Space-time Adaptive Processing Research Articles

An Efficient Maritime Target Joint Detection and Imaging Method with Airborne ISAR System

In this paper, a joint maritime moving target detection and imaging approach, referred to as the fast inverse synthetic aperture radar (ISAR) imaging approach, based on the multi-resolution space−time adaptive processing (STAP), is proposed to improve the target detection performance and the target imaging efficiency in an airborne radar system. In the target detection stage, the sub-band STAP is introduced to improve the robustness of clutter suppression and to enhance the target output power with the decreased range resolution, by which the coarse estimation of target range-Doppler (R-D) location is obtained as the prior knowledge. In the following target imaging stage, the ISAR imaging is applied in the localized R-D zone surrounding with the target location. However, it is difficult to directly apply ISAR imaging with the conventional R-D algorithm because the slow-moving maritime target cannot be separated from the clutter interference in the Doppler frequency dimension. In this regard, the full-band STAP is applied in the R-D two-dimensional frequency domain for the simultaneous clutter suppression and high-resolution ISAR imaging, in which the envelope alignment and phase compensation are achieved by adaptive match filtering with the target Doppler frequency coarse estimation. Moreover, the reduced-dimension STAP applied in the target-surrounded localized Doppler frequency zone gives facilities for alleviating the computation burden. Simulation results corroborate the effectiveness of the proposed method.

Beam-Space Reduced-Dimension 3D-STAP for Nonside-Looking Airborne Radar

The space–time adaptive processing (STAP) technique has achieved good clutter suppression performance for a side-looking airborne radar; however, it suffers from severe performance degradation in nonside-looking airborne radar. This is because the clutter distribution varies considerably with range. The 3D STAP can provide better performance compared with the traditional STAP methods in such a nonstationary clutter environment, but it requires high computational complexity and sample support. In this letter, the characteristic of azimuth–elevation–Doppler 3-D beam pattern for the planar array is explored, and a novel reduced-dimension scheme combined with this characteristic is proposed. We further developed three basic reduced-dimension structures according to this scheme. Furthermore, we also prove that the first and third structures are just the direct extension of the traditional 2-D generalized multibeam and joint-domain localized methods. The second one is an original structure that performs local reduced-dimension processing with three orthogonal planar windows. Numerical results verify that the proposed structures have significant advantages in reducing computational load and training sample numbers compared with existing 3D STAP methods. In particular, the second structure shows the best comprehensive performance in the bad samples environment.

A Low-Complexity MIMO Radar STAP Strategy for Efficient Sea Clutter Suppression

When sparse reconstruction (SR) space-time adaptive processing (STAP) strategy is employed for sea clutter suppression, the computational burden imposed by the spectrum reconstruction process is still a challenge. Hence, a low-complexity MIMO radar SR STAP strategy is developed via reducing model dimension and optimizing the sparse reconstruction scheme. On one hand, STAP echo model is projected into space and time domains owing to the uncoupled relationship, thus the computation burden involved with large measurement matrix can be lessened by low-dimension structure. Meanwhile, a coprime scheme is exerted to improve the degree of freedom (DOF) in sub models and ensure lower sampling consumption. On the other hand, in order to realize efficient spectral reconstruction, an improved SR algorithm is formulated via developing a prior matrix factor and a tradeoff factor. Convergence speed of the algorithm is promoted by tuning the factors reasonably, clutter spectrum is quickly accessible with less iterations so that adaptive filter can be constructed efficiently. According to the experiment results, high-efficiency spectral reconstruction is realized and effective clutter suppression performance can be ensured.

Frequency Diverse Array Auto-Scanning Beam Characteristics and Potential Radar Applications

Different from the phased-array with the same carrier frequency for all elements providing only angle-dependent beampattern, frequency diverse array (FDA) using a small frequency offset across its array elements offers new application capabilities due to its both angle and range dependent beampattern, which allows auto beam-scanning capability even without the employment of phase shifters, which are required in phased-array. This paper studied FDA auto-scanning beam characteristics and several potential radar applications. The FDA auto-scanning beam characteristics including range and angle coupling, beam auto-scanning rate and integral array again are analyzed. To fully utilize FDA specific auto-scanning beam characteristics, several potential applications such as radio frequency (RF) stealth or RF localization deception, range-ambiguous clutter suppression and mainlobe interference suppression are discussed. Moreover, several discussions about space-time adaptive processing and possible future investigations are provided. This paper is formulated from a top-level system description, which aims to introduce this promising FDA technique to radar community so that more investigations can be facilitated.

