Space-time Adaptive Processing Research Articles

Interrupted-Sampling and Non-Uniform Periodic Repeater Jamming against mDT-STAP System

The difference between sampling data and detection data can degrade the performance of space-time adaptive processing (STAP). A jamming algorithm with a non-uniform periodic repeater based on interrupted-sampling is proposed against the reduced dimensional space-time adaptive processing (STAP) system for the first time. Firstly, the model of m-bins doppler transform (mDT) STAP training and processing signal samples is described. Then, the method of false targets generated by the non-uniform periodic repeater is analyzed theoretically based on the principle of interrupted-sampling. The simulation shows that numerous false targets with different amplitude and intervals can be generated by changing the retransmitted parameters. The independent identical distribution (IID) of system sample data can be destroyed after these false targets are received by the radar system, and the main lobe will be distorted when the system’s adaptive weight vector is formed. The processing performance of the mDT-STAP system is seriously degraded. The jamming method proposed based on interrupted-sampling and the non-uniform periodic repeater offers great potential for the interference research on STAP in real conditions.

Low-Altitude Windshear Estimation Method Based on Four-Dimensional Frequency Domain Compensation for Fuselage Frustum Conformal Array

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.

An Improved Iterative Reweighted STAP Algorithm for Airborne Radar

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.

Space‐time adaptive processing algorithm based on hyper beamforming for ionospheric clutter suppression in small‐array high‐frequency surface wave radar

AbstractSmall‐array high‐frequency surface wave radar (HFSWR) is widely used to monitor maritime targets as it can be used to save on‐land resources. In small‐array HFSWR systems, the main lobe of the receiving angle spectrum is significantly broadened. In complex clutter backgrounds, an extremely wide beam severely influences clutter suppression performance; consequently, targets with a low signal‐to‐clutter ratio (SCR) may be eliminated, or the angle may be barely estimated. This study proposes a space‐time adaptive processing (STAP) algorithm based on hyper beamforming (HBF) to improve the clutter suppression performance of small‐array HFSWR. In addition, HBF can obtain more independent identical distributed training samples than the conventional beamforming; thus, the STAP algorithm can extract the clutter information with high accuracy in the covariance matrix estimation. Moreover, this study combines an efficient STAP algorithm with a joint domain localised (JDL) algorithm to improve clutter suppression. Based on the experimental results, the proposed HBF‐JDL algorithm performs satisfactorily and significantly improves the SCR. Moreover, HBF‐JDL is still applicable at lower SCRs of the target compared with JDL.

Autoencoder Neural Network-Based STAP Algorithm for Airborne Radar with Inadequate Training Samples

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 l2 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.

Clutter suppression and target energy focussing for airborne rotating antenna radar using improved Space‐time adaptive processing

AbstractRotating antenna radar is widely used on the airborne platform to extend the range of the radar detection airspace. However, the echo signal received by an airborne rotating radar is spread in the Doppler domain due to the platform motion and the antenna rotation. In the process of a long‐time integration, the antenna rotation harms the signal‐to‐noise ratio and reduces the performance of target detection when using conventional processing methods. Although conventional space‐time adaptive processing (STAP) has been used successfully to suppress clutter on an airborne radar, it does not take into account the antenna rotation. It decreases the performance of clutter suppression on the airborne rotating antenna radar. The signal model of airborne rotating antenna radar is analysed and the STAP technique to the rotating antenna scenario is improved to effectively suppress clutter and focus on target energy. Firstly, the echo of the airborne rotating antenna radar is modelled. Secondly, the factors affecting the Doppler difference of the array elements are investigated. Thirdly, an improved STAP method is proposed. Finally, the effect of antenna rotation on clutter Doppler characteristics and the clutter suppression and target energy focussing on the effects of the improved STAP technique are investigated. Experimental results demonstrate the effectiveness of the proposed method.

A Distortionless Anti-Jamming Method Based on STAP for GNSS Receiver

Global Navigation Satellite System (GNSS) receivers often realize anti-jamming capabilities by combining array antennas with space-time adaptive processing (STAP). Unfortunately, in suppressing the interference, basic STAP degrades the GNSS signal. For one thing, additional carrier phase errors and code phase errors to the GNSS signal are introduced; for another, the shape of the cross-correlation function (CCF) will be distorted by STAP, introducing tracking errors when the receiver is in tracking mode. Both of them will eventually cause additional Pseudo-Range (PR) bias, and these problems prevent STAP from being directly applied to high-precision satellite navigation receivers. The paper proposes a novel anti-jamming method based on STAP that solves the above problems. First, the proposed method constructs a symmetric STAP by constraining the STAP coefficients. Subsequently, with the information of the steering vector, a compensation FIR filter is cascaded after the symmetric STAP. This approach ensures that the proposed method introduces only a fixed offset to the code phase and carrier phase, and the order of the STAP completely determines the offset, which can be compensated during PR measurements. Meanwhile, the proposed method maintains the symmetry of the CCF, and the receiver can accurately track the carrier phase and code phase in tracking mode. The effectiveness of the proposed method is validated through simulations, which suggest that, in the worst case, our method does not increase carrier and code phase errors and tracking error at the expense of only a 2.86dB drop in interference suppression performance.

