DOA Estimation Method for Cyclostationary Signals with Joint Sparse Representation

Based on the idea of joint-sparse representation, a direction of arrival (DOA) estimation method of cyclostationary signals using the second order cyclic statistic is proposed in this paper. Firstly, the conventional spectral correlation signal subspace fitting algorithm is analyzed and researched. Then, by constructing overdeterminated dictionary of array direction matrix in the cycle domain, the joint-sparse signal representation model is formed and the problem of DOA estimation of cyclostationary signals is thus converted into that of recovery of the joint-sparse matrix. Finally, the optimal solution of the joint-sparse matrix is given by using the joint l₀ norm approximation approach, and the DOA estimates are obtained according to the locations of non-zero rows in the optimal joint-sparse matrix. Compared with the conventional spectral correlation signal subspace fitting algorithm, the proposed method has higher DOA estimation precision, and is also suitable to the scenario that the number of signals is more than that of array elements. Theory analysis and simulation results both validate the effectiveness of the proposed method.