基于改进自适应粒子滤波的无线传感网船舶追踪

Ship tracking of wireless sensor network based on improved adaptive particle filter

针对无线传感网(wireless sensor network,WSN)节点在海上动态环境下利用接收信号强度指示器(recieved signal strength indicator,RSSI)对船舶追踪精度不高以及计算量大等问题,提出改进的自适应粒子滤波算法。该算法采用优化边界阈值的方式,在重采样阶段采用KL散度(KullbackLeibler divergence,KLD)采样方法实现自适应选择采样粒子,这使得节点采样的计算量减少,从而缩短采样的计算时间。仿真结果表明:该算法可在保障追踪精度的同时,提高自适应度,减少节点计算量,并且能很好地适应海上环境。

Aiming at the issue that the accuracy of ship tracking is not high and the computational complexity is high based on wireless sensor network （WSN） whose nodes use the received signal strength indicator （RSSI） in the dynamic marine environment, an improved adaptive particle filter algorithm is proposed. In the algorithm, the method of optimizing boundary threshold is used, and the Kullback-Leibler divergence （KLD） resampling method is used at the resampling stage to select sampling particles adaptively, which reduces the computational complexity of node sampling and thus shortens the computing time of sampling. The simulation results show that the proposed algorithm can improve the self-adaptability, reduce the computational amount of nodes, and adapt to the marine environment well while keeping the tracking accuracy.