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The extensive deployment of wind farms has significantly impacted the detection capabilities of space–air bistatic radar (SABR) systems. Although space–time adaptive processing techniques are available, their performance is significantly degraded, and even unable to suppress clutter. This paper explores the geometric configuration of the SABR system and the selection of detection areas, establishing a space–time clutter model that addresses the effects of wind turbine clutter (WTC). Expressions for spatial and Doppler frequencies have been derived to deeply analyze the characteristics of clutter spreading. Building on this, the paper extends two-dimensional space–time data to three-dimensional azimuth–elevation–Doppler data. It proposes a three-dimensional space–time multi-beam (STMB) strategy incorporating the Ordering Points to Identify the Clustering Structure (OPTICS) clustering algorithm to suppress WTC effectively. This algorithm selects WTC samples and applies OPTICS clustering to the clutter-suppressed data to achieve this effect. Simulation experiments further verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In space-time adaptive processing (STAP), the coprime sampling structure can obtain better clutter suppression capabilities at a lower hardware cost than the uniform linear sampling structure. However, in practical applications, the performance of the algorithm is often limited by the number of training samples. To solve this problem, this paper proposes a fast iterative coprime STAP algorithm based on truncated kernel norm minimization (TKNM). This method establishes a virtual clutter covariance matrix (CCM), introduces truncated kernel norm regularization technology to ensure the low rank of the CCM, and transforms the non-convex problem into a convex optimization problem. Finally, a fast iterative solution method based on the alternating direction method is presented. The effectiveness and accuracy of the proposed algorithm are verified through simulation experiments. [ABSTRACT FROM AUTHOR]

Sparse recovery space–time adaptive processing (SR-STAP) technology improves the moving target detection performance of airborne radar. However, the sparse recovery method with a fixed dictionary usually leads to an off-grid effect. This paper proposes a STAP algorithm for airborne radar based on dictionary and clutter power spectrum joint correction (DCPSJC-STAP). The algorithm first performs nonlinear regression in a non-stationary clutter environment with unknown yaw angles, and it corrects the corresponding dictionary for each snapshot by updating the clutter ridge parameters. Then, the corrected dictionary is combined with the sparse Bayesian learning algorithm to iteratively update the required hyperparameters, which are used to correct the clutter power spectrum and estimate the clutter covariance matrix. The proposed algorithm can effectively overcome the off-grid effect and improve the moving target detection performance of airborne radar in actual complex clutter environments. Simulation experiments verified the effectiveness of this algorithm in improving clutter estimation accuracy and moving target detection performance. [ABSTRACT FROM AUTHOR]

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 l 1 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 l 2 norm to approximate the weighted l 1 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. [ABSTRACT FROM AUTHOR]

A sparse dictionary reconstruction algorithm based on grid selection is introduced to solve the grid mismatch when using the sparse recovery space time adaptive processing (SR-STAP) algorithm. First, the atom most closely related to clutter is selected from the traditional dictionary through the spectral value dimensionality reduction method. The local mesh is divided around the selected atoms to create mesh cells, and the mesh cells that are most likely to appear in the real clutter points are judged according to the local selection iteration criteria. In this way, the mesh spacing is refined, the local mesh selection is carried out step by step, and the optimal atoms in the local region are constantly adjusted and selected to narrow the search region until the iteration termination condition is met. Finally, the space-time plane is divided using a novel meshing technique that centers around the optimal atom. By removing atoms beyond the maximum range of spatial and Doppler frequencies, the simplified sparse dictionary can overcome the mesh mismatch problem. The simulation results demonstrate that the algorithm enhances the sparse recovery accuracy of clutter space-time spectrum, mitigates the mesh mismatch effect, and boosts STAP performance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Space-time adaptive processing (STAP) approaches based on sparse Bayesian learning (SBL) have attracted much attention for the benefit of reducing the training samples requirement and accurately recovering sparse signals. However, it has the problem of a heavy computational burden and slow convergence speed. To improve the convergence speed, the variational Bayesian inference (VBI) is introduced to STAP in this paper. Moreover, to improve computing efficiency, a fast iterative algorithm is derived. By constructing a new atoms selection rule, the dimension of the matrix inverse problem can be substantially reduced. Experiments conducted on the simulated data and measured data verify that the proposed algorithm has excellent clutter suppression and target detection performance. [ABSTRACT FROM AUTHOR]