STAP Training Samples Selection Based on GIP and Volume Cross Correlation

Space-time adaptive processing requires reliable training samples to support clutter covariance matrix calculation of the cell under test in the process of moving target detection. However, the heterogeneous clutter and the existence of outliers in the training samples violate the assumption of independent identically distributed with the cell under test, which is crucial for statistical estimation. To solve this problem, this letter suggests a new strategy to select training samples that are homogeneous with the cell under test. First, the generalized inner product method is used to filter training samples with outliers and generate the corresponding load matrix. Second, a distance metric between the training sample clutter subspace and the cell under test clutter subspace is formed by the volume cross-correlation function, the distance metric is used to judge the clutter similarity between the training sample and the cell under test. Finally, the training samples with similar clutter distribution characteristics to the cell under test are selected. Experimental results show that Space-time adaptive processing performance can be improved effectively, especially in heterogeneous environments.

A Novel Sea Clutter Rejection Algorithm for Spaceborne Multichannel Radar Systems

Due to the high-speed movement of a spaceborne radar (SBR) platform, the geographic clutter spectrum expands severely, resulting in the useful moving target signal submerged by the main-lobe clutter background. To deal with this issue, the equipped multichannel arrays in an SBR system provide sufficient spatial degrees, and as a consequence, the space-time adaptive processing (STAP) technology is often preferred to achieve the moving target detection, even in the main-lobe clutter regions. However, for the moving target detection under the sea scene, due to the complex internal motion of sea clutter, the clutter signal received by an SBR system may possess the space- and time-varying characteristics, worsening the multichannel clutter rejection performance using the traditional STAP techniques. In this article, a novel sea clutter suppression method based on the joint space-time-frequency adaptive filtering is proposed. In the proposed algorithm, according to the coherent time analysis of sea clutter, the subaperture time-domain sliding window is employed to alleviate the clutter decorrelation effect, and then, a modified subspace projection technique is applied to accomplish the first-stage clutter rejection. After realizing the effective signal recovery with respect to these residual subaperture clutter data, the second-stage spatial filtering method is applied to realize the final clutter suppression with respect to the relatively high Doppler resolution clutter returns. The effectiveness of the proposed algorithm is verified by both simulated multichannel sea clutter data and real-measured sea clutter data.

Multistage Least Squares Algorithms for Clutter Suppression in Airborne Passive Radar Based on Subband Operation

This article firstly analyzes the influence of utilizing the space-time adaptive processing (STAP) approaches in airborne passive radar (APR) systems on the dimension of the clutter subspace, and the signal-plus-noise ratio loss of the target, and illustrates that it is necessary to propose clutter cancellation algorithms suitable for APR systems. Combined with the property of APR systems, the multistage least squares algorithm based on subband operation (MLS-S) and the multistage least squares algorithm based on subband operation in temporal domain (MLS-ST) are proposed for the Doppler-shift clutter cancellation. The first method, in the case of the clutter with integer Doppler-shift bins, proves to be a simple solution. The second approach, at the expense of an additional stage, is suitable for more situations. Due to the use of Doppler-angle-dependence and subband operation to coarsen the range resolution, the proposed methods not only suppress effectively clutter along the clutter ridge, but also cancel the fractional-order-range clutter. In addition, the computational complexity of the proposed algorithms is low, and the clutter cancellation operations can be performed in parallel, which provide the possibility of real-time clutter suppression for APR systems. The feasibility of the proposed approaches is verified by diverse simulated scenarios.

