Bayesian DOA estimation method using Population Monte Carlo

Fei Hua, Xiao Hong Shen, Zhao Chen, Fu Zhou Yang, Jiang Jian Gu

科研成果: 书/报告/会议事项章节会议稿件同行评审

3 引用 (Scopus)

摘要

Bayesian maximum a posteriori (BMAP) DOA estimation method has a better performance than MUSIC method at low signal to noise ratio (SNR) and few snapshots. However, it suffers a heavy computational complexity due to multi-dimensional search. Monte Carlo methods such as Markov Chain Monte Carlo (MCMC) method can effectively solve this problem. MCMC method may provide a local optimization for it is difficult to assess when the Markov Chain has reached the stationary state. Population Monte Carlo (PMC) which uses sequential techniques draws a set of particles and provides an unbiased estimate at each iteration. Thus provides a global optimization and can enhance the computational efficiency. In this paper, the PMC method is introduced and used for Bayesian DOA estimation in order to reduce the complexity. Simulation results show that it has better performance than MUSIC method at low SNR or few snapshots. Compared with BMAP, it can reduce the computation and keep high resolution performance at low SNR.

源语言英语
主期刊名2012 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012
158-161
页数4
DOI
出版状态已出版 - 2012
活动2012 2nd IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012 - Hong Kong, 中国
期限: 12 8月 201215 8月 2012

出版系列

姓名2012 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012

会议

会议2012 2nd IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012
国家/地区中国
Hong Kong
时期12/08/1215/08/12

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