Unconditional maximum likelihood approach for localization of near-field sources in 3-D space
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Since maximum likelihood (ML) approaches have better resolution performance than the conventional localization methods in the presence of less number and highly correlated source signal samples and low signal to noise ratios we propose unconditional ML (UML) method for estimating azimuth elevation and range parameters of near-field sources in 3-D space in this paper. Besides these superiorities stability asymptotic unbiasedness asymptotic minimum variance properties are motivated the application of ML approach. Despite these advantages ML estimator has computational complexity. Fortunately this problem can be tackled by the application of Expectation/Maximization (EM) iterative algorithm which converts the multidimensional search problem to one dimensional parallel search problems in order to prevent computational complexity. © 2004 IEEE.