The range-dependent phases induced by Frequency Diverse Array (FDA)-Multiple-Input Multiple-Output (MIMO) radar provide additional degrees of freedom for range related scenarios. This paper investigates the joint transmit waveform and frequency increment design of FDA-MIMO radar to enhance target detection in multipath environments. Specifically, we establish a generalized FDA-MIMO signal model for multipath reception and formulate a Signal-to-Interference-plus-Noise Ratio (SINR) maximization problem over the transmit waveform and frequency increment, while constraining the waveform modulus and increment upper-bound. Leveraging on an alternating optimization framework, we update the waveform via power-method-like implementation and the frequency increment via iterating an analytical expression based on a Majorization-Minimization (MM) scheme. Simulation results indicate that the proposed method achieves a gain up to 14dB over conventional MIMO, demonstrating superior energy focusing effect at target location.

This paper investigates transceiver design optimization strategies for frequency diverse array (FDA)-multiple-input multiple-output (MIMO) radar-communication coexistence (RCC) systems, focusing on both radar-centric and communication-centric modes. Specifically, the former formulates the design problem to maximize the signal-to-interference-plus-noise ratio (SINR) in mainlobe deceptive jammer scenarios, whereas the latter aims to maximize the communication rate while simultaneously satisfying a predefined radar SINR constraint. In this framework, practical constraints pertaining to the radar’s transmitted waveform, communication codebook, frequency increment, and receive filter are taken into account. To address the resultant non-convex and NP-hard optimization problems, a maximum block improvement (MBI) approach is employed, where the variables are alternately examined, which are achieved either by leveraging closed-form expressions and hidden convexities or by resorting to the minorization-maximization (MM) approach, while keeping the remaining parameters fixed. The convergence performance of the devised algorithm is thoroughly examined, alongside their computational complexity analyses. Numerical results are provided to validate the efficacy of our approach against mainlobe deceptive jammers, demonstrating superior SINR and communication rate performance compared to existing optimization strategies and benchmark system frameworks.

This paper explores the mitigation of mainlobe deceptive jammers using a phase-coded planar array. At the modelling stage, a Two-Dimensional (2D) phase coding scheme is employed in the transmit planar array, along with the transmission of Linear Frequency Modulated (LFM) waveform in a planar array. Such a phase-coded planar array not only preserves the waveform properties, but also provides enhanced controllable beamforming capabilities in elevation-azimuth-range 3D domain. In the receiver, by separating and compensating the transmitted signals, the mainlobe deceptive jammers occurring in different range ambiguous regions, are discriminated and suppressed. At the analysis stage, the Capon spectra and spatial filtering results are provided to verify the effectiveness of mainlobe jammer mitigation with the proposed phase-coded planar array.

The existence of range ambiguity and range dependence will seriously deteriorate the performance of space-time adaptive processing (STAP). In this regard, an adaptive range-ambiguous clutter separation method suitable for the element-pulse coding (EPC)-multiple-input multiple-output (MIMO) radar is developed in this letter. By introducing the EPC factor in both transmit elements and pulses, the clutter located in different range-ambiguous regions can be distinguished in the transmit spatial frequency dimension. Particularly, to ensure the separated performance of range-ambiguous clutter, the EPC factor is designed. Moreover, an approach on the basis of reweighted atomic norm minimization (RANM) is developed to separate the range-ambiguous clutter, leveraging the transmit spatial frequencies of clutter located in various range ambiguity areas. Furthermore, after clutter separation, the clutter is canceled via STAP individually in each range ambiguous region. A series of simulation results validate the efficacy of the proposed approach.

This letter presents an optimization approach for frequency increments tailored to Frequency Diverse Array (FDA)-Multiple-Input Multiple-Output (MIMO) radar for target localization. We start to formulate the problem as minimizing the Cramér-Rao Bounds (CRBs) for both range and angle estimation, subject to practical constraints on the frequency increments. To facilitate optimization, the objective function is mathematically transformed, which results in a maximization problem, leveraging its inherent non-negativity of both the numerator and denominator. To address the resultant non-convex and NP-hard optimization problem, a Minorization-Maximization (MM)-Maximum Block Improvement (MBI) algorithm is devised by partitioning the frequency increment vector into distinct blocks, allowing for alternating maximization. In particular, each frequency increment is refined with the MM algorithm, while holding the others fixed, and only the block yielding the maximum objective increment is updated within each iteration. Simulation results are provided to demonstrate the excellent target localization of our proposed approach.

