Traditional Space-time Adaptive Processing Research Articles

Isolated point clutter suppression method for airborne STAP radar in wind farm environment

With the large-scale construction of wind farms, wind turbine isolated point clutter has an increasingly serious impact on airborne radar target detection performance. Traditional space-time adaptive processing methods cannot suppress wind turbine clutter (WTC) with spectrum broadening characteristics, which may lead to a decrease in target detection probability and an increase in false alarm rate. In this paper, a WTC suppression method for airborne radar based on micro-Doppler features is proposed, and we construct the feature subspace of wind turbine echo to distinguish wind turbine, target, clutter, and noise. First, the Sobel operator is used to process the radar range-Doppler spectrum, and the range cells of the wind turbines are preliminarily judged. Then the smallest of constant false alarm rate (SOCFAR) method is used to further confirm the range cells where the wind turbines are located. Next, Mahalanobis distance is used to estimate the optimal dictionary atomic parameters of WTC, and the updated dictionary atoms are used to construct an orthogonal projection matrix to suppress WTC. Finally, short-range nonstationary clutter and sidelobe clutter are suppressed by space-time adaptive segment processing. On the one hand, the proposed method realizes the accurate positioning of wind turbines through image edge detection and constant false alarm detection. On the other hand, Mahalanobis distance is used to estimate the atomic parameters of the wind turbine dictionary, which ensures the homogeneity of wind turbine samples after clutter suppression. The simulation and measured data results show that the proposed method can significantly reduce the false alarm rate caused by WTC while ensuring the effective detection of the target.

An Efficient Sparse Recovery STAP Algorithm for Airborne Bistatic Radars Based on Atomic Selection under the Bayesian Framework

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

A novel sparse recovery space‐time adaptive processing algorithm using the log‐sum penalty to approximate the ℓ0 − norm penalty

AbstractApplying the sparse recovery (SR) technique to airborne radar space‐time adaptive processing (STAP) can greatly reduce the number of required training samples, which is advantageous in detecting targets in non‐homogeneous and non‐stationary clutter environments. However, the poor performance, the slow convergence speed or the high computational complexity of the traditional SR STAP algorithms limit their practical application. To tackle this problem, a novel efficient SR STAP algorithm is proposed. The newly proposed SR STAP algorithm utilises the log‐sum penalty to approximate the penalty, which exhibits improved convergence performance and clutter suppression performance compared to the traditional SR STAP algorithms. Besides, the proposed algorithm can ensure the convergence in each iteration by offering a closed‐form analytic solution. Furthermore, the result of the mathematical derivation demonstrates the essential equivalence between our method and the iterative reweighted method. By utilising this equivalence, two additional methods are proposed that incorporate the knowledge of the clutter Capon spectrum and the clutter spectrum of the iterative adaptive approach (IAA) as the components of weighted values, resulting in further performance improvement of the proposed algorithm. Finally, simulation results with both simulated data and Mountain‐Top data demonstrate the high effectiveness and performance of the proposed methods.

A Fast IAA–Based SR–STAP Method for Airborne Radar

Space–time adaptive processing (STAP) is an effective technology in clutter suppression and moving target detection for airborne radar. When working in the heterogeneous environment, the number of training samples that satisfy independent and identically distributed (IID) conditions is insufficient, making it difficult to ensure the estimation accuracy of the clutter plus noise covariance matrix for traditional STAP methods. Sparse recovery–based STAP (SR–STAP) methods have received widespread attention in the past few years. The accurate estimation of the clutter plus noise covariance matrix can be achieved using only a few training samples. The iterative adaptive approach (IAA) can quickly and accurately estimate the power spectrum, but applying this method directly to the STAP method cannot produce good performance. In this paper, a fast IAA–based SR–STAP method is proposed. Based on the weighted l1 problem, the IAA spectrum is used as a weighted term to obtain a good approximation. In order to obtain an analytical solution, we use the weighted l2 norm to approximate the weighted l1 norm without loss of performance. Compared with the IAA–STAP method, the proposed method is more robust to errors. Moreover, the proposed method has a fast computational speed. The effectiveness of the proposed method is demonstrated by simulations.