A Clutter Suppression Algorithm via Weighted ${\ell }_2{\rm{ - norm}}$ Penalty for Airborne Radar

In this letter, to improve the performance of the space-time adaptive processing (STAP) filter with finite training samples, a novel algorithm with multiple measurement vectors (MMV) based on sparse recovery (SR) is proposed. Compared with traditional SR STAP algorithms, we utilize the knowledge of Capon spectrum to design a weighted <inline-formula><tex-math notation="LaTeX">${\ell }_2{\rm{ - norm}}$</tex-math></inline-formula> penalty which can better approximate the original <inline-formula><tex-math notation="LaTeX">${\ell }_0{\rm{ - norm}}$</tex-math></inline-formula>. Besides, the proposed algorithm has fast convergence performance and closed-form analytic solution in each iteration. Simulation results demonstrate the effectiveness and great performance of the proposed method.

Near-Range Clutter Suppression With Elevation Element Multifrequency Subpulse Coding Array Radar

It is hard to tackle the near-range clutter when range ambiguity exists in the non-sidelooking moving target detection (MTD) application. The angle-Doppler spectra of the far- and near-range clutter cannot be aligned simultaneously in this case due to the range dependence, which would significantly degrade the performance of the space–time adaptive processing (STAP) technology. To address this problem, an elevation element multifrequency subpulse coding (EMFSPC) array framework is proposed in this article. For each pulse duration, the main-lobe beam of the proposed framework can automatically sweep the full space by coding the subpulses and the transmitting elements, which steers the subpulses to different directions. Besides, these multiple subpulses occupy different range-frequency bands, and thus corresponding bandpass filters could be carefully designed to extract the unambiguous signals. After that, one can align the clutter spectra centers and employ the full-dimensional or dimension-reduced STAP techniques to achieve clutter cancellation. Furthermore, the simulation experiments are conducted to demonstrate the validity of the proposed EMFSPC system in near-range strong clutter suppression.

Optimally Matched Space-Time Filtering Technique for BFSAR Nonstationary Clutter Suppression

Clutter suppression in synthetic aperture radar (SAR) is one of the urgent and attractive problems in ground moving target indication (GMTI) application. With separate transmitter and receiver, ground clutter is nonstationary in bistatic forward-looking SAR (BFSAR), which directly leads to the inaccurate clutter covariance matrix (CCM) estimation. As a consequence, the traditional space-time adaptive processing (STAP) will suffer from a serious performance deterioration. In this article, an optimally matched space-time filtering (MSTF) technique is proposed to suppress nonstationary clutter for BFSAR systems. The main idea of the proposed method is to directly design and generate a suppression filter in space-time domain, whose space-time frequency response is matched with clutter spectrum. To construct the matched space-time filter, clutter modeling with arbitrary BFSAR configuration is first proposed to acquire space-time information of clutter spectrum. And then, the suppression filter can be designed and the design process is transferred into a constrained optimization problem (COP), according to the obtained clutter space-time information. Finally, the particle swarm optimization (PSO) algorithm is applied to solve the COP and obtain the optimal solution, i.e., the desired matched space-time filter weight, for BFSAR nonstationary clutter suppression. Since the generation of the designed filter circumvents CCM estimation, the proposed method will not be affected by the nonstationary characteristic of BFSAR clutter. In October 2020, the first airborne BFSAR-GMTI experiment in the world has been successfully conducted by us, and the experimental results are given to validate the effectiveness of the proposed method.

Gridless Sparse Clutter Nulling STAP Based on Particle Swarm Optimization

Sparse clutter nulling space–time adaptive processing (STAP) methods achieve superior performance in the case of limited samples, so they are suitable for nonhomogeneous clutter environments. Traditional sparse clutter nulling STAP algorithms try to estimate the clutter subspace by selecting a suitable set of space–time steering vectors (atoms) in spatial-Doppler profile grids. However, the off-grid effect is inevitable for the nonside-looking case due to the nonlinear distribution of clutter and, thus, leads to significant performance degradation. To solve this problem, a gridless sparse clutter nulling STAP algorithm based on the particle swarm optimization (PSO) named PSO-STAP is proposed in this letter. A criterion function to evaluate the suitability of atoms is first devised. By regarding the criterion as a fitness function, PSO-STAP can select atoms in gridless spatial-Doppler profile, which is more suitable to construct the accurate clutter subspace. Numerical results verified the feasibility and superiority of the proposed algorithm.

