一种带遗忘因子的自适应卡尔曼滤波器及其在移动机器人中的应用  

Forgetting Factor Kalman Filter and Its Application to Mobile Robots

作　　者：虞波[1] 周翟和[1] 赵庆涛[1] 胡佳佳[1] 

机构地区：[1]南京航空航天大学自动化学院,江苏南京210016

出　　处：《机械制造与自动化》2016年第4期149-152,共4页Machine Building & Automation

基　　金：中央高校基本科研业务费专项资金(NS2014033);国家自然科学基金(61174102)

摘　　要：在移动机器人姿态估计过程中,由于难以建立准确的系统模型,导致参数量测不准确。采用一种自适应卡尔曼滤波器,通过加入遗忘因子的方式改变滤波器对状态估计的信任程度,改善了系统中噪声统计模型不准确对测量结果的影响。仿真结果表明,当经验知识不足导致建立姿态测量模型不准确时,自适应卡尔曼滤波器的滤波效果优于传统卡尔曼滤波器,提高了姿态信号的估计精度。Because it is difficult to establish the exact system model of wheeled mobile robot during the measurement process, its tilt angle is usually imprecise. This paper uses an adaptive Kalman filter with a forgetting factor to change the level of confidence of the present state estimation from the filter and improve the influence of the inaccuracy of noise statistical model in the system on the meas-urement results. Simulation results show that, when the measurement model can not used to measure the tilt anlge accurately, the adaptive Kalman filter is used to improve the accuracy of the tilt angle, because its effect is better than the traditional Kalman filter.

关 键 词：遗忘因子 自适应滤波器 姿态估计 移动机器人 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]