论文部分内容阅读
针对资源受限条件下大规模无线传感器网络中协作目标跟踪问题,提出一个基于粒子群优化的节点调度方案.该方案利用高斯粒子滤波算法和方差交叉融合算法获得目标状态预测信息,进而选择下一时刻簇成员节点,并构造了通信能耗的代价函数,利用粒子群优化方法选择最佳的簇头节点,减少了节点调度的计算复杂度,同时保持了较好的跟踪精度.仿真结果验证了所提出方案的有效性.
Aiming at the problem of cooperative target tracking in large-scale wireless sensor networks with limited resources, a particle swarm optimization-based node scheduling scheme is proposed, which uses Gaussian particle filter and cross-variance crossover algorithm to obtain target state prediction information, Time cluster membership nodes, and constructs the cost function of communication energy consumption, selects the best cluster head node by using particle swarm optimization method, reduces the computational complexity of node scheduling while maintaining good tracking accuracy.The simulation results verify The effectiveness of the proposed solution.