Regularized Tapered Sample Covariance Matrix

Covariance matrix tapers have a long history in signal processing and related fields. Examples of applications include autoregressive models (promoting a banded structure) or beamforming (widening the spectral null width associated with an interferer). In this paper, the focus is on high-dimensional setting where the dimension <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$p$</tex-math></inline-formula> is high, while the data aspect ratio <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n/p$</tex-math></inline-formula> is low. We propose an estimator called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tabasco</small> (TApered or BAnded Shrinkage COvariance matrix) that shrinks the tapered sample covariance matrix towards a scaled identity matrix. We derive optimal and estimated (data adaptive) regularization parameters that are designed to minimize the mean squared error (MSE) between the proposed shrinkage estimator and the true covariance matrix. These parameters are derived under the general assumption that the data is sampled from an unspecified elliptically symmetric distribution with finite 4th order moments (both real- and complex-valued cases are addressed). Simulation studies show that the proposed <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tabasco</small> outperforms all competing tapering covariance matrix estimators in diverse setups. An application to space-time adaptive processing (STAP) also illustrates the benefit of the proposed estimator in a practical signal processing setup.

Bistatic SAR Clutter-Ridge Matched STAP Method for Nonstationary Clutter Suppression

Clutter suppression is a challenging task in synthetic aperture radar-ground moving target indication (SAR-GMTI). In general, sufficient secondary samples are not easily acquired due to the nonstationary and nonhomogeneous characteristics of bistatic SAR (BiSAR) clutter, resulting in worse clutter suppression results. Recently, space–time adaptive processing based on sparse recovery (SR-STAP) has been developed since its better clutter suppression performance with less samples. However, since the off-grid problem in space–time domain caused by BiSAR’s separate configuration, existing SR-STAP would suffer from severe performance degradation. To address this problem, a clutter-ridge matched STAP (CRM-STAP) method for BiSAR nonstationary clutter suppression is proposed. First, clutter distribution modeling with arbitrary BiSAR configuration is applied to accurately obtain the clutter ridge in space–time domain. Then, keystone transform and time-division processing are applied to correct range cell migration and eliminate Doppler frequency migration, respectively. Next, to solve the off-grid problem, the CRM dictionary is reconstructed via adaptive gradient method, which is established along the direction of clutter ridge and its orthogonal direction. Then, with the constructed CRM dictionary, the clutter covariance matrix (CCM) estimation process is transformed to a multimeasured vector optimization problem, and it can be directly solved by the sparse Bayesian learning algorithm. Finally, based on the estimated CCM, the CRM-STAP filter is built to suppress the nonstationary clutter effectively. Compared with the existing STAP and SR-STAP methods, this method can avoid the performance degradation in clutter suppression caused by the off-grid problem and overcomes the strong nonstationary problem of BiSAR clutter in heterogeneous environments. In October 2020, we have successfully carried out the world’s first airborne BiSAR-GMTI experiment, and the experimental results are given to verify the effectiveness of this method.

Tensor-Based Sparse Recovery Space-Time Adaptive Processing for Large Size Data Clutter Suppression in Airborne Radar

Sparse recovery space-time adaptive processing (SR-STAP) can achieve an ideal clutter suppression with very few training samples, however, its application faces two challenges: (i) severe gird mismatch effect; (ii) large time-resources requirement. In practice, a coarse space-time grids will bring a serious mismatch between the true clutter points and the divided grids, which leads to a significant performance degradation of clutter suppression. Although the high-resolution mesh can effectively reduce the grid mismatch effect, its cost is huge computational load. Thus, it is meaningful to reduce the large-scale dictionary operation complexity while maintaining suboptimal clutter suppression performance for SR-STAP when applying in real airborne radar system. This paper proposed a tensor-based SR-STAP scheme aims at large-scale dictionary application. In the proposed framework, traditional vector-based operations are replaced by their corresponding low-complexity tensor representation. As a result, a large-scale matrix operation can be degraded into multiple small-scale matrix calculation, thus the huge computational loading can be saved in recovery. A comparison of tensor-based SR-STAP and traditional vector-based SR-STAP in large-scale dictionary application is also exhaustive discussed here. Based on this framework, a tensor-based sparse Bayesian learning and its fast matrix-realization form are developed. A series of carefully designed numerical simulation and measurement experiments indicate that the significant advantages of the tensor-based SR-STAP whether in performance or computation loading.

