body. To address these problems, this paper proposes an adaptive method for generating 3D human point clouds based on 4D millimeter-wave radar (Self-Adaptive mPoint, SA-mPoint). This method estimates the rough human point cloud by considering micro-motion and respiration characteristics while combining the echo dynamic with static information. Furthermore, it enhances the density of point cloud generation. It reduces interference from multipath noise through multi-frame dynamic fusion and an adaptive density-based clustering algorithm based on the center points of humans. The effectiveness of the SA-mPoint algorithm is verified through experiments conducted using the TI Millimeter Wave Cascade Imaging Radar Radio Frequency Evaluation Module 77G 4D cascade radar to collect challenging raw data consisting of single-target and multi-target human poses in an open classroom setting. Experimental results demonstrate that the proposed algorithm achieves an average accuracy rate of 97.94% for generating point clouds. Compared to the popular TI-mPoint algorithm, it generates a higher number of point clouds on average (increased by 87.94%), improves the average accuracy rate for generating point clouds (increased by 78.3%), and reduces the running time on average (reduced by 11.41%). This approach exhibits high practicality and promising application prospects. [ABSTRACT FROM AUTHOR]

A fundamental challenge in the practical implementation of multistatic radar systems (MSRS) is the requirement for precise time and frequency synchronisation between the spatially separated radar nodes. The authors evaluate the performance of different classes of commercially available Global Navigation Satellite Systems (GNSS) timing receivers, Local Oscillators (LO) and GNSS Disciplined Oscillators (GNSSDOs) to determine the limitations of using one‐way GNSS Time and Frequency Transfer (TFT) in this application. From evaluating the performance of three pairs of GNSSDOs, it is concluded that one‐way GNSS TFT will likely be suitable only for the synchronisation of fully spatially coherent MSRS with carrier frequencies up to 100 MHz and waveform bandwidths up to 20 MHz. Whereas, in the case of short‐term spatially coherent MSRS, synchronisation of systems with carrier frequencies up to a few GHz and waveform bandwidths of over 100 MHz will likely be possible. The performance of the different classes of GNSSDOs during GNSS denial (holdover) are evaluated, where it is concluded that frequency offsets between LOs at the point of GNSS denial will often significantly contribute, or even dominate, the holdover performance. Analysis of two practical multistatic radar measurements verifies the function of using the GNSSDOs for wireless synchronisation of the ARESTOR MSRS. [ABSTRACT FROM AUTHOR]

This article is a guest editorial for the IET Radar, Sonar & Navigation journal, focusing on selected papers from the RADAR 2022 conference held in Edinburgh, UK. The conference provided an opportunity for radar specialists from 22 countries to explore the latest developments in radar systems. Key topics discussed at the conference included new radar trends, target detection (with a focus on drones), low-frequency radar, and cognitive radar. The special issue contains 17 papers based on extended work presented at the conference, covering topics such as multistatic radar, passive radar, target signatures, and advanced radar processing techniques. The authors hope that this special issue will serve as a valuable resource for further research in the field. [Extracted from the article]

An analysis of the parameter estimation uncertainty for the target location and velocity achievable using a single‐transmitter‐multiple‐receiver multistatic radar system is presented. A framework for establishing measures of multistatic radar parameter uncertainties by expansion of the bistatic radar parameter uncertainty measures is presented for systems containing omnidirectionally radiating nodes. The methodology uses analytical methods based on the Cramér–Rao Lower Bounds applied to scenarios in a two‐dimensional physical space with a single target exhibiting Doppler characteristics and a bistatic angle dependent radar cross‐section. A set of geometric descriptors is proposed to characterise the system, and parameter uncertainty results are reported as a function of these descriptors. The results indicate that angular separation between the transmitter and the centre of the receiver distribution is of greater importance than the quantity of receivers within the system when low uncertainty estimation capabilities are desired, though a minimum of two receivers must be available. The proportion of receivers within the system which contributed information crucial to obtaining the minimum estimation uncertainty is reported for systems containing different quantities of receivers. It was observed that, as the total number of receivers available increased, the proportion of receivers required to achieve the minimal uncertainty level reduces significantly. [ABSTRACT FROM AUTHOR]

