Maximum likelihood DOA estimation based on the cross-entropy method

Yen Chih Chen*, Yu-Ted Su

*Corresponding author for this work

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

In this paper, we propose two simulation based maximum likelihood (ML) methods to estimate the direction of arrival (DOA) by a novel combination of the Cross-Entropy (CE) method and the polynomial parameterization scheme. The CE method is an efficient stochastic approximation method for solving both discrete and continuous optimization problems. We bridge the ML approach and the stochastic search algorithm by properly randomizing the desired parameters. Numerical results show that the proposed CE-based algorithms yield highly accurate DOA estimation with fast convergence rate while requiring only linear processing complexity. Compared with the conventional iterative quadratic maximization likelihood (IQML) method, the proposed algorithms can alleviate the error propagation effect in low signal to noise ratio (SNR) region and asymptotically approach the Cramér-Rao bound in high SNR region.

原文English
主出版物標題Proceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006
頁面851-855
頁數5
DOIs
出版狀態Published - 1 十二月 2006
事件2006 IEEE International Symposium on Information Theory, ISIT 2006 - Seattle, WA, United States
持續時間: 9 七月 200614 七月 2006

出版系列

名字IEEE International Symposium on Information Theory - Proceedings
ISSN(列印)2157-8101

Conference

Conference2006 IEEE International Symposium on Information Theory, ISIT 2006
國家United States
城市Seattle, WA
期間9/07/0614/07/06

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