Generalized Maximum likelihood Algorithm for Direction-of-Arrival Estimation of Coherent Sources |
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Authors: | Wang Bu-hong Wang Yong-liang Chen Hui and Guo Ying |
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Affiliation: | (1) Key Research Laboratory, Air Force Radar Academy, Wuhan, 430010, China;(2) Telecommunication Engineering Institute, Air Force Engineering University, Xi’an, 710077, China |
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Abstract: | The generalized maximum likelihood (GML) algorithm for direction-of-arrival estimation is proposed. Firstly, a new data model
is established based on generalized steering vectors and generalized array manifold matrix. The GML algorithm is then formulated
in detail. It is flexible in the sense that the arriving sources may be a mixture of multiclusters of coherent sources, the
array geometry is unrestricted, and the number of sources resolved can be larger than the number of sensors. Secondly, the
comparison between the GML algorithm and the conventional deterministic maximum likelihood (DML) algorithm is presented based
on their respective geometrical interpretation. Subsequently, the estimation consistency of GML is proved, and the estimation
variance of GML is derived. It is concluded that the performance of the GML algorithm coincides with that of the DML algorithm
in the incoherent sources’ case, while it improves greatly in the coherent source case. By using genetic algorithm, GML is
realized, and the simulation results illustrate its improved performance compared with DML, especially in the case of multiclusters
of coherent sources.
Translated from “Generalized Maximum Likelihood Algorithm for Direction-of-Arrival Estimation of Coherent Sources” published
in Journal of Electronics & Information Technology, 2004, 26(2): 225-232 (in Chinese) |
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Keywords: | direction-of-arrival estimation ML estimation genetic algorithm |
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