Direct Data Domain Sparsity-Based STAP Utilizing Subaperture Smoothing Techniques

Zhaocheng Yang,Hongqiang Wang,Rui Fa,Yuliang Qin,Xiang Li

doi:10.1155/2015/171808

Abstract

We propose a novel direct data domain (D3) sparsity-based space-time adaptive processing (STAP) algorithm utilizing subaperture smoothing techniques for airborne radar applications. Different from either normal sparsity-based STAP or D3 sparsity-based STAP, the proposed algorithm firstly uses only the snapshot in the cell under test (CUT) to generate multiple subsnapshots by exploiting the space-time structure of the steering vector and the uncorrelated nature of the components of the interference covariance matrix. Since the interference spectrum is sparse in the whole angle-Doppler plane, by employing a sparse regularization, the generated multiple subsnapshots are jointly used to recover the interference spectrum. The interference covariance matrix is then estimated from the interference spectrum, followed by the space-time filtering and the target detection. Simulation results illustrate that the proposed algorithm outperforms the generalized forward/backward method, the conventional D3 least squares STAP algorithm, and the existing D3 sparsity-based STAP algorithm. Furthermore, compared with the normal sparsity-based STAP algorithm using multiple snapshots, the proposed algorithm can also avoid the performance degradation caused by discrete interferers merely appearing in the CUT.

Highlights

Space-time adaptive processing (STAP) is considered to be an effective tool for detection of weak targets by airborne radar systems in strong interference environments [1,2,3,4]
It is seen from the curves that (i) the output signal-to-interference-plus-noise ratio (SINR) performance of the proposed subaperture smoothing (SASM) D3-sparse recovery (SR)-STAP algorithm is better than that of the conventional D3-SR-STAP algorithm corresponding to the case of N󸀠 = N and M󸀠 = M since the proposed algorithm uses multiple subsnapshots to estimate the interference covariance matrix. (ii) There is a range of the sizes of N󸀠 and M󸀠 to obtain a good SINR performance, which leads to a relaxing parameter setting of N󸀠 and M󸀠. (iii) When M − M󸀠 > 5 or N − N󸀠 > 5, the performance of the proposed algorithm degrades because of too many DOFs reduced
We focus on the impact of model mismatch, such as the ICM and the channel mismatch, to the proposed SASM D3-SR-STAP algorithm

Summary

Introduction

Space-time adaptive processing (STAP) is considered to be an effective tool for detection of weak targets by airborne radar systems in strong interference environments [1,2,3,4]. Knowledge-aided (KA) STAP approaches using digital land classification data and digital elevation data were proposed to select training data to obtain improved STAP performance [11] Another KA-STAP method, called model-based approach (see [12,13,14,15,16,17,18,19] and the references therein), basically employs some prior knowledge to form the simplified general clutter model (GCM) and blends the GCM with the measured observations to design the STAP filter or directly uses it to design the STAP filter. The proposed algorithm uses only snapshot in the CUT as conventional D3-SR-STAP does It uses the decimation techniques [32, 33] to generate multiple subsnapshots by exploiting the space-time structure of steering vector and the uncorrelated nature of the components of the interference covariance matrix.

Signal Model and Problem Formulation

Proposed SASM D3-SR-STAP Algorithm

Simulation Results

Conclusions