Space-time adaptive processing (STAP) is an important method of clutter suppression that requires adequate training samples. For an airborne conformal array radar, conventional STAP methods do not have enough training samples to acquire good performance due to the range dependent clutter caused by geometry and the problem of polarization. Sparse-recovery-based STAP (SR-STAP) methods have garnered significant attention in the past few decades because they only require a small number of training samples. Sparse Bayesian Learning (SBL) methods have seen increasing amounts of development due to its robust, self-regularizing nature and because it is not sensitive to user parameters, but it converges slowly. In this paper, a novel fast SBL (NFSBL) method is put forward to increase the rate of convergence. To minimize the SBL penalty function, the proposed method introduces the conjugate function to construct a surrogate function. Additional solution sparsity will be achieved through iteratively minimizing the surrogate function. Then, from the proposed method, we could obtain a more accurate clutter plus noise covariance matrix. Numerical simulation results express that this method could acquire better performance of STAP and improvement in convergence and computational complexity for a conformal array. [ABSTRACT FROM AUTHOR]

In this article, a new statistical method for estimating the clutter covariance matrix in space–time adaptive radar signal processing (STAP) is presented and studied. The new method was designed for multiple-input–multiple-output (MIMO) radar with time division multiplexing (TDM). An extensive analysis of statistical and non-statistical methods for estimating the clutter covariance matrix in STAP is presented in this paper. In addition, the STAP algorithm for the standard statistical SMI clutter covariance matrix estimation method, which is based on QR distribution, has been presented. The new method is based on LU distribution with partial pivoting. Simulation results confirm the validity of the presented model and theoretical assumptions. In addition, more accurate object detection results were demonstrated for specific computational examples than for other statistical methods. Considering the current analysis of the literature, it is noted that attention has now been focused worldwide on the study of non-statistical methods for estimating clutter covariance matrices in heterogeneous environments. Hence, it should be emphasized that the posted study fills a gap in current research on STAP. [ABSTRACT FROM AUTHOR]

This paper proposes a cooperated range ambiguous clutter suppression method for moving target detection in the background of range-ambiguous clutter with a phased array (PA)–frequency diverse array (FDA) dual-mode radar. With the FDA mode, the range-ambiguous clutters are discriminated in the transmit spatial frequency domain, and thus the clutter covariance matrixes (CCMs) corresponding to unambiguous and ambiguous regions can be independently estimated. Therefore, the enhanced CCM can be reconstructed by using a linear combination of these distinguished CCMs from different range regions. With the PA mode, the enhanced CCM is applied, thus taking advantages of its high beampattern gain as well as alleviating the range ambiguous clutter suppression problem. Simulation results are presented to verify the effectiveness of the proposed method in range-ambiguous clutter scenarios. [ABSTRACT FROM AUTHOR]

In order to deal with the problem space-time adaptive processing (STAP) performance degradation of an airborne phased array system caused by the serious shortage of independent and identical distributed (IID) training samples in the nonhomogeneous clutter environment, an improved direct data domain method based on sparse Bayesian learning is proposed in this paper, which only uses a single snapshot data of a cell under test (CUT) to suppress the clutter and has fast computational speed. Firstly, three hyper-parameters required to obtain the sparse solution are derived. Secondly, the comparative analysis of their iterative formulas is made, and the piecewise iteration of hyper-parameter that has an obvious influence on the computational complexity of obtaining sparse solution is presented. Lastly, with the approximate prior information of the target, the clutter sparse solution is given and its covariance matrix is effectively estimated to calculate the adaptive filter weight and realize the clutter suppression. Simulation results verify that the proposal can dramatically decrease the computational burden while keeping the superior heterogeneous clutter suppression performance. [ABSTRACT FROM AUTHOR]

