A High Resolution Two-Dimensional DOA Estimation for Multipath Environments by Accurate Separation of Signals

In this paper, a two-dimensional DOA estimation method for multipath environments is proposed, in which there are simultaneous uncorrelated, partly correlated and coherent signals. The presence of coherent signals caused a loss of rank in the spatial covariance matrix, resulting in a sharp reduction in the precision of the estimates. Therefore, in a novel approach, first of all, the noncoherent signals, coherent groups and the noise subspace are separated with a good accuracy, according to statistical data of the eigenvalues of the covariance matrix and the use of k-medoids clustering. The DOAs of noncoherent signals are estimated separately using the proposed method, after determining the total number of sources, the number of noncoherent signals and the number of coherent groups. The DOAs of coherent signals are estimated in each group by constructing a virtual steering matrix, after determining the number of coherent signals in each coherence group by the MDL criterion. The proposed method does not require any primary information such as knowing the number of sources. The simulation results show that the proposed method is able to distinguish between the noncoherent and coherent signals, even in the cases of low SNRs and low snapshots. Also, in terms of detection accuracy and resolution of estimation, the proposed method shows improvement of over 1.2% and 43% respectively, compared to the conventional FBSS scheme.