Clutter Suppression Performance Research Articles

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 Data and Model-Driven Clutter Suppression Method for Airborne Bistatic Radar Based on Deep Unfolding

Space–time adaptive processing (STAP) based on sparse recovery achieves excellent clutter suppression and target detection performance, even with a limited number of available training samples. However, most of these methods face performance degradation due to grid mismatch, which impedes their application in bistatic clutter suppression. Some gridless methods, such as atomic norm minimization (ANM), can effectively address grid mismatch issues, yet they are sensitive to parameter settings and array errors. In this article, the authors propose a data and model-driven algorithm that unfolds the iterative process of atomic norm minimization into a deep network. This approach establishes a concrete and systematic link between iterative algorithms, extensively utilized in signal processing, and deep neural networks. This methodology not only addresses the challenges associated with parameter settings in traditional optimization algorithms, but also mitigates the lack of interpretability issues commonly found in deep neural networks. Moreover, due to more rational parameter settings, the proposed algorithm achieves effective clutter suppression with fewer iterations, thereby reducing computational time. Finally, extensive simulation experiments demonstrate the effectiveness of the proposed algorithm in clutter suppression for airborne bistatic radar.

Knowledge-aided multi-dictionary block sparsity-aware STAP for airborne polarimetric conformal array radar

Airborne polarimetric conformal array radar has gained increased attention recently. However, the conformal array configuration and polarization factors may lead to non-stationary clutter, which significantly degrades the performance of space–time adaptive processing (STAP), particularly in short-range clutter environments. In this paper, we introduce a knowledge-aided multi-dictionary block sparse Bayesian learning (KA-MDBSBL) algorithm to improve the clutter suppression performance. Using prior knowledge, the proposed algorithm designs multi-dictionary matrices for each training sample, rather than a single dictionary matrix for the cell under test (CUT). We take advantage of the identical clutter profile under each dictionary matrix. In the multi-dictionary case, we enforce shared sparsity in the clutter profiles. Additionally, we utilize the inherent block structure of the dictionary matrix to jointly recover clutter and noise power through a fast convergence learning framework. Subsequently, the clutter plus noise covariance matrix is reconstructed using precisely estimated clutter and noise power, along with the dictionary matrix corresponding to the CUT. Numerical simulations are included to demonstrate the effectiveness and superiority of the proposed algorithm.

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.

Tensor structure-based ground clutter suppression approaches for pulse doppler radar

ABSTRACT For pulse doppler (PD) radar, two tensor structure-based methods are proposed to improve the clutter suppression performance. One is tensor singular value decomposition (TSVD) and the other is tensor robust principal component analysis (TRPCA). Two algorithms both stack the range-Doppler matrices of multiple coherent processing intervals (CPI) into a tensor structure, which helps make the most of the temporal information across CPIs. TSVD utilizes the strong temporal correlation of ground clutter data to estimate the clutter subspace. Meanwhile, TRPCA exactly separates both low-rank clutter component and sparse target components from the time series range-Doppler tensor structure. Experimental results indicate that compared with conventional matrix-based schemes such as singular value decomposition (SVD) or singular value decomposition (RPCA), the proposed methods have significant advantages in keeping the target echo while suppressing the clutter. In addition, the computational complexity of each algorithm is analysed in detail.

Robust SR-STAP algorithms in partly calibrated arrays for airborne radar

Sparse recovery based space-time processing (SR-STAP) techniques are capable of achieving satisfactory clutter suppression and target detection performance, even with limited training samples. The majority of existing approaches are developed under the assumption of ideal uniform linear arrays, where no imperfections are present. In this study, we consider the robust STAP problem in the context of airborne partly calibrated array (PCA) composed of several well calibrated subarrays with unknown inter-subarray gain-phase and displacement errors. Initially, the space-time signal for airborne STAP radar based on PCAs is modeled. Subsequently, the authors propose two robust SR-STAP algorithms, considering both grid-based and gridless methods, leveraging the block-sparsity and low rank structural characteristics inherent in the signal model. Extensive numerical experiments have shown the significant performance benefits including small sample applicability and resilience to inter-subarray gain-phase and displacement errors.

Beam-Space Post-Doppler Reduced-Dimension STAP Based on Sparse Bayesian Learning

The space–time adaptive processing (STAP) technique can effectively suppress the ground clutter faced by the airborne radar during its downward-looking operation and thus can significantly improve the detection performance of moving targets. However, the optimal STAP requires a large number of independent identically distributed (i.i.d) samples to accurately estimate the clutter plus noise covariance matrix (CNCM), which limits its application in practice. In this paper, we fully consider the heterogeneity of clutter in real-world environments and propose a sparse Bayesian learning-based reduced-dimension STAP method that achieves suboptimal clutter suppression performance using only a single sample. First, the sparse Bayesian learning (SBL) algorithm is used to estimate the CNCM using a single training sample. Second, a novel angular Doppler channel selection algorithm is proposed with the criterion of maximizing the output signal-to-clutter-noise ratio (SCNR). Finally, the reduced-dimension STAP filter is constructed using the selected channels. Simulation results show that the proposed algorithm can achieve suboptimal clutter suppression performance in extremely heterogeneous clutter environments where only one training sample can be used.