In this paper, a low-altitude wind speed estimation method based on the fuselage frustum conformal array system is proposed. Firstly, based on the signal model of the fuselage conformal array radar, the four-dimensional joint phase compensation of the echo data in the Doppler domain and three-dimensional space-frequency domain is performed by using the four-dimensional frequency domain compensation method. Secondly, the clutter covariance matrix is estimated by the compensated echo data, and a space-time Adaptive Processing (STAP) processor suitable for low-altitude windshear target is constructed to suppress clutter. Finally, the maximum Doppler value of each distance cell is extracted, and the wind velocity is estimated. Simulation results show that the proposed method can effectively suppress clutter and accurately estimate wind speed. [ABSTRACT FROM AUTHOR]

In recent years, sparse recovery-based space-time adaptive processing (SR-STAP) technique has exhibited excellent performance with insufficient samples. Sparse Bayesian learning algorithms have received considerable attention for their remarkable and reliable performance. Its implementation in large-scale radar systems is however hindered by the overwhelming computational load and slow convergence speed. This paper aims to address these drawbacks by proposing an improved iterative reweighted sparse Bayesian learning algorithm based on expansion-compression variance-components (ExCoV-IIR-MSBL). Firstly, a modified Bayesian probabilistic model for SR-STAP is introduced. Exploiting the intrinsic sparsity prior of the clutter, we divide the space-time coefficients into two parts: the significant part with nontrivial coefficients and the irrelevant part with small or zero coefficients. Meanwhile, we only assign independent hyperparameters to the coefficients in the significant part, while the remaining coefficients share a common hyperparameter. Then the generalized maximum likelihood (GML) criterion is adopted to classify the coefficients, ensuring both accuracy and efficiency. Hence, the parameter space in Bayesian inference will be significantly reduced, and the computational efficiency can be considerably promoted. Both theoretical analysis and numerical experiments validate that the proposed algorithm achieves superior performance with considerably improved computational efficiency in sample shortage scenarios. [ABSTRACT FROM AUTHOR]

Clutter suppression is a key problem for airborne radar, and space-time adaptive processing (STAP) is a core technology for clutter suppression and moving target detection. However, in practical applications, the non-uniform time-varying environments including clutter range dependence for non-side-looking radar lead to the training samples being unable to satisfy the sample requirements of STAP that they should be independent identical distributed (IID) and that their number should be greater than twice the system's degree of freedom (DOF). The lack of sufficient IID training samples causes difficulty in the convergence of STAP and further results in a serious degeneration of performance. To overcome this problem, this paper proposes a novel autoencoder neural network for clutter suppression with a unique matrix designed to be decoded and encoded. The main challenges are improving the accuracy of the estimation of the clutter-plus-noise covariance matrix (CNCM) for STAP convergence, designing the form of the data input to the network, and making the network successfully explored to the improvement of CNCM. For these challenges, the main proposed solutions include designing a unique matrix with a certain dimension and a series of covariance data selections and matrix transformations. Consequently, the proposed method compresses and retains the characteristics of the covariances, and abandons the deviations caused by the non-uniformity and the deficiency of training samples. Specifically, the proposed method firstly develops a unique matrix whose dimension is less than half of the DOF, meanwhile, it is based on a processing of the selected clutter-plus-noise covariances. Then, an autoencoder neural network with l 2 regularization and the sparsity regularization is proposed for the unique matrix to be decoded and encoded. The training of the proposed autoencoder can be achieved by reducing the total loss function with the gradient descent iterations. Finally, an inverted processing for the autoencoder output is designed for the reconstruct ion of the clutter-plus-noise covariances. Simulation results are used to verify the effectiveness and advantages of the proposed method. It performs obviously superior clutter suppression for both side-looking and non-side-looking radars with strong clutter, and can deal with the insufficient and the non-uniform training samples. For these conditions, the proposed method provides the relatively narrowest and deepest IF notch. Furthermore, on average it improves the improvement factor (IF) by 10 dB more than the ADC, DW, JDL, and original STAP methods. [ABSTRACT FROM AUTHOR]

