Parameters Estimation of Coherently Distributed Non-circular Signal Based on Sparse Representation

A novel method for parameters estimation of coherently distributed non-circular signal based on the concept of sparse representation is proposed. The non-circular property is introduced into the model of distributed source, and the non-circular property is fully used to unite the covariance and elliptic covariance matrix of the array output. By representing them on overcomplete dictionaries subject to sparse constraint, and transforming DOA estimation into a sparse reconstruction problem, the method is able to solve the central DOA and angular spread at a time. Simulation results show that the proposed method can be used in different kinds of non-circular rate with better performance of low SNR and resolution, and the proposed algorithm can also effectively estimate the DOA in the case of both circular and non-circular signal existing.