The use of WiFi signals for sensing purposes has attracted a lot of interest from both the radar and communications communities and several techniques have been explored. In the attempt of meeting the requirements for small sensor size, compactness, and easy deployment, the authors consider reference‐free approaches, namely approaches that do not require a good copy of the transmitted waveform to be available at the radar receiver. To this end, the authors first resort to a passive radar‐based processing scheme that only exploits the invariant a priori known initial portion of the physical layer protocol data unit, that is, the PHY Preamble, and its limitations in practical applications is investigated. Specifically, the authors show that, with this approach, an accurate time, phase, and frequency synchronization is essential and a possible strategy is investigated. As an alternative solution the authors consider a forward scatter radar‐based approach where only the amplitude modulation of the received signal is exploited to detect the presence of a moving target thus avoiding the need to know the transmitted signal. For the first time, the authors comparatively investigate advantages and drawbacks of the two reference‐free approaches and present practical strategies to handle the limitations observed. The results are reported for experimental tests with people and drones using WiFi transmissions in the 2.4 and 5 GHz band. [ABSTRACT FROM AUTHOR]

This paper proposes a simple non‐adaptive approach for the suppression of direct signal and clutter contributions in a passive radar system based on orthogonal frequency division multiplexing transmissions, exploiting the properties of a reciprocal filter (RF). The use of a RF allows to circumvent the limits posed by the waveform ambiguity function, producing a data‐independent time‐invariant response to a stationary point‐like target echo at the output of the range compression stage. This feature enables simple clutter cancellation strategies, based on the subtraction of delayed portions of the surveillance signal, according to the conventional moving target indication methodologies. The proposed processing scheme consists of a range compression stage based on a RF, followed by a Single Canceller. Its performance is investigated against a simulated and an experimental data set, for the case of a digital video broadcasting‐terrestial‐based passive radar scenario, and it is compared to an alternative scheme based on a conventional matched filter and adaptive cancellation techniques. The proposed approach is shown to yield a perfect cancellation of the clutter returns from a stationary scene under ideal conditions. Moreover, it proves to be robust to real‐world conditions, despite its considerably limited computational load compared to adaptive clutter cancellation techniques. [ABSTRACT FROM AUTHOR]

This article discusses the importance of radar systems and processing methods for space situational awareness (SSA). It highlights the vulnerability of the space environment due to the increasing number of satellites and space debris, as well as the growing threat of attacks. Radar sensors are particularly useful for SSA because they can detect small debris over long ranges and accurately track objects. The article presents various topics related to radar systems for SSA, including ground-based and space-based radar, multistatic radar networks, and cognitive radar and deep-learning methods. It concludes by providing an overview of the articles included in the special issue. [Extracted from the article]

Traffic participant classification is crucial in autonomous driving perception. Millimeter wave radio detection and ranging radar is a cost-effective and powerful method to perform the task in adverse traffic scenarios, especially in bad inclement weather (e.g. fog, snow and rain) and poor lighting conditions. This paper presents an intelligent target classification algorithm for 77G radar based on correction data set. First, in order to handle the problem that the original data set may easily be interfered by obstacles, the angle information is filtered by analyzing the spatial information of radar signals, which means the interference clutter of obstacles can be effectively removed. Second, the primary data set is corrected using the significant difference between the micro-Doppler of the human body and car. Finally, the characteristic information of the radar signal is extracted, including distance, speed, orientation, micro-Doppler and reflection intensity, and the obtained data sets containing three types of targets (vehicles, human bodies and obstacles) are generated. The generated dynamic and static data sets are collected by sufficient experiments to construct deep learning classification models. The results show that the classification accuracy is improved by the measure of data set correction. [ABSTRACT FROM AUTHOR]

Passive bistatic radar is a novel radar technology that passively detects targets without actively emitting signals. Since passive bistatic radar entails larger data volumes and computations compared to traditional active radiation radar, the development of hardware and software platforms capable of efficiently processing signals from passive bistatic radar has emerged as a research focus in this field. This research investigates the signal processing flow of passive bistatic radar based on its characteristics and devises a parallel signal processing scheme under graphic processing unit (GPU) architecture for computation-intensive tasks. The proposed scheme utilizes high-computing-power GPU as the hardware platform and compute unified device architecture (CUDA) as the software platform and optimizes the extensive cancellation algorithm batches (ECA-B), range Doppler and constant false alarm detection algorithms. The detection and tracking of a single target are realized on the passive bistatic radar dataset of natural scenarios, and experiments show that the design of this algorithm can achieve a maximum acceleration ratio of 113.13. Comparative experiments conducted with varying data volumes revealed that this method significantly enhances the signal processing rate for passive bistatic radar. [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]