Airborne distributed coherent aperture radar is of great significance for expanding the detection capability of the system. However, the extra observation dimension introduced by its sparse configuration also deteriorates the performance of traditional adaptive processing in a non-uniform environment. This paper focuses on moving target detection when the system works in a clutter–jamming-coexisting environment. In order to make full use of the specific low-rank structure to reduce the requirement for training data, this paper proposes a two-stage adaptive scheme that cancels jamming and clutter separately. The proposed suppression scheme first excludes the mainlobe jamming component from the training data based on the prior clutter subspace projection and performs intra-node clutter suppression. Then, the remaining jamming is jointly canceled based on the covariance obtained with its inter-pulse mixture model. Numerical examples show that this scheme can effectively reduce the blocking effect of main lobe jamming on high-speed targets but, due to the inaccuracy of the prior subspace, there is a certain additional loss of signal-to-noise ratio for near stationary targets. The simulation also shows that the proposed scheme is equally applicable to systems with a time-varying distributed geometry. [ABSTRACT FROM AUTHOR]

In order to study the space-time characteristics of sea clutter, the sea clutter is always measured by the airborne multichannel radar; however, the sea clutter shows the heterogeneity between range gates, which means the space-time covariance matrix's correspondence to the single range gate cannot be estimated accurately. Meanwhile, the measurement of the sea clutter data by the airborne radar is usually affected by the motion of the platform, which makes the analysis results unrepresentative of the space-time characteristics of the pure sea clutter. In this paper, a sea clutter measurement method based on L-band shore-based multichannel radar is proposed, where the transmit sub-array periodically moves with the pulse repetition period to obtain multiple sets of coherent processing interval pulses for each range gate. This measurement method can exclude the influences of the moving platform. Moreover, a sea clutter space-time signal model of the single range gate is proposed, and the model is used to simulate three-dimensional sea clutter data with space-time coupling characteristics. With verification of the measured and simulated data, it can be seen that the data composed of single range gate and multiple coherent processing interval pulses can accurately estimate the space-time covariance matrix corresponding to this single range gate. Furthermore, the space-time characteristics are analyzed based on the measured data. The results show that the eigenvalue spectrum and the spread width of space-time power spectrum are influenced by the backscattering coefficient of sea clutter and the speed of sea surface motion. In comparison, the decorrelation effect caused by the backscattering coefficient of sea clutter is stronger than that caused by the speed of the surface motion. The proposed method is helpful for guiding multichannel sea clutter measurement and the analysis results are of great significance to the clutter suppression algorithms of the marine multichannel radar. [ABSTRACT FROM AUTHOR]

Space-time adaptive processing (STAP) is a well-known technique for slow-moving target detection in the clutter spreading environment. For an airborne conformal array radar, conventional STAP methods are unable to provide good performance in suppressing clutter because of the geometry-induced range-dependent clutter, non-uniform spatial steering vector, and polarization sensitivity. In this paper, a knowledge aided STAP method based on sparse learning via iterative minimization (SLIM) combined with Laplace distribution is proposed to improve the STAP performance for a conformal array. The proposed method can avoid selecting the user parameter. the proposed method constructs a dictionary matrix that is composed of the space-time steering vector by using the prior knowledge of the range cell under test (CUT) distributed in clutter ridge. Then, the estimated sparse parameters and noise power can be used to calculate a relatively accurate clutter plus noise covariance matrix (CNCM). This method could achieve superior performance of clutter suppression for a conformal array. Simulation results demonstrate the effectiveness of this method. [ABSTRACT FROM AUTHOR]

Detecting a moving target is an attractive topic in many fields, such as remote sensing. Space-time adaptive processing (STAP) plays a key role in detecting moving targets in strong clutter backgrounds for airborne early warning radar systems. However, STAP suffers serious clutter suppression performance loss when the number of training samples is insufficient due to the inhomogeneous clutter environment. In this article, an efficient sparse recovery STAP algorithm is proposed. First, inspired by the relationship between multiple sparse Bayesian learning (M-SBL) and subspace-based hybrid greedy algorithms, a new optimization objective function based on a subspace penalty is established. Second, the closed-form solution of each minimization step is obtained through the alternating minimization algorithm, which can guarantee the convergence of the algorithm. Finally, a restart strategy is used to adaptively update the support, which reduces the computational complexity. Simulation results show that the proposed algorithm has excellent performance in clutter suppression, convergence speed and running time with insufficient training samples. [ABSTRACT FROM AUTHOR]

