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

Traditional space-time adaptive processing (STAP) is a strategy for clutter suppression in airborne radar, which requires a large number of computational complexity and secondary data. In order to address the problem, reduced-dimension (RD) STAP is generally used. We propose a novel RD STAP through searching the best channels as the auxiliary channels to cancel the interference. Based on the estimation of the clutter Fourier basis vectors offline, a parameter named angle-Doppler correlation coefficient (ADC 2) is constructed to evaluate the capability of each auxiliary channel in clutter suppression, and the best sets of RD channels can be selected. The proposed algorithm can achieve the best detection performance with the fixed number of auxiliary channel. When the degrees of freedom (DOF) are restricted to a small value, only one auxiliary channel is needed to guarantee the SINR loss less than 3 dB. Therefore, the requirement of the training sample can be reduced, which makes the proposed approach more suitable for the heterogeneous clutter environments.

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.

Sparse Bayesian learning for the Laplace transform inversion in dynamic light scattering

The performance of the optimal and reduced-dimension Space-Time Adaptive Processing (STAP) algorithms for the airborne phased array radar is investigated in this paper. The clutter suppression in the mainbeam and the sidelobe clutter regions and the computation load are analyzed and compared between the optimal and two kinds of typical reduced-dimension STAP algorithms, i.e. the 2-D temporal-spatial adaptive algorithm (TSA) and the m-bins Doppler transform-space adaptive processing (mDT-SAP) algorithm. Based on the performance analysis of these STAP algorithms, a new clutter suppression strategy is proposed with consideration of actual clutter environment and the spatial channel and temporal pulse restricted system, which can detect the target more effectively and implement more easily for the airborne phased array radar.

Space–time adaptive processing (STAP) can effectively detect moving targets in the background of ground clutter, but the performance will drop sharply when the training samples are limited. In this paper, to improve the clutter suppression performance when the training samples are limited, the authors propose a novel STAP algorithm based on sparse Bayesian learning (SBL) using a hierarchical synthesis prior. Firstly, we construct a novel three‐level hierarchical synthesis prior (HSP) model, which promotes the sparsity more significantly than traditional priors used in SBL. Secondly, in the framework of type‐II maximum likelihood approach, a novel iterative update criterion for hyperparameters is derived. Thirdly, in order to reduce the computational burden, the authors design a novel local space–time dictionary to transform the full‐dimensional clutter spectrum recovery problem into a local clutter spectrum recovery problem. Numerical results with both simulated and measured data demonstrate the excellent performance and relatively high computational efficiency of the proposed method.

In this article, the problem of the transmit beamspace (TB) matrix design in multiple-input multiple-output (MIMO) radar is considered. To reduce the computational burden and to focus the energy within optimal spatial sector, a joint design method of the TB matrix and a reduced-dimension space-time adaptive processing (STAP) filter is proposed based on the maximum signal-to-clutter-plus-noise ratio (SCNR). Firstly, the signal model of the reduced-dimension STAP for TB-based MIMO radar is established. Then, using the maximum SCNR as the design metric, the joint design model of the TB matrix and the reduced-dimension STAP filter is presented. Furthermore, the cyclic optimisation method is exploited to deal with the non-convex joint design problem with the energy constraint. Finally, simulation experiments demonstrate the joint design method of the TB matrix and the STAP filter not only has better output SCNR performance but also owns lower computational burden when compared with the other existing methods.

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.

We consider the progressive reconstruction of a streaming signal from compressive, streaming measurements. We develop a progressive reconstruction algorithm based on sliding window processing, where we reconstruct the streaming signal over small overlapping shifting intervals. Since the consecutive intervals share some common sparse signal vectors, the key idea of this work is to utilize the preliminary information from the preceding interval to improve the performance of the signal recovery algorithm. For this purpose, we propose a novel sparse Bayesian learning (SBL) algorithm, which is highly efficient for recovering streaming signals. One major advantage of SBL is that it provides a measure of uncertainty of the reconstructed signal rather than computing only a point estimate. The proposed SBL algorithm utilizes the previous estimates as well as the correlations among the nonzero coefficients to improve the performance of the algorithm. Since the effect of uncertainty of the reconstructed signal from the preceding interval is specifically taken into account in the recovery process of the current interval, the proposed algorithm is more robust to the error propagation compared to the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm minimization counterpart. Further, we propose a warm-start procedure and derive fast update formulae to reduce the computational cost of the SBL algorithm. In addition, we also discuss the properties of the signal and the underlying approximations which enable the progressive reconstruction of the streaming signal from compressive measurements.