This paper deals with the simultaneous adaptive target detection and parameter estimation utilizing a Frequency Diverse Array Multiple-Input Multiple-Output (FDA-MIMO) radar. At the design stage, a linearized model of the received signal is derived, leveraging the centro-Hermitian (persymmetric) structure of the array manifold and considering potential steering vector mismatches related to the actual target parameters. Exploiting this model, the detection problem is formulated and tackled leveraging adaptive detection approaches, resorting to the Persymmetric Generalized Likelihood Ratio (PGLR) and Persymmetric Adaptive Matched Filter (PAMF), respectively. However, they demand the Maximum Likelihood (ML) estimates of target incremental range (i.e., the deviation of the target actual position from the center of the range bin) and angle. Two iterative methods are devised to solve the ML estimation problem. The former is based on Dinkelbach's algorithm, which is capable of achieving the global optimum, whereas the latter is a fast-converging alternative approach relying on the Coordinate Descent (CD) framework. Numerical results highlight the effectiveness of the proposed simultaneous detection and estimation strategies, also in comparison with suitable benchmarks and counterparts.

Spaceborne distributed multi-transmit multi-receive (SD-MIMO) radar has great potential to enhance the capability of ground moving target indication (GMTI), particularly with the increasing of space-time-frequency degrees of freedom. However, GMTI performance is severely limited by complex geographical clutter. To address this issue, we proposed a clutter suppression method assisted by online classification using the range-Doppler-band 3D dataset of SD-MIMO radar. The core points and innovations of our method are summarized as follows. Firstly, we extend Markov random field (MRF) classification to 3D space by integrating a 3D adaptive weighted penalty (3D-AWP) function and a probability distribution model of multi-channel clutter. The 3D-AWP function is constructed using a novel spatial distance metric designed to characterize the interaction potential of image pixels within their 3D neighborhood. This ensures that the MRF smooth factor can be adaptively adjusted in both homogeneous and inhomogeneous clutter regions through different weighting values. The probability distribution model of clutter is established by the K-Wishart distribution, which incorporates amplitude and interferometric phase between multiple spatial channels. Then, under the Bayesian framework, we achieve classification of multiband SAR images by solving the maximum posterior probability problem. Subsequently, the homogeneous and inhomogeneous regions are identified based on classification results. And adaptive clutter suppression processing is performed independently for each sub-region. Finally, the SAR-GMTI experiments are performed utilizing synthetic data and measured data. The results demonstrated an improvement in clutter suppression capability by 2 dB and an enhancement in target oSCNR by 1.5 dB compared to traditional methods.

Ground Moving Target Indication (GMTI) has always been one of the key tasks in synthetic aperture radar (SAR) systems. For SAR-GMTI, existing GMTI algorithms can be classified into traditional signal processing algorithms and low-rank matrix recovery algorithms. Space-time adaptive processing (STAP), as a typical traditional approach, has shown excellent performance in detecting moving targets in real applications; low-rank matrix recovery algorithms have become one of the recent research hotspots. Robust principal component analysis (RPCA), as a typical low-rank matrix recovery approach, has been applied in SAR-GMTI due to its ability to decompose an approximate low-rank matrix into a low-rank component and a sparse component. However, a drawback of RPCA algorithms is its relatively high false alarm rate caused by the energy leakage from strong clutter. The detection performance of the STAP algorithm may degrade when the training samples are contaminated. To address these challenges, we propose a block-based RPCA-STAP algorithm that integrates traditional adaptive clutter suppression methods into the RPCA framework. The proposed algorithm first implements clutter classification using the Markov random field (MRF) image segmentation algorithm. Then, RPCA-STAP suppression is applied to different clutter blocks, which not only satisfies the IID requirement for STAP training samples but also reduces the false alarm rate caused by strong clutter in the RPCA algorithm by introducing STAP. Additionally, considering the sensitivity of GMTI algorithms to channel errors, we propose an improved adaptive 2-D calibration (IA2DC) algorithm to further enhance channel correlation. Simulation and real data experiments validate the effectiveness of the proposed algorithms.

Despite the enhanced spectral efficiency brought by the integrated radar and communication technique, it poses significant risks to communication security when confronted with malicious radar targets. To address this issue, a reconfigurable intelligent surface (RIS)-aided transmission scheme is proposed to improve secure communication in two systems, i.e., the radar and communication co-existing (RCCE) system, where a single transmitter is utilized for both radar sensing and communication, and the dual-functional radar and communication (DFRC) system. At the design stage, optimization problems are formulated to maximize the secrecy rate while satisfying the radar detection constraint via joint active beamforming at the base station and passive beamforming of RIS in both systems. Particularly, a zero-forcing-based block coordinate descent (BCD) algorithm is developed for the RCCE system. Besides, the Dinkelbach method combined with semidefinite relaxation is employed for the DFRC system, and to further reduce the computational complexity, a Riemannian conjugate gradient-based alternating optimization algorithm is proposed. Moreover, the RIS-aided robust secure communication in the DFRC system is investigated by considering the eavesdropper’s imperfect channel state information (CSI), where a bounded uncertainty model is adopted to capture the angle error and fading channel error of the eavesdropper, and a tractable bound for their joint uncertainty is derived. Simulation results confirm the effectiveness of the developed RIS-aided transmission scheme to improve the secrecy rate even with the eavesdropper’s imperfect CSI, and comparisons between both systems reveal that the RCCE system can provide a higher secrecy rate than the DFRC system.