When the transmitter is in motion, the airborne passive bistatic radar (PBR) has a complex clutter geometry and lacks independent and identically distributed training samples in clutter estimation and suppression. In order to solve these problems, this paper proposes a space–time adaptive processing (STAP) algorithm based on root off-grid sparse Bayesian learning. The proposed algorithm first models the space–time base of the dictionary as an adjustable state. Then, the positions of those dynamic bases are optimized by iterating a maximum expectation algorithm. In this way, the off-grid error in clutter estimation can be eliminated even when the modeling grid is wide. To further improve the accuracy of clutter estimation, the proposed algorithm eliminates the error caused by samples with singular values in the root off-grid sparse Bayes learning by artificially adding pseudorandom noise and using hypothesis testing. The simulation results show that the proposed algorithm achieves better performance than the existing algorithms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Sparse Bayesian learning-based space–time adaptive processing (SBL-STAP) algorithms can achieve superior clutter suppression performance with limited training sample support in practical heterogeneous and non-stationary clutter environments. However, when the system has high degrees of freedom (DOFs), SBL-STAP algorithms suffer from high computational complexity, since the large-scale matrix calculations and the inversion operations of large-scale covariance matrices are involved in the iterative process. In this article, we consider a computationally efficient implementation for SBL-STAP algorithms. The efficient implementation is based on the fact that the covariance matrices that need to be updated in the iterative process of the SBL-STAP algorithms have a Hermitian Toplitz-block-Toeplitz (HTBT) structure, with the result being that the inverse covariance matrix can be expressed in closed form by using a special case of the Gohberg–Semencul (G-S) formula. Based on the G-S-type factorization of the inverse covariance matrix and the structure of the used dictionary matrix, we can perform almost all operations in the SBL-STAP algorithms by 2-D FFT/IFFT. As a result, compared with the original SBL-STAP algorithms, even for moderate data sizes, the proposed algorithms can directly reduce the computational load by about two orders of magnitudes without any performance loss. Finally, simulation results validate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Space-time adaptive processing (STAP) algorithms based on sparse recovery (SR) have been researched because of their low requirement of training snapshots. However, once some portion of clutter is not located on the grids, i.e., off-grid problems, the performances of most SR-STAP algorithms degrade significantly. Reducing the grid interval can mitigate off-grid effects, but brings strong column coherence of the dictionary, heavy computational load, and heavy storage load. A sparse Bayesian learning approach is proposed to mitigate the off-grid effects in the paper. The algorithm employs an efficient sequential addition and deletion of dictionary atoms to estimate the clutter subspace, which means that strong column coherence has no effect on the performance of the proposed algorithm. Besides, the proposed algorithm does not require much computational load and storage load. Off-grid effects can be mitigated with the proposed algorithm when the grid-interval is sufficiently small. The excellent performance of the novel algorithm is demonstrated on the simulated data. [ABSTRACT FROM AUTHOR]

Space-time adaptive processing (STAP) is an effective technology in clutter suppression and moving target detection for airborne radar. Because airborne radar moves at a constant acceleration, and there is a lack of independent and identically distributed (IID) training samples caused by the heterogeneous environment, using the conventional STAP methods directly cannot ensure a good performance. To eliminate these effects and improve the performance of clutter suppression, a STAP method based on a sparse Bayesian learning (SBL) framework for uniform acceleration radar is proposed here. This paper introduces the signal model of the uniform acceleration radar. To promote the sparsity, a generalized double Pareto (GDP) prior is introduced into our method, and the estimation of hyper parameters via expectation maximization (EM) is given. The effectiveness of the proposed method is demonstrated by simulations. [ABSTRACT FROM AUTHOR]