End-to-End Moving Target Indication for Airborne Radar Using Deep Learning

Moving target indication (MTI) based on space–time adaptive processing (STAP) has been widely used in airborne radar due to its ability for clutter suppression performance. However, the existing MTI methods suffer from the problems of insufficient training samples and low detection probability in a non-homogeneous clutter environment. To address these issues, this paper proposes a novel deep learning framework to improve target indication capability. First, combined with the problems of target indication caused by the non-homogeneous clutter, the clutter-plus-target training dataset was modeled by simulation, where various non-ideal factors, such as aircraft crabbing, array errors and internal clutter motion (ICM), were considered. The dataset considers various realistic situations, making the proposed method more robust. Then, a five-layer two-dimensional convolutional neural network (D2CNN) was designed and applied to learn the clutter and target characteristics distribution. The proposed D2CNN can predict the target with a high resolution to implement an end-to-end moving target indication (ETE-MTI) with a higher detection accuracy. In this D2CNN, the input was obtained by the clutter-plus-target angle-Doppler spectrum with a low-resolution estimated only by a few samples. The label was given by the target angle-Doppler spectrum with a high-resolution obtained by the target’s exact angle and Doppler. Thirdly, the proposed method used a few samples to improve the target indication and detection probability, which solved the problem of insufficient samples in the non-homogeneous clutter environments. To elaborate, the proposed method directly implements ETE-MTI without the support of the conventional STAP algorithm to suppress the clutter. The results verify the validity and the robustness of the proposed ETE-MTI with a few samples in the non-homogeneous and low signal-to-clutter ratio (SCR) environments.

The influence analysis of antenna radiation pattern aberration of airborne radar on clutter feature

In order to quantitatively evaluate the influence of the aberration of the antenna radiation pattern of airborne radar on clutter feature, the range-azimuth two dimensional (2D) model of the airborne radar clutter is established. Then the theoretic modelling and simulation analysis are proposed in three aspects, including the Doppler, power spectrum and space-time 2D characteristics of the clutter. Finally, the processing of moving target indication, pulse Doppler and space-time adaptive processing are used to suppress the clutter of the echo signals before and after the aberration. The results of the simulations indicate the aberration would elevate the sidelobe level, extend the power spectrum and bend the space-time trajectory of the clutter Doppler spectra. And the residual clutter would be elevated nearly 5 dB after STAP processing. Then the pattern preserving and fast time cancel combined space-time adaptive processing method is proposed, which can obtain narrow clutter notch and large improvement factor, and effectively suppress clutter. The modelling and quantitative analysis study have important theoretical and engineering significance for the detection capability of the airborne radar installed on the fuselage of the aircraft platform, the tactical and technical performance index, and improving the signal processing ability of airborne radar.

The Study of the Possibility of Applying Parallel Programming to the Algorithms of Space-Time Adaptive Processing

The article presents the description, assumptions and subsequent steps of the space-time adaptive processing (STAP) algorithms used as a signal processing tool in radars. The possibilities of object detection using the Sample Matrix Inversion (SMI) and Data Domain Least Squares (DDLS) algorithms were compared and showned. The article shows the impact of the use of parallel programming on the computation time of both algorithms. The main aim of this study was to propose an efficient method for the real-time implementation of the STAP algorithm in airborne radar systems. The idea of using parallel programming in STAP, supported only by the preliminary research results presented above, gives a real chance for the casual implementation of the STAP algorithm in a radar operating in close to real time mode.

Space-Time Adaptive Processing Based on Modified Sparse Learning via Iterative Minimization for Conformal Array Radar.

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.

Adaptive Support-Driven Sparse Recovery STAP Method with Subspace Penalty

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.

