This study proposes novel sparsity- aware space-time adaptive processing (SA-STAP) algorithms with l-1-norm regularisation for airborne phased-array radar applications. The proposed SA-STAP algorithms suppose that a n...
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This study proposes novel sparsity- aware space-time adaptive processing (SA-STAP) algorithms with l-1-norm regularisation for airborne phased-array radar applications. The proposed SA-STAP algorithms suppose that a number of samples of the full-rank STAP datacube 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 regularisation (l-1-norm type) to the minimum variance STAP cost function. Under some reasonable assumptions, the authors firstly propose an l-1-basedsamplematrixinversion to compute the optimal filter weight vector. However, it is impractical because of its matrixinversion, which requires a high computational cost when using a large phased-array antenna. In order to compute the STAP parameters in a cost-effective way, the authors devise low-complexity algorithms based on conjugate gradient 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 convergence and good performance of the proposed algorithms are achieved.
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