An algorithm for finding the best solution of stochastic ML estimation of DOA
Haihua Chen, Masakiyo Suzuki · 2007
This paper addresses the issue of uniqueness of Stochastic or unconditional Maximum Likelihood (SML) estimation for direction-of-arrival (DOA) finding. The SML estimation is not unique inherently in the noise-free case unlike the Deterministic or conditional ML (DML) estimation of DOA. Since also in the noisy case, there is no guarantee that the SML estimation is unique, global search techniques fail to find DOA. However, the one closest to the DML estimate among the several global solutions can be considered to be the most adequate solution for DOA. This paper proposes an algorithm which uses a local search together with the DML estimation as initialization to find the best solution of the SML estimation. Finally some simulation results are shown to demonstrate the proposed algorithm is effective.