Application of compressive sensing for fast solving multi-snapshot signal model in uniform linear arrays

This paper presents an efficient approach for rapidly solving the multiple snapshot signal model, aiming to achieve accurate estimation of the DOA while reconstructing the incident signals. The proposed method is based on compressed sensing (CS) and involves several steps. Firstly, a measurement matrix is introduced into the multi-snapshot model, resulting in a reduced number of snapshot received data. Secondly, the corresponding source signals are obtained using the orthogonal matching pursuit (OMP) algorithm, which serves as the measurement results of the original source signals. Thirdly, the original source signals are reconstructed using the discrete Fourier transform (DFT) basis or interpolated DFT basis as the sparse transform, combined with OMP. The algorithm’s principle is elaborated in detail, and numerical simulations demonstrate its accuracy and low complexity.