A Clutter Suppression Algorithm via Enhanced Sparse Bayesian Learning for Airborne Radar

The traditional space-time adaptive processing (STAP) method based on sparse Bayesian learning (SBL) has the problems of low computational efficiency and slow convergence speed. In this paper, a novel SBL approach based on statistical threshold is proposed to address these issues. To discriminate between the active and inactive atoms of the dictionary, we first develop an adaptive decision test. Next, the adaptive decision test is integrated into the SBL method, which increases its accuracy and convergence rate. Additionally, the detection threshold has an important property that is independent of signal and noise power. Therefore, the adaptive decision test is robust to external changes. Numerous simulations demonstrate that the proposed algorithm has excellent clutter suppression performance and fast convergence rate.

Range-Ambiguous Clutter Suppression for Space-Based Early Warning Radar Using Vertical FDA and Horizontal EPC

The range ambiguous clutter in space-based early warning radar is more severe compared with that in airborne radar due to the faster movement velocity. Meanwhile, the clutter angle-Doppler characteristics vary with range owing to Earth’s rotation. As a result, the mainbeam clutter returned from different ambiguous range occupies most of the Doppler spectrum, which leads to the degradation of the traditional space-time adaptive processing (STAP) performance in canceling clutter. Though frequency diverse array (FDA) based on multiple-input multiple-output can provide the ability to identify the range ambiguous clutter, it cannot perform well for space-based early warning radar because of the finite available vertical elements. In this paper, a novel STAP method, which utilizes the element-pulse coding (EPC) and FDA technique to mitigate the range ambiguous clutter, is proposed. Firstly, EPC is used to pre-whiten the most range ambiguous clutter via coding and decoding in horizontal elements and coherent pulses. Then the interval in the vertical frequency of the residual range-ambiguous clutter is expanded by FDA, and thus the targets and clutter from the vertical mainlobe are easily extracted using a spatial filter with a few vertical elements. Finally, the extracted clutter can be effectively suppressed through the traditional STAP method. Simulation results are provided to demonstrate the performance of the proposed method.

Random Matrix Theory-Based Reduced-Dimension Space-Time Adaptive Processing under Finite Training Samples

Space-time adaptive processing (STAP) is a fundamental topic in airborne radar applications due to its clutter suppression ability. Reduced-dimension (RD)-STAP can release the requirement of the number of training samples and reduce the computational load from traditional STAP, which attracts much attention. However, under the situation that training samples are severely deficient, RD-STAP will become poor like the traditional STAP. To enhance RD-STAP performance in such cases, this paper develops a novel RD-STAP algorithm using random matrix theory (RMT), RMT-RD-STAP. By minimizing the output clutter-plus-noise power, the estimate of the inversion of clutter plus noise covariance matrix (CNCM) can be obtained through optimally manipulating its eigenvalues, thus producing the optimal STAP weight vector. Specifically, the clutter-related eigenvalues are estimated according to the clutter-related sample eigenvalues via RMT, and the noise-related eigenvalue is optimally selected from the noise-related sample eigenvalues. It is found that RMT-RD-STAP significantly outperforms the RD-STAP algorithm when the RMB rule cannot be satisfied. Theoretical analyses and numerical results demonstrate the effectiveness and the performance advantages of the proposed RMT-RD-STAP algorithm.

Range Ambiguous Clutter Suppression Approach With Elevation Time Diverse Array

When range ambiguity and range dependence coexist in forward-looking ground moving target indication radar, the traditional space-time adaptive processing (STAP) approaches may fail to work. To cope with this issue, a range-ambiguous clutter suppression approach based on elevation time diverse array (TDA) radar system and azimuth phase coding technique is presented in this article. Due to the advantages of frequency scanning, the elevation TDA radar is able to cover all directions by transmitting a single time-shifted waveform. Therefore, a frequency bandpass filter is devised for each single range bin to achieve the mainlobe echo extraction procedure. In the sequel, the azimuth phase decoding and Doppler filtering are performed to deal with the residual sidelobe ambiguous energy. With two-dimensional filtering, one can extract the unambiguous signals for each range region independently. Then, the traditional clutter compensation and STAP methods are employed to suppress the ground clutter. Finally, the effectiveness of the proposed framework in resolving range ambiguity is verified by the numerical simulation experiments.

