Unconditional maximum likelihood approach for localization of near-field sources in 3D space

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 3D 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.