This paper investigates the performance of a wide variety of radar imaging modes, such as nadir-looking B-scan, or side-looking synthetic aperture radar tomographic acquisitions, performed in both back- and forward-scattering geometries, for the inspection and characterization of roadways. Nadir-looking B-scan corresponds to a low-complexity mode exploiting the direct return from the response, whereas side-looking configurations allow the utilization of angular and polarimetric diversity in order to analyze advanced features. The main objective of this paper is to evaluate the ability of each configuration, independently of aspects related to operational implementation, to discriminate and localize shallow underground defects in the wearing course of roadways, and to estimate key geophysical parameters, such as roughness and dielectric permittivity. Campaign measurements are conducted using short-range radar stepped-frequency continuous-waveform (SFCW) devices operated in the C and X bands, at the pavement fatigue carousel of Université Gustave Eiffel, over debonded areas with artificial defects. The results indicate the great potential of the newly proposed forward-scattering tomographic configuration for detecting slight defects and characterizing roadways. Case studies, performed in the presence of narrow horizontal heterogeneities which cannot be detected using classical B-scan, show that both the coherent integration along an aperture using the back-projection algorithm, and the exploitation of scattering mechanisms specific to the forward-looking bistatic geometry, allows anomalous echoes to be detected and further characterized, confirming the efficacy of radar imaging techniques in such applications. [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]

We evaluate the correlation function of a radar signal scattered by inhomogeneities of turbulent medium and received by a radar station at the output of the matched filter. For the turbulent medium, we employ the model of wandering inhomogeneities, which is a generalization of the 'frozen' turbulence. The concepts of strong and weak wanderings are introduced. The case of bistatic radar station is considered. For a monostatic radar station, simple asymptotic formulas are obtained. It is shown that, in the short-wavelength limit, wanderings are almost always strong, and in the long-wavelength limit, the weak wandering limit takes place mainly in the long-wavelength approximation. [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]

We present an analytical solution to the problem of determining a complex dielectric permittivity of ground based on bistatic Moon location results for the case where the measured values are the moduli of reflection coefficients of radio waves for vertical and horizontal polarizations and the difference in their phases. [ABSTRACT FROM AUTHOR]

Drones are currently being used for various applications. However, the detection of drones for defense or security purposes has become problematic because of the use of plastic materials and the small size of these drones. Any drone can be placed under surveillance to accurately determine its position by collecting high-resolution data using various detectors such as the radar system proposed in this paper. The W-band radar has a high carrier frequency, which makes it easy to design a wide bandwidth system, and the wideband FMCW signal is suitable for creating high resolution images from a distance. Unfortunately, the huge amounts of data gathered in this way also contain clutter (such as background data and noise) that is usually generated from unstable radar systems and complex environmental factors, and which frequently gives rise to distorted data. Accurate extraction of the position of the target from this big data requires the clutter to be suppressed and canceled, but conventional clutter cancellation methods are not suitable. Four clutter cancellation algorithms are assessed and compared: standard deviation, adaptive least mean squares (LMS), recursive least squares (RLS), and the proposed LMS. The proposed LMS has combined LMS with the standard deviation method. First, the big data pertaining to the target position is collected using the W-band radar system. Subsequently, the target position is calculated by applying these algorithms. The performance of the proposed algorithms is measured and compared to that of the other three algorithms by conducting outdoor experiments. [ABSTRACT FROM AUTHOR]