Clutter Suppression for Wideband Radar STAP

Traditional space-time (ST) adaptive processing (STAP) theory is based on the assumption of narrowband or “zero-bandwidth,” where the decorrelation within the ST snapshot is ignored. However, with radar bandwidths increasing, this assumption becomes invalid due to the deteriorated decorrelation of the received signals within the ST snapshot. The decorrelation directly causes the dispersion of the received signals in both spatial and temporal domains, leading to the spreading of the clutter spectrum in the 2-D frequency (Doppler-spatial frequency) domain. With the spreading of the clutter spectrum, the clutter suppression notch in the traditional STAP filters is widened, resulting in a relative poor ability to detect slow-moving targets. In this article, we focus on the clutter suppression for wideband radar STAP. A generalized signal model of the ground clutter is first established for the wideband array radar. Using this outcome, we analyze the influence of bandwidth on the characteristics of the ground clutter and quantitatively describe the 2-D spreading of the ground clutter on the Doppler-spatial frequency plane. Moreover, the model of clutter covariance matrix for wideband STAP (W-STAP) is established. Finally, a 2-D keystone transform (KT) algorithm, referred to as ST KT (ST-KT), is proposed to eliminate the spreading of the ground clutter in the 2-D frequency domain caused by increasing bandwidths. Simulation results are employed to validate the theoretical analysis and verify the overperformance of the ST-KT based W-STAP method in terms of the output signal-to-clutter-plus-noise ratio (SCNR) of moving targets.

Robust STAP Detection Based on Volume Cross-Correlation Function in Heterogeneous Environments

The performance of moving target detection in heterogeneous environments with the traditional space-time adaptive processing (STAP) may degrade when the real clutter environments deviate from the prior assumption on the clutter distribution. In this letter, a new detector for STAP applications based on volume cross-correlation function (VCF), namely VCF-STAP, is proposed to achieve robust performance of moving target detection in heterogeneous environments. In the new VCF-STAP, the VCF is used to form a distance measure between the sample signal subspace and the target subspace without modeling the clutter distribution. Then, a new robust STAP detection statistic is constructed using this distance measure. Simulation and experimental results show that the proposed VCF-STAP achieves robust performance of moving target detection in heterogeneous environments, especially it achieves much superior detection performance compared with existing STAP methods when the real clutter environments do not satisfy their prior assumptions. Besides, it is also shown that VCF-STAP has the constant false alarm rate (CFAR) property.

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.

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.

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 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.

Knowledge‐aided block sparse Bayesian learning STAP for phased‐array MIMO airborne radar

The phased-array multiple-input multiple-output (PA-MIMO) airborne radar faces more severe sample shortage problem than the conventional PA radar. Hence, it suffers from severe performance degradation when it adopts the traditional space-time adaptive processing (STAP) methods. Fortunately, the introduction of sparse recovery (SR) brings a novel perspective to reducing the sample requirement. However, most existing SR-STAP methods only consider the clutter sparsity as prior information. In fact, more useful information, such as radar parameters, ground environment, airplane state, can all be obtained in advance and further utilised to improve the performance of SR-STAP. In this study, the clutter structure, obtained based on the abovementioned prior knowledge, is utilised in the block SR-STAP in the PA-MIMO airborne radar. The clutter suppression performance is poor when the block SR algorithm is directly applied into the STAP because the clutter structure is destroyed by common vectorisation operation. To solve this problem, the vectorisation operation is first redesigned based on the clutter distribution characteristics and a novel dictionary is constructed. A basic block sparse Bayesian learning STAP scheme is then proposed in the PA-MIMO radar’s application. The numerical experiments illustrate that the proposed method can achieve satisfactory performance with a few training samples and outperforms the state-of-the-art SR-STAP methods.

Dense false target jamming suppression for airborne superimposed stepped frequency radar

Traditional space-time adaptive processing (STAP) suffers from severe performance degradation in dense false target jamming scenarios, especially mainlobe deceptive jamming. To solve this problem, the superimposed stepped frequency (SSF) radar is explored and its ability in suppressing the mainlobe deceptive jamming is studied in this work. SSF radar can provide a controllable degree of freedom in range domain besides the traditional freedom in angle and Doppler domains, which can be used to distinguish true and false targets. Thus, the range cascaded STAP approach is proposed to suppress the deceptive jamming and the clutter. First, the true target and clutter are blocked in carrier frequency domain. Then, the jamming is attenuated by the adaptive filter in carrier frequency domain with the jamming covariance matrix being constructed by the blocked data. At last, the clutter can be suppressed by the cascaded STAP in the space-time domain. Simulation results demonstrate the effectiveness of the proposed radar framework and the processing approach in suppressing the clutter and jamming.