Space-time adaptive processing (STAP) encounters severe performance degradation with insufficient training samples in inhomogeneous environments. Sparse Bayesian learning (SBL) algorithms have attracted extensive attention because of their robust and self-regularizing nature. In this study, a computationally efficient SBL STAP algorithm with adaptive Laplace prior is developed. Firstly, a hierarchical Bayesian model with adaptive Laplace prior for complex-value space-time snapshots (CALM-SBL) is formulated. Laplace prior enforces the sparsity more heavily than Gaussian, which achieves a better reconstruction of the clutter plus noise covariance matrix (CNCM). However, similar to other SBL-based algorithms, a large degree of freedom will bring a heavy burden to the real-time processing system. To overcome this drawback, an efficient localized reduced-dimension sparse recovery-based space-time adaptive processing (LRDSR-STAP) framework is proposed in this paper. By using a set of deeply weighted Doppler filters and exploiting prior knowledge of the clutter ridge, a novel localized reduced-dimension dictionary is constructed, and the computational load can be considerably reduced. Numerical experiments validate that the proposed method achieves better performance with significantly reduced computational complexity in limited snapshots scenarios. It can be found that the proposed LRDSR-CALM-STAP algorithm has the potential to be implemented in practical real-time processing systems. [ABSTRACT FROM AUTHOR]

In high-frequency surface wave radar (HFSWR) systems, clutter is a common phenomenon that causes objects to be submerged. Space-time adaptive processing (STAP), which uses two-dimensional data to increase the degrees of freedom, has recently become a crucial tool for clutter suppression in advanced HFSWR systems. However, in STAP, the pattern is distorted if a clutter component is contained in the main lobe, which leads to errors in estimating the target angle and Doppler frequency. To solve the main-lobe distortion problem, this study developed a clutter-suppression method based on beam reshaping (BR). In this method, clutter components were estimated and maximally suppressed in the side lobe while ensuring that the main lobe remained intact. The results of the proposed algorithm were evaluated by comparison with those of standard STAP and sparse-representation STAP (SR-STAP). Among the tested algorithms, the proposed BR algorithm had the best suppression performance and the most accurate main-lobe peak response, thereby preserving the target angle and Doppler frequency information. The BR algorithm can assist with target detection and tracking despite a background with ionospheric clutter. [ABSTRACT FROM AUTHOR]

Space-time adaptive processing (STAP) plays an essential role in clutter suppression and moving target detection in airborne radar systems. The main difficulty is that independent and identically distributed (i.i.d) training samples may not be sufficient to guarantee the performance in the heterogeneous clutter environment. Currently, most sparse recovery/representation (SR) techniques to reduce the requirement of training samples still suffer from high computational complexities. To remedy this problem, a fast group sparse Bayesian learning approach is proposed. Instead of employing all the dictionary atoms, the proposed algorithm identifies the support space of the data and then employs the support space in the sparse Bayesian learning (SBL) algorithm. Moreover, to extend the modified hierarchical model, which can only apply to real-valued signals, the real and imaginary components of the complex-valued signals are treated as two independent real-valued variables. The efficiency of the proposed algorithm is demonstrated both with the simulated and measured data. [ABSTRACT FROM AUTHOR]

In this study, the effects of non-sidelooking airborne radar clutter dispersion on space-time adaptive processing (STAP) is considered, and an efficient adaptive angle-Doppler compensation (EAADC) approach is proposed to improve the clutter suppression performance. In order to reduce the computational complexity, the reduced-dimension sparse reconstruction (RDSR) technique is introduced into the angle-Doppler spectrum estimation to extract the required parameters for compensating the clutter spectral center misalignment. Simulation results to demonstrate the effectiveness of the proposed algorithm are presented. [ABSTRACT FROM AUTHOR]

In this paper, an efficient direct data domain space-time adaptive processing (STAP) algorithm for moving targets detection is proposed, which is achieved based on the distinct spectrum features of clutter and target signals in the angle-Doppler domain. To reduce the computational complexity, the high-resolution angle-Doppler spectrum is obtained by finding the sparsest coefficients in the angle domain using the reduced-dimension data within each Doppler bin. Moreover, we will then present a knowledge-aided block-size detection algorithm that can discriminate between the moving targets and the clutter based on the extracted spectrum features. The feasibility and effectiveness of the proposed method are validated through both numerical simulations and raw data processing results. [ABSTRACT FROM AUTHOR]

In this paper, a low-altitude wind speed estimation method based on the fuselage frustum conformal array system is proposed. Firstly, based on the signal model of the fuselage conformal array radar, the four-dimensional joint phase compensation of the echo data in the Doppler domain and three-dimensional space-frequency domain is performed by using the four-dimensional frequency domain compensation method. Secondly, the clutter covariance matrix is estimated by the compensated echo data, and a space-time Adaptive Processing (STAP) processor suitable for low-altitude windshear target is constructed to suppress clutter. Finally, the maximum Doppler value of each distance cell is extracted, and the wind velocity is estimated. Simulation results show that the proposed method can effectively suppress clutter and accurately estimate wind speed.

