基于分群粒子群优化的传感器调度方法

Sensor scheduling method based on grouping particle swarm optimization

对面向目标跟踪任务的多传感器多任务调度问题进行研究。考虑到探测目标的运动特性,采用扩展卡尔曼滤波法实施目标跟踪,以成功调度任务的综合优先权、目标跟踪精度以及传感器网络的能源消耗为指标,建立了多传感器多任务调度的混合整数规划模型。提出一种基于分群机制的分群粒子群算法对模型进行求解,该方法通过粒子分群,提高对问题域的全局搜索能力,避免算法过快收敛和发生早熟。实验结果表明,该方法用于传感器调度问题,具有较好的求解性能。

The problem of multi-sensor multi-task scheduling for target tracking tasks is studied.In view of the mobility of targets,the extended Kalman filter method for target tracking is adopted.A mixed integral programming model is founded,in which three contradictory objectives,the overall priority for scheduling tasks successfully,the accuracy of target tracking and the consumption of energy in the sensor network,are considered.The grouping particle swarm optimization algorithm is then introduced,which avoids too fast convergence and prematurity by partitioning the swarm into groups and enabling each group evolving toward its own best position.Numerical results show that the presented method is efficient in solving the sensor scheduling problem.