Maximum likelihood DOA estimator based on importance sampling

Xiong Li, Jian Guo Huang, Qun Fei Zhang

科研成果: 期刊稿件文章同行评审

1 引用 (Scopus)

摘要

Maximum Likelihood Estimator (MLE) has been shown to be the best performance in parameter estimation. However, the computation burden of MLE is very large. In order to resolve the question of computation burden, Monte Carlo methods are combined with maximum likelihood DOA estimator. A novel Maximum Likelihood DOA Estimator based on Importance Sampling (ISMLE) is proposed. ISMLE not only keeps the excellent performance of the original MLE, but also reduces the computation greatly, from the computational complexity O(LK) of original method to O(K×H).

源语言英语
页(从-至)1529-1532
页数4
期刊Tien Tzu Hsueh Pao/Acta Electronica Sinica
33
8
出版状态已出版 - 8月 2005

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