A novel reduced-dimensional dictionary grid screening strategy for SR-STAP

The traditional space-time adaptive processing (STAP) methods require large training samples to estimate the clutter covariance matrix. Sparse recovery STAP (SR-STAP) can get a highly accurate estimation in the case of insufficient samples and it alleviates the above problem of conventional STAP methods, however, the price of SR-STAP is that it involves a huge computational load in optimization, especially when the input data size is very big. This paper proposed a novel SR-STAP dictionary construction method that can significantly improve computational efficiency. We introduce the noise grids as the supplement of the spectrum-aid methods and design a second grids screening method based on the prior spectrum information. After the process of the proposed method, the dimension of the space-time dictionary has a sharp decrease compared to the over-complete dictionary, moreover, the sparsity of variable can be maintained better than the common spectrum-aid methods. The numerical experiments verify that the proposed method not only can significantly reduce the optimization time to far smaller than the SR-STAP but also more robust than spectrum-aid methods even in the small input size.

A Space-Time Adaptive Processing Method Based on Sparse Reconstruction of Reverberation Interference

Aiming at the problem of how to obtain reverberation samples and estimate their covariance matrix in the space-time adaptive processing(STAP) of sonar system, a new space-time adaptive processing method is proposed based on sparse reconstruction of reverberation in this paper. Firstly, according to the space-time distribution characteristics of reverberation received by moving platform sonar, a space-time steering dictionary for sparse reconstruction of reverberation is designed along the relation curve between Doppler frequency shift and incident cone angle cosine of the reverberation unit. Then, a reverberation sample in the rangecell under test (RUT) is reconstructed with high precision by sparse decomposition of signals obtained from the sonar array in the space-time steering dictionary. Finally, based on the prior information of reverberation probability distribution model, a sufficient number of reverberation samples are generated to meet the requirement of performance loss index on reverberation sample size in the space-time adaptive processing, so as to correctly obtain estimation of the covariance matrix of reverberation. This method can reconstruct the reverberation samples and estimate the reverberation covariance matrix directly from the data in RUT without relying on the auxiliary data from units adjacent to the RUT. Therefore, it is not only suitable for the environment with constant reverberation statistical characteristics, but also suitable for the environment with varying statistical characteristics. Simulation results of sonar forward-looking array and side-looking array indicate that the improvement factor of the proposed method is about 10dB lower than the traditional space-time adaptive processing method. So this new STAP method has good anti-reverberation performance.

Iterative adaptive approach STAP algorithm based on arc antenna array

The radar with arc antenna array has the advantages of all-orientation space scanning, simultaneous estimation of pitch-azimuth angle and good antenna pattern. However, due to the special geometry of the antenna, the echoes from different range cells are not independently and identically distributed (IID) samples, resulting in performance degradation in space-time adaptive processing technology. In this paper, the model of airborne radar with arc antenna array is established, and the clutter distribution is analyzed. The conclusion is that the nonlinear variation of spatial angular frequency with array element number results in the non-uniform clutter distribution in different range cells. Therefore, the traditional space-time adaptive processing is not fit for the target detection of arc array radar. Hence, the arc array-based iterative adaptive approach (IAA) algorithm is studied. The IAA algorithm is utilized to estimate the space-time spectrum, which can distinguish clutter and targets. Simulation experiments verify the effectiveness of the IAA algorithm in arc array radar.

A Novel Covariance Matrix Estimation via Cyclic Characteristic for STAP

The accurate estimation of the clutter covariance matrix (CCM) is crucial for space-time adaptive processing (STAP). In this letter, a new intrinsic cyclic characteristic of CCM is found. Then, a novel STAP is proposed based on the cyclic characteristic. In the proposed method, the cyclic CCMs, i.e., the temporal cyclic CCM, the spatial cyclic CCM, and the spatial–temporal cyclic CCM, are first constructed based on the cyclic characteristic. Then, the cyclic CCMs are employed as the secondary data, and the more accurate CCM estimation is obtained by averaging the cyclic CCMs and the estimated CCM of the existing STAP methods. Compared with the existing methods, the proposed method has the following advantages: (1) the proposed method can be directly combined with the various existing STAP methods to improve their performance, (2) the output signal-to-clutter-plus-noise ratio (SCNR) of the proposed method is 2.055 dB higher than that of the traditional STAP methods reported in [7] – [9] , and (3) the output SCNR of the proposed method is 1.704 dB higher than that of the knowledge-aided STAP (KA-STAP) reported in [16] .