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

Wentao Shi, Jianguo Huang, Yunshan Hou

科研成果: 期刊稿件文章同行评审

32 引用 (Scopus)

摘要

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.

源语言英语
文章编号6190865
页(从-至)173-178
页数6
期刊Journal of Systems Engineering and Electronics
23
2
DOI
出版状态已出版 - 4月 2012

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