基于无迹卡尔曼滤波的被动多传感器融合跟踪  

Multi-passive-sensor fusion tracking based on unscented Kalman filter

作　　者：杨柏胜[1] 姬红兵[1] 

机构地区：[1]西安电子科技大学电子工程学院,西安710071

出　　处：《控制与决策》2008年第4期460-463,共4页Control and Decision

基　　金：国家自然科学基金项目(60677040)

摘　　要：针对被动传感器观测的非线性问题,将无迹变换引入卡尔曼滤波算法中.进一步,针对其弱可观测性,采用多个被动传感器集中式融合跟踪策略,提出了基于无迹卡尔曼滤波的被动多传感器融合跟踪算法.以3个被动站跟踪为例进行仿真研究,结果表明所提出的算法可达到比经典的扩展卡尔曼滤波算法更高阶的跟踪精度.According to the nonlinear problem for the passive-sensor system, unscented transformation is introduced into Kalman filter based on passive target tracking algorithm. Then, multi-passive-sensor fusion scheme is adopted to deal with the difficulty of observability problem for single passive sensor system. Thus, multi-passive-sensor fusion tracking algorithm based on unscented Kalman filter （UKF） is proposed. Simulation results show that the proposed algorithm perform better than extended Kalman filter （EKF） in target tracking for a three-passive-sensor system.

关 键 词：无迹卡尔曼滤波 集中式融合跟踪 可观测性 非线性 

分 类 号：TN976[电子电信—信号与信息处理]