Meshless Search SR-STAP for Airborne Radar Based on Meta-Heuristic Algorithms

The sparse recovery (SR) space-time adaptive processing (STAP) method has excellent clutter suppression performance under the condition of limited observation samples. However, when the cluttering is nonlinear in a spatial-Doppler profile, it will cause an off-grid effect and reduce the sparse recovery performance. A meshless search using a meta-heuristic algorithm (MH) can completely eliminate the off-grid effect in theory. Therefore, genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and grey wolf optimization (GWO) methods are applied to SR-STAP for selecting exact clutter atoms in this paper. The simulation results show that MH-STAP can estimate the clutter subspace more accurately than the traditional algorithm; PSO-STAP and GWO-STAP showed better clutter suppression performance in four MH-STAP methods. To search for more accurate clutter atoms, PSO and GWO are combined to improve the method’s capacity for global optimization. Meanwhile, the fitness function is improved by using prior knowledge of the clutter distribution. The simulation results show that the improved PSO-GWO-STAP algorithm provides excellent clutter suppression performance, which solves the off-grid problem better than does single MH-STAP.

Estimation of clutter covariance matrix in stap based on knowledge-aided and geometric methods

The insufficient number of available samples can cause inaccurate estimation of the clutter covariance matrix (CCM) in space-time adaptive processing (STAP), resulting in degraded clutter suppression performance. To tackle this problem, a CCM estimation approach based on knowledge-aided (KA) and geometric methods is proposed in this paper. A combination of environmental as well as structural (Persymmetric or Symmetric structure) knowledge information is utilized to model the covariance matrix of each sample as a knowledge-aided Hermitian positive definite (KA-HPD) covariance matrix. The estimation problem is introduced into the Riemannian manifold composed of the KA-HPD covariance matrices, and the geometric method is used for nonlinear processing. Based on the Kullback-Leibler (KL) divergence and the KL mean, the final estimated CCM is designed as a weighted combination of each KA-HPD covariance matrix. Experiment results show that the two designed structural covariance matrix estimators possess superior clutter suppression performance.

Improved Ultrafast Power Doppler Imaging Using United Spatial-Angular Adaptive Scaling Wiener Postfilter.

Ultrafast power Doppler imaging (uPDI) using high-frame-rate plane-wave transmission is a new microvascular imaging modality that offers high Doppler sensitivity. However, due to the unfocused transmission of plane waves, the echo signal is subject to interference from noise and clutter, resulting in a low signal-to-noise ratio (SNR) and poor image quality. Adaptive beamforming techniques are effective in suppressing noise and clutter for improved image quality. In this study, an adaptive beamformer based on a united spatial-angular adaptive scaling Wiener (uSA-ASW) postfilter is proposed to improve the resolution and contrast of uPDI. In the proposed method, the signal power and noise power of the Wiener postfilter are estimated by uniting spatial and angular signals, and a united generalized coherence factor (uGCF) is introduced to dynamically adjust the noise power estimation and enhance the robustness of the method. Simulation and in vivo data were used to verify the effectiveness of the proposed method. The results show that the uSA-ASW can achieve higher resolution and significant improvements in image contrast and background noise suppression compared with conventional delay-and-sum (DAS), coherence factor (CF), spatial-angular CF (SACF), and adaptive scaling Wiener (ASW) postfilter methods. In the simulations, uSA-ASW improves contrast-to-noise ratio (CNR) by 34.7 dB (117.3%) compared with DAS, while reducing background noise power (BNP) by 52 dB (221.4%). The uSA-ASW method provides full-width at half-maximum (FWHM) reductions of [Formula: see text] (59.5%) and [Formula: see text] (56.9%), CNR improvements of 25.6 dB (199.9%) and 42 dB (253%), and BNP reductions of 46.1 dB (319.3%) and 12.9 dB (289.1%) over DAS in the experiments of contrast-free human neonatal brain and contrast-free human liver, respectively. In the contrast-free experiments, uSA-ASW effectively balances the performance of noise and clutter suppression and enhanced microvascular visualization. Overall, the proposed method has the potential to become a reliable microvascular imaging technique for aiding in more accurate diagnosis and detection of vascular-related diseases in clinical contexts.