Interference multipath is an important factor to affect the anti-jamming performance for the global navigation satellite system (GNSS) antenna array receiver. However, interference multipath must be considered in practical application. In this paper, the antenna array model for interference multipath is analyzed, and an equivalent model for interference multipath is proposed. According to the equivalent interference multipath model, the influence of interference multipath on anti-jamming performance is analyzed from the space only processing (SOP) and space-time adaptive processing (STAP). Interference multipath can cause loss of the degree of freedom (DoF) of SOP. Through analysis of the equivalent model and STAP mechanism, it further reveals how the STAP can solve the interference multipath. The simulation experiments prove that the equivalent model is effective, and the analysis conclusion is correct. This paper also points out that the interference bandwidth is wider and more taps in STAP are required, under the same experiment conditions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The novel sensing technology airborne passive bistatic radar (PBR) has the problem of being affecting by multipath components in the reference signal. Due to the movement of the receiving platform, different multipath components contain different Doppler frequencies. When the contaminated reference signal is used for space–time adaptive processing (STAP), the power spectrum of the spatial–temporal clutter is broadened. This can cause a series of problems, such as affecting the performance of clutter estimation and suppression, increasing the blind area of target detection, and causing the phenomenon of target self-cancellation. To solve this problem, the authors of this paper propose a novel algorithm based on sparse Bayesian learning (SBL) for direct clutter estimation and multipath clutter suppression. The specific process is as follows. Firstly, the space–time clutter is expressed in the form of covariance matrix vectors. Secondly, the multipath cost is decorrelated in the covariance matrix vectors. Thirdly, the modeling error is reduced by alternating iteration, resulting in a space–time clutter covariance matrix without multipath components. Simulation results showed that this method can effectively estimate and suppress clutter when the reference signal is contaminated. [ABSTRACT FROM AUTHOR]

Space-time adaptive processing (STAP) techniques have been motivated as a key enabling technology for advanced airborne radar applications. In this paper, a slow-time code design is considered for the STAP technique in airborne radar, and the principle for improving signal-to-clutter and noise ratio (SCNR) based on slow-time coding is given. We present two algorithms for the optimization of transmitted codes under the energy constraint on a predefined area of spatial-frequency and Doppler-frequency plane. The proposed algorithms are constructed based on convex optimization (CVX) and alternating direction (AD), respectively. Several criteria regarding parameter selection are also given for the optimization process. Numerical examples show the feasibility and effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Clutter suppression in heterogeneous environments is a serious challenge for airborne radar. To address this problem, a matrix-manifold-based clutter suppression method is proposed. First, the distributions of training data in heterogeneous environments are analyzed, while the received data are characterized on a Riemannian manifold of Hermitian positive definite matrices. It is indicated that the training data with different distributions with the same power are separated, whereas data with the same distribution are closer together. This implies that the underlying geometry of the data can be better revealed by manifolds than by Euclidean space. Based on these properties, homogeneous training data are selected by establishing a binary hypothesis test such that the negative effects of the use of heterogeneous samples are alleviated. Moreover, as exploiting a geometric metric on manifolds to reveal the underlying information of data, experimental results on both simulated and real data validate that the proposed method has a superior performance with small sample support. [ABSTRACT FROM AUTHOR]

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.