A General Framework for Slow and Weak Range-Spread Ground Moving Target Indication Using Airborne Multichannel High-Resolution Radar

Airborne multichannel high-resolution radar (HRR)-ground moving target indication (GMTI) is of great significance to wide-area surveillance, traffic monitoring and target recognition. The Aerospace Information Research Institute, Chinese Academy of Sciences, produced an advanced airborne digital array radar with high resolution and conducted an experiment with slow and weak cooperative moving targets in 2021. In this paper, an overall processing framework for target detection, parameter estimation and target tracking using this system is introduced. For HRR detection, the echo energy of targets is spread into multiple range units, so-called range-spread targets; thus, using detectors designed for point-like targets will severely degrade the detection performance, especially for slow and weak targets. To address the adaptive detection of such range-spread targets embedded in Gaussian clutter with an unknown covariance matrix, a novel two-step generalized space-time adaptive processing (GSTAP) algorithm is proposed, which offers an enhanced clutter suppression capability compared with natural competitors. Moreover, a tracking method is implemented in the range-Doppler domain to avoid track loss caused by azimuth relocation error and reject discrete false alarms. Both simulation and experimental results are presented to demonstrate the effectiveness of the proposed method and provide a paradigm for further research.

A Two-Stage STAP Method Based on Fine Doppler Localization and Sparse Bayesian Learning in the Presence of Arbitrary Array Errors.

In the presence of unknown array errors, sparse recovery based space-time adaptive processing (SR-STAP) methods usually directly use the ideal spatial steering vectors without array errors to construct the space-time dictionary; thus, the steering vector mismatch between the dictionary and clutter data will cause a severe performance degradation of SR-STAP methods. To solve this problem, in this paper, we propose a two-stage SR-STAP method for suppressing nonhomogeneous clutter in the presence of arbitrary array errors. In the first stage, utilizing the spatial-temporal coupling property of the ground clutter, a set of spatial steering vectors with array errors are well estimated by fine Doppler localization. In the second stage, firstly, in order to solve the model mismatch problem caused by array errors, we directly use these spatial steering vectors obtained in the first stage to construct the space-time dictionary, and then, the constructed dictionary and multiple measurement vectors sparse Bayesian learning (MSBL) algorithm are combined for space-time adaptive processing (STAP). The proposed SR-STAP method can exhibit superior clutter suppression performance and target detection performance in the presence of arbitrary array errors. Simulation results validate the effectiveness of the proposed method.

A Modified Anti-Jamming Method Using Dual-Polarized Ellipsoid Minimum Variance Distortionless Response to Predict the Coverage Ratio of Global Positioning System Signal

The dual-polarized array is considered as one of the key anti-jamming techniques due to twice counts of the jamming signals suppressed relative to a monopolar array. This paper proposes an adaptive anti-jamming dual-polarized ellipsoid minimum variance distortionless response (EMVDR) method to evaluate the carrier-to-noise ratio (<inline-formula> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula> a/<inline-formula> <tex-math notation="LaTeX">${N}_{{0}}$ </tex-math></inline-formula>) for global positioning system (GPS) coverage. Firstly in GPS L1 band, the dual-polarized gains obtained by controlled reception pattern antenna (CRPA) array are investigated as the signal model of GPS coverage evaluation. Secondly, a novel EMVDR method based the Hermitian tridiagonal loading technique is proposed to obtain the smooth anti-jamming weight matrix, which is against the system error by using the highpass filtering performance of Hermitian tridiagonal matrix. Finally, <inline-formula> <tex-math notation="LaTeX">${C}\text{a}/{N}_{{0}}$ </tex-math></inline-formula> based the proposed EMVDR is used to evaluate the coverage percentage of the GPS signal, namely <inline-formula> <tex-math notation="LaTeX">${C}\text{a}/{N}_{{0}}$ </tex-math></inline-formula> needs to be beyond a threshold of <inline-formula> <tex-math notation="LaTeX">$28~\text {dB}\cdot \text {Hz}$ </tex-math></inline-formula> by recovering GPS signal. Through many Monte Carlo simulations, the results indicate that the anti-jamming performance of proposed EMVDR method outperforms the classic minimum variance distortionless response (MVDR) based the space adaptive processing (SAP) and the space time adaptive processing (STAP), and the GPS coverage ratio can keep from 60&#x0025; to 70&#x0025; until degrees of freedom (DOFs) are exhausted to zero.

