DOA estimation of sparsely sampled nonstationary signals

The paper deals with sparsely sampled nonstationary signals in a multi-sensor array platform. We examine direction-of-arrival (DOA) estimation using sparsity-based time-frequency signal representation (TFSR). While conventional time-frequency analysis techniques suffer from noise-like artifacts due to missing data samples, high-fidelity time-frequency signatures can be obtained by applying kernelled processing and sparse reconstruction. Since the signals received at different sensors occupy the same time-frequency regions and share a common nonzero support, the recovery of TFSRs can be cast as a group sparse reconstruction problem. The reconstructed auto- and cross-sensor TFSRs enable the formation of the spatial time-frequency distribution (STFD) matrix, which is used, in turn, to propose the sparse time-frequency MUSIC (STF-MUSIC). The proposed STF-MUSIC method achieves effective source discrimination capability, leading to improved DOA estimation performance.