带噪声统计估计器的Unscented卡尔曼滤波器设计  

Design of unscented Kalman filter with noise statistic estimator

作　　者：赵琳[1] 王小旭 薛红香[1] 夏全喜[1] 

机构地区：[1]哈尔滨工程大学自动化学院,哈尔滨150001

出　　处：《控制与决策》2009年第10期1483-1488,共6页Control and Decision

基　　金：国家自然科学基金项目(60474046)

摘　　要：针对传统Unscented卡尔曼滤波器(UKF)在噪声先验统计未知或不准确时滤波精度下降甚至发散的问题,基于极大后验(MAP)估计原理,设计了一种带噪声统计估计器的UKF.该UKF滤波算法在进行状态估计的同时,能实时估计和修正噪声均值和协方差.相比于传统UKF,所提出的UKF具有应对噪声统计变化的自适应能力.仿真结果表明了该UKF滤波算法的有效性.For the problem that the aecuray of the conventional UKF declines and further diverges when the prior noise statistic is unknown or inaccurate, an unscented Kalman filter （UKF） with noise statistic estimator is designed. This UKF filtering algorithm based on maximum a posterior （MAP） estimation can estimate and correct the mean and covariance of the noise in real time while it estimates the states. The proposed UKF has the adaptive capability of dealing with variable noise statistic. The simulation results show the effectiveness of the proposed UKF filtering algorithm.

关 键 词：Unscented卡尔曼滤波器 极大后验估计 噪声统计估计器 自适应能力 

分 类 号：TP202[自动化与计算机技术—检测技术与自动化装置]