This paper aims to suppress mainlobe deceptive jammers considering the multipath effect in a frequency diverse array-multiple-input multiple-output (FDA-MIMO) radar. At the problem formulation stage, the overall received signal including the true target, main-lobe deceptive jammers, and burst jamming signal in the presence of multipath propagation, is represented as a "low-rank + low-rank + sparse" decomposition model. Then, an improved Go Decomposition (GoDec) algorithm is developed to recover the components corresponding to the target signal and disturbance (including the mainlobe jammers and burst jamming signal). Furthermore, a data-dependent beamforming approach is implemented to eliminate the mainlobe deceptive jammers, where the covariance matrix is constructed using the recovered disturbance components, and the steering vector is obtained with a priori knowledge of the target position. Numerical results are provided to demonstrate the effectiveness of the devised technique and its superiority over competing methods in suppressing mainlobe deceptive jammers under multipath environments.

This paper deals with the problem of Doppler ambiguity in a frequency diverse array (FDA) multiple-input and multiple-output (MIMO) radar. In our modeling stage, a spread phase alignment (SPA) method is proposed by utilizing the pulse-dependent transmit spatial frequency. In this respect, the Doppler spread alignment is performed in each transmit pulse based on spatial cancellation. Hence, the point targets, which are de-focused due to the spread Doppler frequency can be focused in the transmit spatial frequency domain. Furthermore, the Doppler ambiguity index is estimated after principal velocity compensation resorting to the maximum likelihood (ML) criterion. Numerical results are provided to verify the effectiveness of the proposed method in resolving the Doppler ambiguity.

This letter focuses on discriminating true target from mainlobe jammers using a frequency diverse array-multiple-input multiple-output (FDA-MIMO) radar. To this end, a three-stage detection and discrimination architecture is proposed. Specifically, mainlobe jammers caused by large time delays can be discriminated from the true target based on the range degrees-of-freedom (DOFs) of FDA-MIMO radar, while the true target and jammers generated with small time delays are distinguishable in the fast time domain. In particular, a binary hypothesis test is formulated for discrimination, where the$H_{1}$hypothesis contains the true target, while the$H_{0}$hypothesis contains the unknown mainlobe jammer. The mainlobe jammer can be formulated as either a known subspace model or an unknown rank-one model. The former leads to a subspace-based selective detector (SSD) and the latter generates a rank-one-based SD (ROSD). Then, the formulated tests are handled using the generalized likelihood ratio test (GLRT) criterion. At the analysis stage, the selectivity and detection performance are compared with the conventional GLRT and selective receivers. Simulation results validate that the proposed detectors achieve satisfactory detection performance while providing the selectivity to mainlobe jammers.

In this letter, the problem of adaptive target detection for a frequency diverse array-multiple-input multiple-output (FDA-MIMO) radar in the presence of unknown mutual coupling (MC) is investigated. Unlike the conventional phased array (PA) radar, the signal model of FDA-MIMO radar in unknown MC is constructed using two symmetric Toeplitz matrices accounting for the effects of unknown MC at the transmitter and receiver. At the formulation stage, the detection problem is formulated as either a binary hypothesis test or a multi-hypothesis test. The former is tackled based on the generalized likelihood ratio test (GLRT) leading to a robust detector (RD). The latter resorts to the penalized test and multifamily LRT (MFLRT), resulting to Modified-Multiple Hypothesis Penalized Likelihood Ratio Test (M-MHPLRT) and MFLRT detectors, respectively. At the analysis stage, the effectiveness of the developed detectors is verified with comparisons among the benchmarks and existing detectors via Monte Carlo simulation results

Forward-looking mode for moving target detection is important for airborne radar systems, however, it is difficult to suppress the range-dependent clutter in the presence of range ambiguity using the traditional space-time adaptive processing (STAP) techniques. In this paper, a vertical coherent frequency diverse array (FDA) radar using quadratic phase coding (QPC) is proposed to alleviate the range-ambiguous clutter problem. The proposed vertical coherent FDA radar has two major advantages: i) it uses an identical baseband waveform for each transmit element, which prevents the unreal orthogonal waveform assumption in multiple-input multiple-output (MIMO) framework; ii) it achieves wide spatial coverage in elevation within a single pulse duration, which is desirable for the airborne reconnaissance radar. The space-frequency coupled characteristic of vertical coherent FDA is revealed and a series of 2-dimensional (2-D) matched filters are designed, which is helpful for range-ambiguous clutter separation. Based on the QPC technique, the residual clutter is suppressed by orthogonal projection (OP) filtering, which further improves the target detection performance. With the proposed vertical coherent FDA using QPC, the range-ambiguous clutter can be separated successfully, and the clutter suppression performance is improved. Simulation results are presented to verify the effectiveness of the proposed method in serious range-ambiguous clutter scenarios.

