光电跟踪模型噪声卡尔曼滤波算法  

Model noise Kalman filter algorithm for electro-optical tracking

作　　者：王秋平[1] 李凤[1] 马春林[1] 

机构地区：[1]东北电力大学自动化工程学院,吉林132012

出　　处：《中国惯性技术学报》2012年第5期573-576,582,共5页Journal of Chinese Inertial Technology

基　　金：东北电力大学博士基金(BSJXM-200802);吉林省教育厅项目(吉教科合字2009100);吉林市科技发展计划资助项目(20090601)

摘　　要：为减小滤波性能对跟踪目标状态空间模型噪声的敏感性,提出一种基于新息同时更新系统噪声方差和测量噪声方差方法,并将其与非线性卡尔曼滤波类算法相结合,构成一类适用于光电跟踪目标的自适应非线性卡尔曼滤波算法。同时将此方法应用到非线性测量光电跟踪系统中,并与扩展卡尔曼滤波和U卡尔曼滤波进行性能对比。仿真实验结果证明该方法可以实时调整系统噪声方差和测量噪声方差,有效地避免由于系统模型噪声统计特性不准确所带来的滤波性能下降的问题,而且其性能明显优于扩展卡尔曼滤波和U卡尔曼滤波。In order to reduce the sensitivity of the filtering performance to the model noise in the target state space model,the method of updating the system noise covariance and measurement noise variance was proposed.By combining it with nonlinear Kalman filter,an adaptive nonlinear Kalman filter was constituted which is suitable for applying in electro-optical target tracking.Meanwhile it was applied in nonlinear measurement electro-optical tracking system,and compared with those of extended Kalman filter and unscented Kalman filter.The Matlab simulation results show that this method can adjust measurement noise covariance and system noise covariance in real time,and can effectively avoid the problem of filter performance degradation caused by the inaccurately statistical properties of the measurement noise and system noise,and the performance by the model noise Kalman filter significantly outperforms those of the extended Kalman filter and the unscented Kalman filter.

关 键 词：卡尔曼滤波 光电跟踪 非线性 自适应 

分 类 号：U666.1[交通运输工程—船舶及航道工程]