Efficient Clustering of Non-coherent and Coherent Components Regardless of Sources’ Powers for 2D DOA Estimation

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Conventional decorrelation techniques that resolve all signals simultaneously are not efficient in mixture scenarios of non-coherent and coherent signals. In newer methods for one-dimensional arrays, non-coherent signals and coherent groups are resolved separately. However, employing an unreliable and non-adaptive threshold is the most significant disadvantage of these methods. On the other hand, they cannot be implemented for two-dimensional arrays. To deal with these issues, the signals separation using k-medoids clustering (SSKMC) algorithm was presented. Although the SSKMC algorithm does not have any of the shortcomings mentioned above, it relies on a basic limiting assumption that the sources should be equi-power. Therefore, the practical application of the SSKMC algorithm is facing a serious problem. In this paper, the SSKMC algorithm is extended so that it can be used even if the sources’ powers are not the same. First, the two-dimensional array is divided into several parallel linear sub-arrays. Then, by defining a components separation matrix, and employing its eigenvalues, the non-coherent and coherent components are identified. The effectiveness of the proposed solution is proven by mathematical facts. Simulation results verify the proofs and the benefit of the proposed solution.