Underdetermined Blind Identification for Uniform Linear Array by a New Time-Frequency Method

This paper proposes a novel underdetermined blind identification method with several new single-source points (SSPs) detection criteria for uniform linear array, where the mixing matrix is complex-valued. These new criteria are based on quadratic time-frequency distribution and employed to detect the SSPs so that the complex-valued mixing matrix can be estimated more precisely. To further enhance the estimation accuracy, a modified peak detection method is presented by exploiting the known source number. Finally, the complex-valued mixing matrix can be obtained by performing a clustering algorithm on samples at selected SSPs. One of the outstanding superiorities for the proposed algorithm is that the new criteria are strict enough for the points that are not the SSPs, which ensures the estimation accuracy of the mixing matrix. The other is that the performance of estimation precision is high even in the noisy case. Numerical simulation results verify the superiority of the proposed algorithm over the existing algorithms.