无线传感网络中分布式粒子滤波的目标追踪算法

在给出无线传感网络的传感器配置模型的基础上,提出了一种分布式粒子滤波DPF(d istributedparticle filter)算法,并实现了对网络中的一个运动目标的追踪。利用传感器模型,可将网络划分为一系列不相联系的传感器组,并在每个传感器组上运行一个局部粒子滤波,通过中心节点将估计状态传递给下一个传感器组的中心节点,依次实施对目标定位、跟踪。为减少网络间的通讯负荷和节约传感器节点能量,使用了高斯混合器模型(GMM),对局部粒子滤波的粒子和相应的权值进行近似;并提出了根据估计误差自适应激活传感器的优化算法。仿真结果表明,基于GMM近似的DPF在保持较高的估计精度的同时,能够大幅度减少网络间的通讯负荷。

Target tracking based on distributed particle filter in wireless sensor network

A distributed particle filter algorithm was proposed to track a moving target in a wireless sensor network based on the sensor model.With the sensor model,the wireless sensor network could be separated into a of series disjointed sensor cliques,and the local particle filter was running on each sensor clique.The estimated state distribution was propagated by the wireless communication channel from the central node of a sensor clique to the next central node.To reduce the communication burden,the Gaussian mixture mode(GMM) was proposed to approximate the belief;and an adaptive heuristic sensor configuration algorithm was also proposed.The tracking results of the simulation show that the proposed DPF based on GMM approximation has a high performance of tracking,and can reduce the communication burden significantly.