Maximum likelihood DOA estimator based on importance sampling

Xiong Li, Jian Guo Huang, Qun Fei Zhang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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).

Original languageEnglish
Pages (from-to)1529-1532
Number of pages4
JournalTien Tzu Hsueh Pao/Acta Electronica Sinica
Volume33
Issue number8
StatePublished - Aug 2005

Keywords

  • Computational complexity
  • DOA estimation
  • Importance sampling
  • Maximum likelihood estimator
  • Monte Carlo

Fingerprint

Dive into the research topics of 'Maximum likelihood DOA estimator based on importance sampling'. Together they form a unique fingerprint.

Cite this