{"title":"DOA estimation using fast EM algorithm","authors":"Pei-Jung Chung, J. Böhme","doi":"10.1109/ISSPA.2001.949792","DOIUrl":null,"url":null,"abstract":"We study the direction of arrival estimation using expectation-maximization (EM) algorithm. The EM algorithm is a general and popular numerical method for finding maximum likelihood estimates which usually has a simple implementation and stable convergence. However, the computational cost caused by the slow convergence of the EM algorithm is still immense for the direction finding problem. Motivated by componentwise convergence of the EM algorithm, we suggest the use of smaller search spaces after a few iterations. In this way, the overall computational cost can be reduced drastically. An adaptive procedure which determines the search spaces involved in the maximization (M) step is proposed. With numerical experiments we demonstrate the improvement of the computational efficiency by using the proposed algorithm.","PeriodicalId":236050,"journal":{"name":"Proceedings of the Sixth International Symposium on Signal Processing and its Applications (Cat.No.01EX467)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Sixth International Symposium on Signal Processing and its Applications (Cat.No.01EX467)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSPA.2001.949792","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract

We study the direction of arrival estimation using expectation-maximization (EM) algorithm. The EM algorithm is a general and popular numerical method for finding maximum likelihood estimates which usually has a simple implementation and stable convergence. However, the computational cost caused by the slow convergence of the EM algorithm is still immense for the direction finding problem. Motivated by componentwise convergence of the EM algorithm, we suggest the use of smaller search spaces after a few iterations. In this way, the overall computational cost can be reduced drastically. An adaptive procedure which determines the search spaces involved in the maximization (M) step is proposed. With numerical experiments we demonstrate the improvement of the computational efficiency by using the proposed algorithm.
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基于快速EM算法的DOA估计
利用期望最大化算法研究了到达估计的方向。EM算法是一种常用的求最大似然估计的数值方法,通常具有实现简单、收敛稳定等优点。然而,对于测向问题来说,EM算法收敛速度慢所带来的计算代价仍然是巨大的。由于EM算法的组件收敛性,我们建议在几次迭代后使用更小的搜索空间。通过这种方式,可以大大降低总体计算成本。提出了一种确定最大化(M)步骤所涉及的搜索空间的自适应方法。通过数值实验证明了该算法对计算效率的提高。
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