A Novel Multi-Passive-Sensor Target Tracking Algorithm Based On Gaussian Filter

摘要：

This paper presents a new multi-passive-sensor target tracking algorithm which yields a nonlinear state estimator called Gaussian filter based on deterministic sampling. Firstly, this state estimator employs a deterministic sample selection scheme, where a parametric density function representation of the sample points is employed to approximate the cumulative distribution function of the prior Gaussian density. The performance of the filter is more accurate than the extended Kalman Filter (EKF) and the unscented Kalman Filter (UKF) in nonlinear dynamic system. Secondly, in order to avoid the unobservability problem of passive target tracking, a nonlinear measurement model of multiple passive sensors is founded. Finally, the algorithm performance has been verified by illustrating some simulation results.

关键词： Gaussian Filter Target Tracking Multi-Passive-Sensor

作者: Juan-li Liu Hong-bing Ji Hui Guo

作者单位: School of Electronic Engineering, Xidian University, Xian, P. R. China

会议类型: 国际会议

会议名称: 2009 International Workshop on Information Security and Application(2009 信息安全与应用国际研讨会)

会议地点: 青岛

会议语种:英文

页码: 108-111

在线出版日期: 2009-11-21（万方平台首次上网日期，不代表论文的发表时间）