In this letter, a novel method based on multifrequency pulse diversity (MFPD) is proposed to achieve high-resolution and wide-swath (HRWS) imaging by utilizing the displaced phase center antenna (DPCA) technique. In the MFPD mode, multiple waveforms from different frequency bands are transmitted through a single channel. Thus, within the same receive window, the echoes from different range regions correspond to different frequency bands, making it possible to separate the range ambiguous echoes in the range frequency domain. However, the azimuth sampling rate will be reduced in the MFPD mode, leading to the Doppler ambiguity. To this end, the MFPD-DPCA technique is utilized, which is capable of separating the range ambiguous echoes without loss of azimuth sampling rate. Moreover, the MFPD-DPCA technique can achieve high range resolution by spectrum splicing, which enhances the feasibility of super-high-resolution imaging. Finally, the HRWS imaging can be obtained by performing the traditional synthetic aperture radar (SAR) algorithm on the reconstructed unambiguous wideband echoes. The proposed method offers an alternative in system implementation but does not necessarily offer improved swath width over current classical HRWS-SAR methods. Numerical results corroborate the effectiveness of the considered HRWS imaging strategies in ambiguous scenarios.

Beamforming plays a crucial role in synthetic aperture radar (SAR) for interference mitigation and ambiguity unaliasing. In this article, a series of novel beamforming methods for SAR systems is proposed based on nulling the transceive beampattern accurately in frequency diverse array (FDA)-multiple-input and multiple-output (MIMO) scheme. In general, these methods are implemented by assigning artificial interferences with prescribed powers within the given rectangular regions in the joint transmit-receive spatial frequency domain. In specific, according to the predefined null depths, closed-form expressions of artificial interference powers are first formulated. Then, iteration algorithms are developed to update the interference-plus-noise covariance matrix and the designed weight vector. In such a way, a trough-like transceive beampattern with arbitrarily distributed broadened nulls is formed in the joint transmit-receive spatial frequency domain. As a result, interferences mixed in signals received by SAR can be suppressed effectively. Numerical simulations and experimental results are provided to corroborate the effectiveness of the proposed methods.

Nowadays, in the complicated electromagnetic environment, the complex interferences, including the narrowband interferences (NBIs) and wideband interferences (WBIs), may severely affect the imaging quality of synthetic aperture radar (SAR) systems. Most traditional methods can only tackle with one kind of isolated interferences, NBIs or WBIs. In this paper, we first strictly derive the low-rank property of both NBIs and WBIs and then employ the robust principal component analysis (RPCA) to simultaneously suppress them. Unlike the traditional methods, the proposed method is capable to tackle with complicated interferences, not only the isolated NBIs or WBIs. The real X-band SAR data is provided to demonstrate the effectiveness of the proposed method.

Nowadays, narrowband interferences (NBIs) severely affect the imaging quality of synthetic aperture radar (SAR) systems. Fortunately, NBIs has nearly fixed frequencies along the azimuth time and they are demonstrated to be low rank in previous studies. All the NBI suppression methods are based on one-dimensional (1-D) and two-dimensional (2-D) domains to extract NBIs from the received signal. Actually, NBIs have a special low-rank property which can be employed in three-dimensional (3-D) domain for extra spacial degrees of freedom (DOFs). Hence in this paper, we propose a reweighted tensor nuclear norm minimization (RTNNM) algorithm to efficiently and effectively mitigate NBIs via three-mode tensor structure. The proposed method employs the special low-rank property of NBIs via multiple views in range-azimuth-space domain and deals with the drawback of the tensor nuclear norm minimization algorithm. The real X-band SAR data is employed to demonstrate the effectiveness and efficiency of the proposed method.

An algorithm based on optical flow is proposed to detect a ground moving target via the single-channel synthetic aperture radar. First, the signal models of uniform moving targets are established and classified into three types. Next, the Doppler spectrum is divided to generate a multilook image sequence. Then, the motion feature of a moving target response is described in the image sequence, in which the optical flow is introduced to realize the moving target detection. The detection results of real moving targets are obtained after the false alarm elimination based on the response motion relevance. This algorithm has a large range of detectable velocity and can even be applied to detect the moving targets with acceleration. In addition, compared with constant false alarm rate method, the optical flow has a better anti-interference performance against the strong static scatters. Finally, some numerical experiments are provided to demonstrate the effectiveness of the proposed method.