光电跟踪系统非线性新息自适应卡尔曼滤波算法  

Nonlinear Innovation Adaptive Kalman Filter Algorithm for Electro-optical Tracking

作　　者：王秋平[1] 左玲[1] 康顺[1] 

机构地区：[1]东北电力大学自动化工程学院,吉林吉林132012

出　　处：《光电工程》2011年第2期9-13,共5页Opto-Electronic Engineering

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

摘　　要：为解决非线性部分状态卡尔曼滤波算法中由于线性化误差所导致的滤波精度下降问题,提出采用UT变换方法计算系统状态误差方差,及基于新息自适应调整系统噪声方差,进而构成一种新的非线性自适应部分状态卡尔曼滤波算法,并总结出详细算法结构。同时,将此方法应用到非线性测量光电跟踪系统中,并与U卡尔曼滤波和非线性部分状态卡尔曼滤波进行性能对比。仿真实验结果证明,将UT变换和基于新息自适应调整系统噪声方差方法引入部分状态卡尔曼滤波是有效的,而且其性能明显优于U卡尔曼滤波和非线性部分状态卡尔曼滤波。In order to solve the problem of accuracy decline caused by the linearization error in nonlinear reduced state Kalman filter, a new nonlinear adaptive reduced state Kalman filter algorithm is provided by using UT transformation to calculate the covariance of the system state error and modify adaptively the system noise covariance based on innovation, and the algorithm structure is summarized in detail. Then, the algorithm is applied in nonlinear measurement electro-optical tracking system and the performances of nonlinear adaptive reduced state Kalman filter were compared with unscented Kalman filter and nonlinear reduced state Kalman filter. The Matlab simulation results show that applying UT transformation and modifying adaptively the system noise covariance based on innovation in reduced state Kalman filter is valid, and the performance outperforms those of the unscented Kalman filter and nonlinear reduced state Kalman filter.

关 键 词：卡尔曼滤波 光电跟踪 非线性 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]