Ant colony optimization for bearings-only maneuvering target tracking in sensors network  

作　　者：Benlian XU Zhiquan WANG Zhengyi WU 

机构地区：[1]Department of Information and Control Engineering, Changshu Institute of Technology, Changshu Jiangsu 215500, China [2]School of Automation, Nanjing University of Science and Technology, Nanjing Jiangsu 210094, China

出　　处：《控制理论与应用（英文版）》2007年第3期301-306,共6页

基　　金：This paper was supported by the Natural Science Foundation of Jiangsu province of China (BK2004132)

摘　　要：In this paper, the problem of bearings-only maneuvering target tracking in sensors network is investigated. Two objectives are proposed and optimized by the ant colony optimization （ACO）, then two kinds of node searching strategies of the ACO algorithm are presented. On the basis of the nodes determined by the ACO algorithm, the interacting multiple models extended Kalman filter （IMMEKF） for the multi-sensor bearings-only maneuvering target tracking is introduced. Simulation results indicate that the proposed ACO algorithm performs better than the Closest Nodes method. Furthermore, the Strategy 2 of the two given strategies is preferred in terms of the requirement of real time.In this paper, the problem of bearings-only maneuvering target tracking in sensors network is investigated. Two objectives are proposed and optimized by the ant colony optimization （ACO）, then two kinds of node searching strategies of the ACO algorithm are presented. On the basis of the nodes determined by the ACO algorithm, the interacting multiple models extended Kalman filter （IMMEKF） for the multi-sensor bearings-only maneuvering target tracking is introduced. Simulation results indicate that the proposed ACO algorithm performs better than the Closest Nodes method. Furthermore, the Strategy 2 of the two given strategies is preferred in terms of the requirement of real time.

关 键 词：Ant colony algorithm Multi-objective optimization Maneuvering target tracking BEARINGS-ONLY 

分 类 号：O242.23[理学—计算数学]