Effective Non-Stationary Clutter Suppression Method via Elevation Oblique Subspace Projection for Moving Targets Detection with a Space-Based Surveillance Radar

The clutter becomes non-stationary for a space-based surveillance radar (SBSR), which is harmful for the moving targets detection due to the earth’s rotation. The non-stationarity will degrade the accuracy of clutter covariance matrix (CCM) estimation and increase the clutter degree of freedom (DOF), thereby degrading the performance of clutter suppression. To solve this problem, this paper proposes a novel non-stationary clutter suppression method using an elevation oblique subspace projection method. After analyzing the range ambiguity and non-stationarity of the clutter, the proposed method utilized the oblique projection matrix to project the signal onto the subspace spanned by the near-range and far-range clutter components along the subspace spanned by the main lobe clutter component. Then, the projected signal was used to estimate the elevation covariance matrix and calculate the optimal weight vector for the elevation adaptive filter. The proposed method can suppress the non-stationary clutter effectively with a higher improvement factor (IF) and a narrower main lobe width. Finally, the simulation results were given to verify the correctness and effectiveness of the proposed method.

A novel sparse recovery‐based space‐time adaptive processing algorithm based on gridless sparse Bayesian learning for non‐sidelooking airborne radar

AbstractNon‐sidelooking airborne radar encounters significant non‐stationary and heterogeneous clutter environments, resulting in a severe shortage of samples. Sparse recovery‐based space‐time adaptive processing (SR‐STAP) methods can achieve good clutter suppression performance with limited samples. Nonetheless, grid‐based SR‐STAP algorithms encounter off‐grid effects in non‐sidelooking arrays, which can severely degrade the clutter suppression performance. In this study, the authors propose a novel gridless SR‐STAP method in the continuous spatial‐temporal domain to address the issue of off‐grid effects. Inspired by the fact that sparse Bayesian learning (SBL) framework implicitly performs a structured covariance matrix estimation, the authors reparameterise its cost function to directly estimate the block‐Toeplitz structured matrix from the measurements in a gridless manner. Since the proposed cost function is non‐convex, we utilise a majorisation‐minimisation‐based iterative procedure to estimate the clutter covariance matrix. Finally, using the standard concept of semidefinite programming, the authors derive a convex gridless implementation of the SBL cost function for uniformly sampled radar systems. Extensive simulation experiments demonstrate the exceptional clutter suppression and target detection performance of the proposed algorithm.

A novel gridless space-time adaptive processing method based cyclic minimization

Space-time adaptive processing (STAP) algorithm based on sparse recovery (SR) can effectively suppress clutter in limited sample scenarios. However, the clutter suppression performance will be reduced because of grid mismatch (off-grid), which happens when the clutter ridge fails to match the dictionary. To address this issue, a novel gridless STAP algorithm based on cyclic minimization is proposed in this paper. Compared with the STAP algorithm based on atomic norm minimization (ANM), the proposed algorithm avoids the approximate loss caused by relaxing the rank norm to the nuclear norm. In addition, in order to improve the computational efficiency, we derive a fast iterative structure based on the alternating direction method of multipliers (ADMM) framework. The proposed algorithm offers better clutter suppression performance and excellent computational efficiency, according to a vast collection of experiments based on simulation data and measured data.

A sea clutter suppression algorithm for over-the-horizon radar based on dictionary learning and subspace estimation

Detecting a ship target by over-the-horizon radar (OTHR) is seriously affected by sea clutter. This paper proposes a clutter suppression algorithm based on dictionary learning and subspace estimation (DLSE). First, the K-singular value decomposition (K-SVD) is used for dictionary learning and sparse representation of the sea clutter. Then, the orthogonal matching pursuit (OMP) is used to reconstruct the sea clutter component to suppress the outliers. Finally, the clutter covariance matrix is calculated using the reconstructed clutter spectrum, and the clutter subspace of the covariance matrix is estimated to achieve clutter suppression. In this paper, the K-SVD is improved to preserve the phase information of the complex signal. The sea clutter spectrum, after sparse representation and reconstruction, is not impacted by outliers in neighboring cells. Therefore, the clutter covariance matrix has a better estimate accuracy than existing subspace decomposition algorithm. The adaptive parameter estimate method also enhances the adaptability of the DLSE. The experimental results of real data demonstrate that the DLSE is as sophisticated as existing subspace decomposition algorithms while having a higher output signal-to-clutter-plus-noise ratio (SCNR) and peak-to-side lobe ratio (PSLR), improving about 2-5 dB. Results of outlier suppression attest to the robustness of the DLSE. Under various coherent integration times (CIT) and frequencies, the DLSE has the best clutter suppression performance among the comparison algorithms.

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.

