在多目标跟踪中,要求无线传感器网络在满足跟踪精度的前提下,最大限度地降低对传感器资源的使用.基于这一目的,适当选择节点避免共线度过高,并采用APIT实现精确定位,同时考虑跟踪簇总能耗设计节点调度目标函数,采用二进制量子粒子群优化算法解决传感器资源冲突问题.仿真结果表明:虽然基于BQPSO的节点调度算法比基于PSO的节点调度算法在能耗上增加了17.47％,但定位精度可以提高31.84％.算法在提高定位精度的同时最大限度地降低了对资源的使用,有效延长了无线传感器网络的工作寿命.%On the premise of meeting tracking precision in wireless sensor networks, it is necessary that the maximum limit to reduce the use of sensor resources in multiple target tracking. Based on this objective, it designs node tracking objective function through choosing nodes appropriately to avoid too high colinearity, then using APIT to achieve accurate positioning and considering the total energy consumption of cluster, uses node scheduling algorithm by binary quantum particle swarm optimization to solve sensor resource conflict problem. The simulation results show that the nodes scheduling algorithm based on BQPSO than the nodes scheduling algorithm based on PSO increased 17.47% in energy consumption, but improved 31. 84% in location accuracy. The algorithm reaches tracking accuracy requirements, minimizes the network resource utilization and prolongs the working life of wireless sensor networks.
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