基于改进Rao-Blackwellized粒子滤波的WSN被动目标跟踪  

Passive Target Tracking Using Modified Rao-Blackwellized Particle Filter

作　　者：周红波[1] 万福[1] 蔡祥[1] 

机构地区：[1]海军指挥学院,南京211800

出　　处：《火力与指挥控制》2015年第6期44-47,共4页Fire Control & Command Control

基　　金：国家"八六三"计划基金资助项目(2007AA01Z309)

摘　　要：Rao-Blackwellized粒子滤波虽然适合系统状态包含线性高斯分量的非线性状态估计,但是由于其计算量较大,不适用于实时性较高的被动目标跟踪情况。针对Rao-Blackwellized粒子滤波的不足,提出了改进的Rao-Blackwellized粒子滤波算法用于WSN被动目标跟踪。新的算法由一个粒子滤波和一个卡尔曼滤波组成,在执行过程中,粒子滤波和卡尔曼滤波相互交换信息,并行运行。计算机仿真结果表明,新的算法能够更好地减少计算量,提高跟踪的实时性。Rao-Blackwellized particle filter has advantages in solving nonlinear estimation when the state has linear Gaussian sub-state than particle filter. However,it is limited in passive target tracking which needs high performance of real time for its computational complexity. A new modified Rao-Blackwellized particle filter consist of a particle filer and a Kalman filter is proposed in the paper and be applied in passive target tracking with WSN. At each time,the particle filter and the Kalman filter exchange information with each other for estimation at next time. The simulation results prove that the new algorithm can reduce the computation complexity and improve the performance of real time of passive tracking.

关 键 词：被动目标跟踪 无线传感器网络 状态估计 粒子滤波 卡尔曼滤波 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]