Due to the spectrum congestion in the future 6G networks and the dramatic increasing requirement for the equipment that can detect and communicate simultaneously, the design of bi‐static radar and communication integrated (RCI) system is receiving more and more attention. Orthogonal frequency division multiplexing (OFDM) is a commonly used waveform in bi‐static RCI system. There are two gaps in current research that need to be filled. First, high peak‐to‐average ratio (PAPR) of OFDM signal is less considered in the conventional bi‐static RCI system due to the difficulty in recovering in the receiver. Second, the target echo estimation depends on the reconstruction of the direct‐path communication (DPC) signal. To overcome these limitations, we firstly propose a deep learning (DL)‐based transceiver design for the bi‐static RCI systems. Specifically, we design a neural network (NN)‐based waveform designer (WDNN) to reduce the PAPR of the transmit waveform, an NN‐based signal separator (SSN) to directly separate the target echo and the DPC signal, and an NN‐based symbol recovery (SRN) to demodulate the symbols. By using the training data and channel realisations, these three networks are jointly trained offline to minimise the PAPR of the transmit waveform, the separation error of the target echo and DPC signals, and the bit error rate. Then, in the online deployment phase, the trained WDNN is used to modify the transmitted waveform, and the trained SSN and SRN are placed at the receiver to separate signal and demodulate symbol. Simulation results show that compared to the conventional method, the proposed scheme not only reduces the PAPR but also improves the estimation of target parameters and communication symbol. [ABSTRACT FROM AUTHOR]

• Evolving the Radar System Simulator into the Sensor Network Simulator. • Newly implemented Optical Performance Model was verified and validated with real data. • The SNS tool suite can be used to analyze sensor networks and their performance. The number of human-made objects orbiting the Earth is growing steadily. This trend is further accelerating during recent years due to increasing commercialization in the field of satellite operations and an increasing launch rate. Concerning the area of space surveillance and tracking (SST), this leads to higher demands with respect to object observation capabilities. In order to ensure the safe usability of the different Earth orbits for all spacecraft operators, it is of great importance to continuously observe and catalog as many of the human-made objects as possible. This is especially true for inactive and uncontrollable objects, i.e. space debris. Only by maintaining accurate catalogs of the space debris environment, involuntary collisions can be avoided and risks of losing a spacecraft kept low. While the number of space debris objects can only be expected to further increase, it is not trivial to increase the number of observation sites, such as telescopes and radars, or their capabilities to counter this. One way to increase the observation capabilities without constructing new observation sites is to search for possibilities to improve the efficiency with which the existing sensors observe objects. With the goal to study and analyze single sensors, sensor networks, and how they can be used in the most efficient way, a sensor network simulator tool suite has been developed at the Institute of Space Systems of the Technical University of Braunschweig. Originally, the tool suite was developed as a radar system simulator concentrating on the different radar system configurations, such as reflector antennas or phased arrays that are used in a mono- or bistatic mode. Additionally, the corresponding radar performance model is able to simulate a tracking as well as a scanning mode. Building upon this tool suite, it is now extended to include an optical performance model and the possibility to simulate not only ground-based but also space-based sensors. Within this work, the preliminary results obtained with the newly implemented optical performance model are shown. Using the enhanced version of this sensor network simulator tool suite, thorough analyses can now be made using the radar and optical performance models. Sensor networks consisting of radars and telescopes being both ground- or space-based can be studied in different configurations. Thereby, different aspects, such as observation strategies, sensor tasking, or choosing the right location and sensor to reach a certain goal can be investigated. [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 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]

Forward scattering radar has been widely used to classify human movements. To reduce concern of prolonged radiation exposure, Wi-Fi based passive scattering radar with bistatic topology is proposed. The movements include walking, bending, sitting, and kneeling. The enveloping algorithm applied to Wi-Fi based passive forward scattering radar signals has produced a comparable pattern to the ones from the active configuration. The features extracted from the frequency spectrum are also similar for both radar topologies. These led to comparable performance when classifying using artificial neural network. Hence, WiFi can effectively reduce the cost of radar sensors. [ABSTRACT FROM AUTHOR]