For statistic space-time adaptive processing (STAP), a critical issue is estimating the clutter covariance matrix (CCM). However, sufficient training samples are difficult to obtain that satisfy the independent and identically distributed (IID) condition. It is because of the realistic heterogeneous environment faced by airborne radar. Moreover, one should eliminate contaminated training samples before CCM estimation. Aiming at the problems of the computational complexity and susceptibility to the outlier of the traditional generalized inner product (GIP) method, a clutter subspace-based training sampling selecting method is proposed combined with specific distribution in the space-time plane of clutter spectrum. Theoretical analysis and simulation results verified the proposed method and indicate that the proposed method is easy to construct CCM and has lower computational complexity and sensitivity to outliers. [ABSTRACT FROM AUTHOR]

In this paper, we present ground moving target indication (GMTI) signal processing algorithm encompassing clutter suppression, target detection and parameter estimation. One of the most significant yet least publicized is the need of the GMTI mode for a forward-looking airborne radar. The integration of GMTI mode in a forward-looking airborne radar allows reconnaissance and surveillance operations in all weather conditions. In this context, space time adaptive processing (STAP) offers a unique prospect of enabling the GMTI mode in forward looking airborne radar. STAP is a two-dimensional filter designed to suppress platform motion-induced clutter Doppler spread. Interference is characterized by a covariance matrix. In the case of a forward-looking airborne radar, the clutter Doppler is dependent on range. Clutter Doppler dependency on the range renders the training cells heterogeneous. The heterogeneity effects are particularly prominent in the near range bins. Non-homogeneous training cells have a deleterious effect on STAP performance. In this study, we propose an adaptive Doppler compensation to mitigate the degraded STAP performance in the near range bins. The adaptivity feature circumvents the need for the availability of radar parameters in real-time. The real time implementation of STAP is impeded by requirements of a large number of training samples and covariance matrix inversion. Therefore, there is a dire need to devise a framework to detect and estimate target parameters within the STAP. In this regard, we propose an efficient STAP algorithm to detect and estimate target parameters. STAP weights are applied to the input data to obtain a 3D array. The range projection of the 3D array is utilized to detect and estimate the range of the target, while the angle–Doppler projection is used to estimate spatial and temporal parameters of the target. Most of the literature on STAP is geared towards a known covariance matrix. The assumption of a known covariance matrix may degrade STAP performance because of the inherent mismatches between the actual and assumed target steering vectors. In this study, we estimate the covariance matrix based on the synthetic data generated from a model of an airborne phased array radar. The developed STAP algorithms closely mimic a real-time implementation scheme in an airborne radar platform. The results of the proposed algorithm are validated through target parameter estimation and STAP metrics on synthetic data. [ABSTRACT FROM AUTHOR]

The article presents a new space-time adaptive processing (STAP) method for target detection in a heterogeneous and non-stationary environment. In study it was proven that it is possible to estimate the clutter covariance matrix (CCM) in STAP by using the MIMO (Multiple Input Multiple Output) radar geometry model and the orthogonal matching pursuit (OMP) algorithm. For the estimation of spatio-temporal spectrum of clutter and target, a model of joint sparse recovery was established. As a result, clutter suppression and target detection in a heterogeneous environment will be achieved. In addition, the proposed method uses a single snapshot of the radar data cube, which eliminates the need for access to all training cells. [ABSTRACT FROM AUTHOR]

The nonhomogeneous clutter is a major challenge for ship detection in high-frequency mixed-mode surface wave radar. In this paper, a geometric barycenter-based reduced-dimension space-time adaptive processing method is proposed to suppress the clutter. Given the measured dataset, the range correlation of sea clutter is first investigated. Then, joint domain localized processing is applied to solve the training samples starve scenario in a practical system. The geometric barycenter-based training data selector is presented to select valid training samples and improve the accuracy of the clutter covariance matrix estimation. Finally, the validity of the proposed method is verified using the experimental data and the results show that it outperforms the conventional method in the nonhomogeneous environment of a practical system. [ABSTRACT FROM AUTHOR]

In this study, the effects of non-sidelooking airborne radar clutter dispersion on space-time adaptive processing (STAP) is considered, and an efficient adaptive angle-Doppler compensation (EAADC) approach is proposed to improve the clutter suppression performance. In order to reduce the computational complexity, the reduced-dimension sparse reconstruction (RDSR) technique is introduced into the angle-Doppler spectrum estimation to extract the required parameters for compensating the clutter spectral center misalignment. Simulation results to demonstrate the effectiveness of the proposed algorithm are presented.