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This article proposes novel sparsity-aware space-time adaptive processing(SA-STAP) algorithms with $l_1$-norm regularization for airborne phased-arrayradar applications. The proposed SA-STAP algorithms suppose that a number ofsamples of the full-rank STAP data cube are not meaningful for processing andthe 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 somereasonable assumptions, we firstly propose a $l_1$-based sample matrixinversion (SMI) to compute the optimal filter weight vector. However, it isimpractical due to its matrix inversion, which requires a high computationalcost when in a large phased-array antenna. Then, we devise lower complexityalgorithms based on conjugate gradient (CG) techniques. A computationalcomplexity comparison with the existing algorithms and an analysis of theproposed algorithms are conducted. Simulation results with both simulated andthe Mountain Top data demonstrate that fastsignal-to-interference-plus-noise-ratio (SINR) convergence and good performanceof the proposed algorithms are achieved.

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