Design of sparse-signal processing in radar systems

Abstract

Sparse-signal processing (SSP) is interpreted in this paper as a sparse model-based refinement of typical steps in radar processing. Matched filtering remains vital within SSP but joined with radar detection promoting the sparsity. Realistic measurements are also supported in SSP by using Monte-Carlo (MC) methods. MC-based SSP promotes the sparsity by detection-driven MC-sampling that also improves efficiency. This MC extension aims for the stochastic description of sparse solutions, and the flexibility to use any prior on signals or on data acquisition, as well as any distribution of noise or clutter. Numerical experiments demonstrate favorable performance of the proposed SSP.