Fast DOA estimation algorithm for MIMO sonar based on ant colony optimization

Wentao Shi, Jianguo Huang, Yunshan Hou

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

The maximum likelihood (ML) estimator demonstrates remarkable performance in direction of arrival (DOA) estimation for the multiple input multiple output (MIMO) sonar. However, this advantage comes with prohibitive computational complexity. In order to solve this problem, an ant colony optimization (ACO) is incorporated into the MIMO ML DOA estimator. Based on the ACO, a novel MIMO ML DOA estimator named the MIMO ACO ML (ML DOA estimator based on ACO for MIMO sonar) with even lower computational complexity is proposed. By extending the pheromone remaining process to the pheromone Gaussian kernel probability distribution function in the continuous space, the proposed algorithm achieves the global optimum value of the MIMO ML DOA estimator. Simulations and experimental results show that the computational cost of MIMO ACO ML is only 1/6 of the MIMO ML algorithm, while maintaining similar performance with the MIMO ML method.

Original languageEnglish
Article number6190865
Pages (from-to)173-178
Number of pages6
JournalJournal of Systems Engineering and Electronics
Volume23
Issue number2
DOIs
StatePublished - Apr 2012

Keywords

  • Ant colony optimization (ACO)
  • Computational complexity
  • Direction of arrival (DOA)
  • Maximum likelihood (ML)
  • Multiple input multiple output (MIMO) sonar

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