Improved Dimension-Reduced Structures of 3D-STAP on Nonstationary Clutter Suppression for Space-Based Early Warning Radar

By introducing degrees of freedom (DOFs) in elevation, the elevation-azimuth-Doppler three-dimensional space–time adaptive processing (3D-STAP) methods have better performance when suppressing the nonstationary clutter caused by the Earth’s rotation in space-based early warning radar (SBEWR). However, the 3D-STAP methods use much more auxiliary beams, leading to greater demands on the training samples and heavier computational burdens than the conventional STAP methods. To solve this problem, the ideas of sum–difference beams, generalized multiple beams and Doppler-domain localization are applied here, and three improved dimension-reduced structures of 3D-STAP are proposed in this article. After analyzing the characteristics and distribution of nonstationary clutter for SBEWR, we find that the demands for auxiliary beams are different in elevation, azimuth and Doppler dimension. In addition, the suggestion to choose the number of auxiliary beams in each dimension is given. Simulation experiments are conducted to verify the analysis and evaluate the performance of the proposed methods. The simulation results show that the proposed 3D-STAP methods have better performance and lower computational burdens than typical 3D-STAP methods.

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.

Airborne Passive Bistatic Radar Clutter Suppression Algorithm Based on Root Off-Grid Sparse Bayesian Learning

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.

On the Efficient Implementation of Sparse Bayesian Learning-Based STAP Algorithms

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.

A Novel Sparse Bayesian Space-Time Adaptive Processing Algorithm to Mitigate Off-Grid Effects

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.

A fast sparse Bayesian learning method with adaptive Laplace prior for space‐time adaptive processing

Space-time adaptive processing with finite samples is supposed to be a crucial technique for airborne radar systems. Inspired by the application of Gaussian prior in sparse Bayesian learning algorithm and the adaptive least absolute shrinkage and selection operator algorithm, a hierarchical Bayesian framework with adaptive Laplace priors is proposed. In this paper, a novel method is applied to avoid the high-dimension matrix inverse operation in the proposed algorithm. Moreover, in order to apply the method in the complex-valued domain, the complex-valued signal is split into two independent variables. Then, the sparse recovery problem in the complex-valued domain can be transformed into the real-value domain. Simulation experiments show that the proposed algorithm can achieve great clutter suppression performance and also ensure high computational efficiency.

Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian Variational Autoencoder

Due to the shortage of independent and identically distributed (i.i.d.) training samples, space−time adaptive processing (STAP) often suffers remarkable performance degradation in the heterogeneous clutter environment. Sparse recovery (SR) techniques have been introduced into STAP for the benefit of the drastically reduced training requirement, but they are incompletely robust for involving the tricky selection of hyper−parameters or the undesirable point estimation for parameters. Given this issue, we incorporate the Multiple−measurement Complex−valued Variational relevance vector machines (MCV) to model the space−time echoes and provide a Gibbs−sampling−based method to estimate posterior distributions of parameters accurately. However, the Gibbs sampler require quantities of iterations, as unattractive as traditional Bayesian type SR−STAP algorithms when the real−time processing is desired. To address this problem, we further develop the Bayesian Autoencoding MCV for STAP (BAMCV−STAP), which builds the generative model according to MCV and approximates posterior distributions of parameters with an inference network pre−trained off−line, to realize fast reconstruction of measurements. Experimental results on simulated and measured data demonstrate that BAMCV−STAP can achieve suboptimal clutter suppression in terms of the output signal to interference plus noise ratio (SINR) loss, as well as the attractive real−time processing property in terms of the convergence rate and computational loads.

Joint Transmit Waveform and Passive Beamforming Design for RIS-Aided DFRC Systems

Reconfigurable intelligent surface (RIS) is a promising technology for 6 G networks owing to its superior ability to enhance the capacity and coverage of wireless communications by smartly creating a favorable propagation environment. In this paper, we investigate the potential of employing RIS in dual-functional radar-communication (DFRC) systems for improving both radar sensing and communication functionalities. In particular, we consider a RIS-assisted DFRC system in which the multi-antenna base station (BS) simultaneously performs both multi-input multi-output (MIMO) radar sensing and multi-user multi-input single-output (MU-MISO) communications using the same hardware platform. We aim to jointly design the dual-functional transmit waveform and the passive beamforming of RIS to maximize the radar output signal-to-interference-plus-noise ratio (SINR) achieved by space-time adaptive processing (STAP), while satisfying the communication quality-of-service (QoS) requirement under one of three metrics, the constant-modulus constraint on the transmit waveform, and the unit-modulus constraint of RIS reflecting coefficients. An efficient algorithm framework based on the alternative direction method of multipliers (ADMM) and majorization-minimization (MM) methods is developed to solve the complicated non-convex optimization problem. Simulation results verify the advancement of the proposed RIS-assisted DRFC scheme and the effectiveness of the developed ADMM-MM-based joint transmit waveform and passive beamforming design algorithm.