Deep learning for high‐resolution estimation of clutter angle‐Doppler spectrum in STAP

Space-time adaptive processing (STAP) methods can provide good clutter suppression potential in airborne radar systems. However, the performance of these methods is limited by the training samples' support in practical applications. To address this issue, a deep learning framework for STAP is developed. First, the clutter space-time data and their exact clutter covariance matrices (CCMs) are simultaneously modelled via simulation, in which various non-ideal factors such as aircraft crabbing, array errors, and internal clutter motion with all possible levels in practice are all considered. Then, a multi-layer two-dimensional convolutional neural network (CNN) is developed. In this CNN, low-resolution angle-Doppler profiles estimated by a few training samples are used for the input and the high-resolution counterpart obtained by the exact CCMs are utilized for the labels. Once trained, the CNN can be used to predict the high-resolution angle-Doppler profile using a few measured data in near real time. The high-resolution clutter spectrum can be further calculated using the space-time steering dictionary and the above obtained profile. Finally, the CCM of the measured data can be constructed and the space-time weight vector can also be achieved. Compared with recently developed sparsity-based STAP methods, the performance of the proposed method is better and the computational load of it is far fewer, and therefore more suitable for real-world implementation. The simulation results have demonstrated the superiority of the proposed method in both clutter suppression performance and computation efficiency.

Reduced‐rank space‐time adaptive processing algorithm based on multistage selections of angle‐Doppler filters

A new reduced-rank (RR) space-time adaptive processing (STAP) algorithm based on multistage selections of angle-Doppler filters is proposed in the form of a generalised sidelobe canceller. First, two types of the RR auxiliary angle-Doppler filters are designed based on the discrete Fourier basis functions. Then, a novel multistage method is implemented to select the angle-Doppler filters by using the cross-correlation observed at the current stage and the residual output observed at the previous stage. Finally, the asymptotic statistic performance of target detection is evaluated in terms of the conventional framework of STAP for the cases of both known and unknown covariance, which shows that the proposed algorithm is capable of providing the constant false alarm rate. Numerical examples are provided, and they demonstrate that the proposed algorithm is able to offer better performance than the existing sparsity-based and the conventional reduced-dimension algorithms under the intrinsic clutter motion and array gain and phase errors.

Multi-Resolution STAP for Enhanced Ultra-Low-Altitude Target Detection

In this paper, an ultra-low-altitude target (ULAT) detection approach, referred to as the multi-resolution space-time adaptive processing (STAP), is proposed to enhance the target detection performance in a missile-borne radar system. In this respect, the whole base band is divided into a series of equal-width and center-frequency-diverse sub-bands with the frequency diversity technique, which enhances the multipath-target coupled (MTC) effect with the decreased range resolution. Hence, it is feasible to exploit the multipath signal power to improve the output signal-to-clutter-plus-noise ratio (SCNR) performance of sub-band STAP. In this regard, the mechanism of the MTC effect is analyzed numerically for the efficient sub-band STAP. However, such SCNR improvement is achieved at the cost of target tracking performance loss. Hence, the full-band STAP is further applied for multipath-target separation based on the target range-Doppler locations detected by the joint multiple sub-bands ΣΔ-STAP, which also alleviates the dynamic target attenuation and the corresponding target Doppler history corruption within the long coherent processing interval (CPI). On this basis, the SCNR performance is further improved by applying coherent accumulation among sub-CPIs, in which the clutter suppression performance degradation and coherent accumulation loss of STAP are alleviated within the sub-CPIs. Numerical and measured results corroborate the effectiveness of ULAT detection with the considered multi-resolution STAP.