基于Unscented粒子滤波的无序观测下水下无线传感器网络目标被动跟踪

Target Passive Tracking Based on Unscented Particle Filter with Out-of-Sequence Measurements in Underwater Wireless Sensor Networks

为提高水下无线传感器网络(UWSN)中的目标被动跟踪性能,提出了一种新的无序观测量(OOSM)处理算法.利用节点动态分簇建立分布式跟踪结构,簇头节点收集子节点的观测量形成本地估计.基于这种分布式结构,利用Unscented粒子滤波(UPF)结合新观测量,产生粒子滤波的建议密度分布,处理OOSM问题.详细推导了基于UPF的OOSM处理算法(OOSM-UPF)的具体实现步骤.利用转弯率建立机动目标跟踪模型,构建虚拟三维WSN仿真环境,比较了几种OOSM算法的性能.仿真结果表明,与其它算法相比,分布式OOSM-UPF算法的跟踪性能有了明显的提高.

A new out-of-sequence measurements(OOSM) processing algorithm for improving the performance of passive tracking in underwater wireless sensor networks(UWSN) is proposed.Decentralized tracking structure is organized by the dynamic clustering of sensor nodes,and cluster heads collect measurements from child nodes to form local estimates.The Unscented particle filter(UPF) is used to incorporate the most current measurement and to generate the proposal distribution of the particle filter with OOSM in this decentralized structure.The detailed implementation steps of OOSM processing based on the UPF(OOSM-UPF) are deduced.The passive tracking state space is modeled by the turn rate model,and 3D visual simulation scenario is constructed to test several filters for OOSM.Simulation results show that performance of OOSM-UPF is much improved than other schemes in UWSN.