A Two-Stage STAP Method Based on Fine Doppler Localization and Sparse Bayesian Learning in the Presence of Arbitrary Array Errors.

Kun Liu,Jianxin Wu,Jinming Chen,Tong Wang

doi:10.3390/s22010077

Kun Liu, Jianxin Wu + Show 2 more

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https://doi.org/10.3390/s22010077

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Abstract

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.

Highlights

Space-time adaptive processing (STAP) [1,2,3,4,5,6,7,8] is an effective approach for ground clutter suppression and low-velocity target detection in airborne radars
Radar operates in STAP mode, 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 the constructed dictionary and measurement vectors sparse Bayesian learning (MSBL) algorithm are combined for STAP
We evaluate the target detection performance by the probability of detection (PD) versus signal to noise ratio (SNR) curves, which are achieved by utilizing the adaptive matched filter (AMF) detector [51], and the probability of false alarm rate (PFA) is set as 10−3, the target is assumed in the main beam direction with the normalized Doppler frequency 0.1, the threshold and probability of detection estimates are based on 104 samples

Summary

Introduction

Space-time adaptive processing (STAP) [1,2,3,4,5,6,7,8] is an effective approach for ground clutter suppression and low-velocity target detection in airborne radars. The independent and identically distributed (IID) target-free training samples adjacent to the CUT are used to estimate the CCM. According to the Reed–Mallett–Brennan (RMB) rule [9], to achieve an output signal-to-clutter-plus-noise ratio (SCNR) loss within 3 dB, the number of used IID training samples must be greater than twice the system degrees of freedom (DOFs). This requirement is hard to be satisfied in the practical heterogeneous and non-stationary clutter environment, thereby resulting in a severe performance degradation of the STAP algorithms. Several low-sample methods have been developed to relieve the performance degradation caused by limited training data, such as reduced-dimension (RD) [10,11,12,13,14,15,16] algorithms, reduced-rank (RR) [17,18,19,20,21] algorithms, parametric adaptive matched filter (PAMF) algorithms [22,23], direct data domain (D3) [24,25] algorithms and knowledge-aided (KA)

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