Maximum likelihood DOA estimator based on importance sampling

Jianguo Huang, Da Xie, Xiong Li, Qunfei Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

DOA estimation is an important research area in array signal processing. Maximum Likelihood Estimator (MLE) has been shown to perform best among all the methods. However, the MLE requires a multidimensional grid search and the computational burden increases exponentially with the dimension. So it is difficult to be used in realtime applications. In order to reduce the computation, Monte Carlo methods are combined with MLE. A novel Maximum Likelihood DOA Estimator based on Importance Sampling (ISMLE) is proposed. ISMLE not only reduces the computational complexity of the original MLE from O(LK) to O(KXH), but also keeps the perfect original performance of the MLE. Simulation results show that ISMLE keeps the excellent performance of MLE, and simultaneously it reduces the computation obviously. Also ISMLE performs better than MUSIC and MiniNorm, especially in low SNRs.

Original languageEnglish
Title of host publication2006 IEEE Region 10 Conference, TENCON 2006
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)1424405491, 9781424405497
DOIs
StatePublished - 2006
Event2006 IEEE Region 10 Conference, TENCON 2006 - Hong Kong, China
Duration: 14 Nov 200617 Nov 2006

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450

Conference

Conference2006 IEEE Region 10 Conference, TENCON 2006
Country/TerritoryChina
CityHong Kong
Period14/11/0617/11/06

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