Dimensionality reduction bistatic radar clutter suppression technique based on three-dimensional cross-beam

Aiming at the problem that the non-stationary of airborne bistatic radar clutter reduces the performance of traditional clutter suppression algorithm, this paper first formulates airborne bistatic radar clutter model, and then analyzes the bistatic radar clutter characteristics in four typical combat scenarios. In addition, in order to reduce the computation burden of the STAP algorithm, a three-dimensional cross-beam clutter suppression algorithm is proposed, which uses the transformation matrix to convert the space-time two-dimensional data into azimuth-elevation-Doppler three-dimensional data. By eliminating auxiliary beam clutter data, only the main beam clutter data which plays a major role in dual-basis clutter suppression is retained to form a three-dimensional cross-beam for local adaptive clutter suppression. The proposed algorithm can greatly reduce the amount of calculation and the demand of training samples, while trying to ensure that the clutter suppression performance does not degrade. The effectiveness of the proposed algorithm is verified by analyzing the clutter suppression effect and computation amount of various algorithms in typical combat scenarios. Therefore, the algorithm has great potential in the applications of airborne bistatic and non-stationary clutter suppression.

An adaptive beamformer based on dynamic phase coherence factor for pixel-based medical ultrasound imaging.

Pixel-based beamforming realizes dynamic focusing at the pixel level with a focused beam by assuming that the received signals are composed of spherical pulses. Far-focused pixel-based (FPB) imaging was proposed to avoid artifacts around the focal depth. However, the contrast improvement is limited. We propose an adaptive weighting method based on dynamic phase coherence factor (DPCF) to improve the image contrast while preserving the speckle pattern. The phase variation is dynamically estimated based on the noise energy proportion of echo signals and it is used to calculate phase coherence weights for suppressing interference and preserving desired signals. A depth-dependent parameter is designed for DPCF to enhance the performance of noise and clutter suppression in the far-field region. We further use the subarray averaging technique to smooth the speckle texture. The proposed method was evaluated on simulated, phantom experimental, and in vivo data. Results show that, compared with the phase coherence factor (PCF) based method, DPCF respectively leads to average CR improvements by more than 60% and 24% in simulation and experiment, while obtaining an improved speckle signal-to-noise ratio. The proposed method is a potentially valuable approach to obtaining high-quality ultrasound images in clinical applications.

Joint Transmit Waveform and Reflection Design for RIS-Assisted MIMO Radar Systems

In this letter, we investigate to deploy multiple reconfigurable intelligent surfaces (RISs) in multi-input multi-output (MIMO) radar systems for achieving better clutter suppression and multi-target detection performance. In particular, the space-time transmit waveform and receive filter of the radar and the reflection coefficients of the RISs are jointly optimized to maximize the minimum radar signal-to-interference-plus-noise ratio (SINR) under the constant-modulus power constraint and the restrict of RISs. We develop an efficient alternating algorithm to solve the resulting complicated non-convex problem. Simulation studies illustrate the advantages of deploying RISs in MIMO radar systems and the effectiveness of the proposed algorithm.

Spread sea clutter suppression via prior knowledge‐based space time adaptive processing in high frequency hybrid sky‐surface wave radar

AbstractIn high frequency (HF) hybrid sky‐surface wave radar, the first‐order sea clutter broadening is severe under the action of ionospheric phase disturbance and bistatic angles. In this study, a spread sea clutter suppression method based on prior knowledge and joint domain localised (JDL) algorithm is proposed. Firstly, the first‐order sea clutter broadening mechanisms and model under the action of ionospheric phase disturbance and bistatic angles are introduced. Secondly, the space‐time coupling characteristics of the first‐order sea clutter is analysed. Then in order to better suppress clutter or avoid target information loss, the spread sea clutter suppression method based on space time adaptive processing is proposed. In the proposed method, the space‐time distribution characteristics and simulation model of the first‐order sea clutter is used as the prior knowledge into JDL algorithm to improve the sea clutter suppression performance. Finally, the performance of sea clutter suppression based on JDL algorithm is simulated and analysed. And the proposed spread sea clutter suppression method based on prior knowledge and JDL algorithm is proved by measured data.

A clutter suppression algorithm via subspace‐weighted mixed‐norm minimisation

AbstractSpace‐time adaptive processing (STAP) struggles to effectively suppress clutter in the heterogeneous clutter environment due to the lack of training samples. In order to enhance clutter suppression performance of STAP, a subspace‐weighted mixed‐norm minimisation approach is given. First, a roughly estimated clutter subspace is obtained using the subspace augment (SA) approach. The weight vector is then designed using the association between the dictionary matrix and the noise subspace, allowing the algorithm to penalise sparse coefficients democratically. Finally, in order to solve the subspace‐weighted mixed‐norm minimisation problem, we derive a fast algorithm based on the alternating direction multiplier method (ADMM) framework. The proposed algorithm does not require iteratively updating the weight vector in contrast to the iterative re‐weighted (IRL1) algorithm. The simulation results demonstrate the effectiveness of the proposed algorithm in terms of computational efficiency and clutter suppression performance.