Unconditional maximum likelihood approach for localization of near-field sources in 3-D space
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Date
2004
Authors
Kabaoğlu, Nihat
Çırpan, Hakan Ali
Paker, Selçuk
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
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.
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Turkish CoHE Thesis Center URL
Citation
9
WoS Q
N/A
Scopus Q
N/A
Source
Volume
Issue
Start Page
233
End Page
237