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

作　　者：Wentao Shi Jianguo Huang Yunshan Hou 

机构地区：[1]College of Marine,Northwestern Polytechnical University,Xi'an 710072,R R.China

出　　处：《Journal of Systems Engineering and Electronics》2012年第2期173-178,共6页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China (60972152);the National Laboratory Foundation of China (9140C2304080607);the Aviation Science Fund (2009ZC53031);the Doctoral Foundation of Northwestern Polytechnical University (CX201002)

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 pro- posed 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.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 pro- posed 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.

关 键 词：multiple input multiple output （MIMO） sonar ant colonyoptimization （ACO） maximum likelihood （ML） direction of arrival（DOA） computational complexity. 

分 类 号：TP301[自动化与计算机技术—计算机系统结构] TN929.5[自动化与计算机技术—计算机科学与技术]