Maximum likelihood DOA estimation based on the cross-entropy method

Yen Chih Chen*, Yu-Ted Su

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006
Pages851-855
Number of pages5
DOIs
StatePublished - 1 Dec 2006
Event2006 IEEE International Symposium on Information Theory, ISIT 2006 - Seattle, WA, United States
Duration: 9 Jul 200614 Jul 2006

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8101

Conference

Conference2006 IEEE International Symposium on Information Theory, ISIT 2006
CountryUnited States
CitySeattle, WA
Period9/07/0614/07/06

Fingerprint Dive into the research topics of 'Maximum likelihood DOA estimation based on the cross-entropy method'. Together they form a unique fingerprint.

  • Cite this

    Chen, Y. C., & Su, Y-T. (2006). Maximum likelihood DOA estimation based on the cross-entropy method. In Proceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006 (pp. 851-855). [4036084] (IEEE International Symposium on Information Theory - Proceedings). https://doi.org/10.1109/ISIT.2006.261734