Space-time adaptive monopulse processing for airborne radar in non-homogeneous environments

A reduced-dimension space time adaptive processing (STAP) for airborne multiple-input multiple-output (MIMO) radar based on three iterations is proposed. The optimum filter for MIMO-STAP is described as a separable filter, and the optimal weight vector in high dimension can be iteratively solved by three lower dimension weight vectors. Consequently, the quite high sample support and computational complexity in optimal MIMO-STAP can be efficiently reduced. For short data records, the proposed method can achieve better clutter suppression performance. Simulation results demonstrate the effectiveness of the proposed method.

Scalable and memory-efficient sparse learning for classification with approximate Bayesian regularization priors

Traditional space-time (ST) adaptive processing (STAP) theory is based on the assumption of narrowband or &#x201C;zero-bandwidth,&#x201D; where the decorrelation within the ST snapshot is ignored. However, with radar bandwidths increasing, this assumption becomes invalid due to the deteriorated decorrelation of the received signals within the ST snapshot. The decorrelation directly causes the dispersion of the received signals in both spatial and temporal domains, leading to the spreading of the clutter spectrum in the 2-D frequency (Doppler-spatial frequency) domain. With the spreading of the clutter spectrum, the clutter suppression notch in the traditional STAP filters is widened, resulting in a relative poor ability to detect slow-moving targets. In this article, we focus on the clutter suppression for wideband radar STAP. A generalized signal model of the ground clutter is first established for the wideband array radar. Using this outcome, we analyze the influence of bandwidth on the characteristics of the ground clutter and quantitatively describe the 2-D spreading of the ground clutter on the Doppler-spatial frequency plane. Moreover, the model of clutter covariance matrix for wideband STAP (W-STAP) is established. Finally, a 2-D keystone transform (KT) algorithm, referred to as ST KT (ST-KT), is proposed to eliminate the spreading of the ground clutter in the 2-D frequency domain caused by increasing bandwidths. Simulation results are employed to validate the theoretical analysis and verify the overperformance of the ST-KT based W-STAP method in terms of the output signal-to-clutter-plus-noise ratio (SCNR) of moving targets.

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.

The ground moving-target indication (GMTI) technique can detect civil and military moving targets, which means that this technique has received much attention. Strong clutter background suppression is one of the critical problems in this application. However, the detection performance in heterogeneous environment can be degraded due to the inaccurate estimation of the clutter covariance matrix (CCM). In this paper, we propose a robust GMTI method using a spaceborne dual-platform synthetic aperture radar (SAR) system, which can obtain highly accurate CCM in nonuniform clutter. Firstly, the accurate CCM is estimated based on the SAR image obtained by the former platform. Then, space–time adaptive processing (STAP) is carried out using the obtained the CCM. Finally, the detection threshold is set according to the estimated CCM and detection is executed accurately. Compared with the traditional CCM estimation method in STAP using the clutter nearby the cell under test, this method directly estimates the CCM using the clutter of the cell under test, which can avoid CCM estimation mistakes in heterogeneous clutter environment. The clutter can be whitened and depressed more effectively. Additionally, with the accurate threshold acquired from the CCM, the detection probability can be effectively improved under a certain false-alarm criterion. Based on simulation data, GMTI experiments in a heterogeneous environment such as clutter with strong pollution, junction zone of hot and cold clutter, and clutter with nonuniform power are carried out; the results show that the moving targets can be effectively detected with the proposed method.

The range-dependence of clutter spectrum for forward-looking airborne radar strongly affects the accuracy of the estimation of clutter covariance matrix at the range under test, which results in poor clutter suppression performance if the conventional space-time adaptive processing (STAP) algorithms were applied, especially in the short range cells. Therefore, a new STAP algorithm with clutter spectrum compensation by utilizing knowledge-aided subspace projection is proposed to suppress clutter for forward-looking airborne radar in this paper. In the proposed method, the clutter covariance matrix of the range under test is firstly constructed based on the prior knowledge of antenna array configuration, and then by decomposing the corresponding space-time covariance matrix to calculate the clutter subspace projection matrix which is applied to transform the secondary range samples so that the compensation of clutter spectrum for forward-looking airborne radar is accomplished. After that the conventional STAP algorithm can be applied to suppress clutter in the range under test. The proposed method is compared with the sample matrix inversion (SMI) and the Doppler Warping (DW) methods. The simulation results show that the proposed STAP method can effectively compensate the clutter spectrum and mitigate the range-dependence significantly.