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

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Unconditional maximum likelihood approach for localization of near-field sources in 3D space.pdf (685.3Kb)
Date
2004
Author
Kabaoǧlu, Nihat
Çırpan, Hakan Ali
Paker, Selçuk
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.

Source

Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology

Pages

233-237

URI

https://hdl.handle.net/20.500.12469/168
https://dx.doi.org/10.1109/ISSPIT.2004.1433729

Collections

  • Araştırma Çıktıları / Scopus [1565]
  • Araştırma Çıktıları / WOS [1518]
  • Kadir Has Meslek Yüksekokulu / Kadir Has Vocational School [15]
  • Teknik Bilimler Meslek Yüksekokulu [6]

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DSpace software copyright © 2002-2015  DuraSpace
Contact Us | Send Feedback
Theme by 
@mire NV