Two‐dimensional DOA estimation for multi‐path environments by accurate separation of signals using k ‐medoids clustering

Abstract

The authors propose a two-dimensional direction-of-arrival (DOA) estimation for multi-path environments, in which there are uncorrelated, partially correlated and coherent signals. The inability to identify non-coherent signals from coherent ones results in a considerable waste of sensors. In this work, an adaptive and automated threshold is considered for the efficient separation of noise, non-coherent signals, and coherent groups. First, non-coherent signals, coherent groups, and noise subspace are separated using k-medoids clustering. After determining the number of sources, non-coherent signals and coherent groups, non-coherent DOAs are estimated separately. The number of coherent signals in each coherence group is determined by the minimum descriptive length criterion. Finally, coherent DOAs are estimated in each group by constructing a coherent estimation matrix. The proposed method does not require any prior information such as knowing the number of signals or the covariance matrix of uncorrelated signals. The simulation results show that the proposed method is able to distinguish between the non-coherent and coherent signals, even at low signal-to-noise ratios and a small number of snapshots. Also, in terms of detection probability and estimation accuracy, it shows an improvement of over 1.2 and 83%, respectively, compared with the conventional forward−backward spatial smoothing scheme.