Sparsity-Aware STAP Algorithms Using $L_1$-norm Regularization For Radar Systems

TL;DR

This paper introduces sparsity-aware STAP algorithms with $l_1$-norm regularization for airborne radar, improving convergence speed and performance by exploiting the sparsity of the optimal filter weights.

Contribution

It proposes novel $l_1$-norm regularized SA-STAP algorithms, including a practical conjugate gradient-based method, for efficient radar signal processing.

Findings

Fast SINR convergence demonstrated in simulations

Reduced computational complexity compared to existing methods

Effective performance with both simulated and real data

Abstract

This article proposes novel sparsity-aware space-time adaptive processing (SA-STAP) algorithms with $l_1$-norm regularization for airborne phased-array radar applications. The proposed SA-STAP algorithms suppose that a number of samples of the full-rank STAP data cube are not meaningful for processing and the optimal full-rank STAP filter weight vector is sparse, or nearly sparse. The core idea of the proposed method is imposing a sparse regularization ($l_1$-norm type) to the minimum variance (MV) STAP cost function. Under some reasonable assumptions, we firstly propose a $l_1$-based sample matrix inversion (SMI) to compute the optimal filter weight vector. However, it is impractical due to its matrix inversion, which requires a high computational cost when in a large phased-array antenna. Then, we devise lower complexity algorithms based on conjugate gradient (CG) techniques. A computational complexity comparison with the existing algorithms and an analysis of the proposed algorithms are conducted. Simulation results with both simulated and the Mountain Top data demonstrate that fast signal-to-interference-plus-noise-ratio (SINR) convergence and good performance of the proposed algorithms are achieved.