Joint DOA and Frequency Estimation for Linear Array with Compressed Sensing PARAFAC Framework

Abstract

In this paper, a joint direction of arrival (DOA) and frequency estimation algorithm of narrow-band signals is proposed via compressed sensing (CS) parallel factor (PARAFAC) framework. The proposed algorithm constructs the data model into a PARAFAC model, and compresses it to a smaller one. Then trilinear alternating least-squares (TALS) algorithm is exploited to estimate the compressed parameter matrices, and finally the joint DOA and frequency estimation is obtained via the spatial sparsity and the frequency sparsity. Due to compression, the proposed algorithm has lower computational complexity than the conventional PARAFAC algorithm, and saves more memory capacity for practical application. The DOA and frequency estimation performance of the proposed algorithm is very close to that of the conventional PARAFAC algorithm, and better than those of the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm and the propagator method (PM). Furthermore, the proposed algorithm can achieve automatically paired DOA and frequency estimation. Besides, it is applicable for nonuniform linear arrays. Effectiveness of the proposed algorithm is assessed by simulations.