The Goddard Profiling Algorithm (GPROF) is used operationally for the retrieval of surface precipitation and hydrometeor profiles from the passive microwave (PMW) observations of the Global Precipitation Measurement (GPM) mission. Recent updates have led to GPROF V7, which has entered operational use in May 2022. In parallel, development is underway to improve the retrieval by transitioning to a neural-network-based algorithm called GPROF-NN. This study validates GPROF V7 and multiple configurations of the GPROF-NN retrieval against ground-based radar mea- surements over the conterminous United States (CONUS) and the tropical Pacific. GPROF retrievals from the GPMMicrowave Imager (GMI) are validated over several years and their ability to reproduce regional precipitation characteristics and effective resolution is assessed. Moreover, the retrieval accuracy for several other sensors of the constellation is evaluated. The validation of GPROF V7 indicates that the retrieval produces reliable precipitation estimates over CONUS. During all four assessed years, annual mean precipitation is within 8% of gauge-corrected radar measurements. Although biases of up to 25% are observed over sub-regions of the CONUS and the tropical Pacific, the retrieval reproduces the principal precipitation characteristics of each region. The effective resolution of GPROF V7 is found to be 51km over CONUS and 18km over the tropical Pacific. GPROF V7 produces robust precipitation estimates also for the other sensors of the GPM constellation. The evaluation further shows that the GPROF-NN retrievals have the potential to significantly improve the GPROF precipi- tation retrievals. GPROF-NN 1D, the most basic neural network implementation of GPROF, improves the mean-squared error, mean absolute error, correlation and symmetric mean absolute percentage error by about twenty percent for GPROF GMI while the effective resolution is improved to 31km over land and 15km over oceans. The two GPROF-NN retrievals that are based on convolutional neural networks can further improve the accuracy up to the level of the combined radar/radiometer retrievals from the GPM core observatory. However, these retrievals are found to overfit on the viewing geometry at the center of the swath, reducing their overall accuracy to that of GPROF-NN 1D. For the other sensors of the constellation, the GPROF-NN retrievals produce larger biases than GPROF V7 and only GPROF-NN 3D achieves consistent improvements compared to GPROF V7 in terms of the other assessed error metrics. This points to shortcomings in the hydrometeor profiles or radiative transfer simulations used in the training of the retrievals for the other sensors of the GPM constellation as a critical limitation for improving GPM PMW retrievals. [ABSTRACT FROM AUTHOR]

It has been shown that bistatic radars using radio telescopes as receivers can be used to increase the sensitivity of orbital debris measurements over traditional terrestrial monostatic radar. A method to calculate the lateral surface area of a bistatic radar is needed to evaluate the efficacy of a proposed bistatic radar configuration for orbital debris measurements. For over three decades, models of the orbital debris (OD) environment in low Earth orbit (LEO) have been developed to assess the risk posed by OD to spacecraft. While terrestrial radar measures debris 3 mm and larger and in situ measurements provide data for debris smaller than 1 mm, no good data sources exist for debris between 1 mm and 3 mm in size. This results in large variations between competing OD models. It also happens to be the size regime which poses the highest mission-ending risk to spacecraft. It is, therefore, of interest to investigate potential new data sources for this under-sampled size regime. There are many radars and radio telescopes that could be combined to create sensitive bistatic radars that could potentially bridge the size gap. In addition to sensitivity, it is necessary to predict the expected count rate of a candidate sensor to evaluate its performance. NASA's Orbital Debris Engineering Model (ORDEM) can be used to predict the flux of debris passing through the line of sight of a radar or telescope. This flux is related to a count rate through the calculation of the lateral surface area of the sensor. While this can be done easily for monostatic radars, a method for calculating the lateral surface area of a bistatic radar is needed. This new method of calculation has been developed and is described. The new method maps the radar beam overlap in 3D space, calculating the area of the complex surface formed by the gain product of the two antennas. Comparisons of the monostatic and new bistatic lateral surface area calculation methods for the monostatic case are presented. Results of a sample lateral surface area calculation for a bistatic radar observation configuration currently employed by NASA are shown. Finally, a guide for total error as a function of baseline and target altitude is established. [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]

Navigation safety at sea is vital for each autonomous surface vehicle (ASV), which involves the problem of motion planning in dynamic environments and their robust tracking through feedback control. We present a practical path-planning method that generates smooth trajectories for a marine vehicle traveling in an unknown environment, where obstacles are detected in real time by millimetre wave (mmWave) radar. Our approach introduces a polynomial curve to describe the lateral and longitudinal trajectories in the Frenet frame, known as the 'motion primitives', whose combination ensures that the planning area is properly covered. In addition, we can select a feasible, optimal and collision-free trajectory from such a set of motion primitives that is generated by considering the vehicle dynamics and comfort. The capabilities of proposed algorithm are demonstrated in the experiment with static and dynamic obstacles. [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]

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]