A Joint Sparse Space-time Adaptive Processing Method

Jinfeng Hu,Yuyan Xia,Jing Liang,Tao Lin,Huiyong Li

doi:10.1109/access.2020.3001304

Abstract

At present, most of the sparse space-time adaptive processing(STAP) methods focus on exploiting the clutter sparsity. In this paper, different from the present sparse STAP methods, both the clutter sparsity and the target sparsity in STAP are considered at the same time, and a novel joint sparse STAP method is proposed. The proposed method imposes a sparse regularization on the clutter and the target to the minimum Capon Spectrum criterion. The processing results of the measured data shows that the output SCNR of the proposed method is 3dB higher than the method in [20] and 2dB higher than the method in [21].

Highlights

S PACE-TIME adaptive processing (STAP) is widely used in the radar for detecting slowly moving targets [1]–[9] and in the communications for suppressing the interference [10], [11].Recently, motivated by the sparsity of clutter, many sparse STAP methods have been proposed [12]–[21], and they can be divided into two categories: the direct method [12]– [18] and the indirect method [19]–[21]
Motivated by the sparsity of clutter, many sparse STAP methods have been proposed [12]–[21], and they can be divided into two categories: the direct method [12]– [18] and the indirect method [19]–[21]
The indirect method exploits indirectly the clutter sparsity by imposing a sparse regularization on the STAP filter weight vector, due to that the clutter sparsity will result in the STAP filter weight vector sparsity

Summary

INTRODUCTION

S PACE-TIME adaptive processing (STAP) is widely used in the radar for detecting slowly moving targets [1]–[9] and in the communications for suppressing the interference [10], [11]. It is difficult to find the most suitable regularization compromise parameter set To solve this problem, a method which can adjust the regularization compromise parameter adaptively for the different snapshots is proposed in [21]. A method which can adjust the regularization compromise parameter adaptively for the different snapshots is proposed in [21] This method does not need to give a regularization compromise parameter set in advance, which further improves STAP performance. The above sparse STAP methods exploit only the clutter sparsity. Based on the above considerations, we propose a joint sparse STAP method, which imposes a sparse regularization on the clutter and the target to the minimum Capon Spectrum criterion.

PROBLEM ANALYSIS

THE PROPOSED JOINT SPARSE STAP METHOD

SOLUTION TO THE PROPOSED JOINT SPARSE STAP METHOD

SIMULATION ANALYSIS

CONCLUSION