摘要
本文从时间序列分析观点,基于观测过程的 CARMA 新息模型,提出了稳态 Kalman 滤波增益估计的两种新算法及相应的自校正 Kalman 滤波器,形成一种新的自适应 Kalman 滤波技术.新算法比Mehra 和 Tajima 的算法简单.作为应用例子,对于一个简单的跟踪系统,导出了带输入估计的自校正α-β滤波器,仿真结果说明了新算法的有效性.
From the point of view of time series analysis,based on CARMA innovation model ofmeasurement process,this paper presents two new algorithms for estimating the steady-state Kalman filtergain,and the corresponding self-tuning Kalman filters,which form a new adaptive Kalman filtering tech-nique.New algorithms are simpler than that of Mehra and Tajima.As an application example,self-tuning α-β tracking filter with input estimation is given,and simulation results show the effectivenessof the new algorithms.
出处
《信息与控制》
CSCD
北大核心
1991年第6期20-26,共7页
Information and Control
基金
黑龙江自然科学基金资助课题
关键词
KALMAN滤波
增益估计
算法
滤波器
estimation of steady-state Kalman filter gain
adaptive Kalman filtering technique
self-tuning Kalman filter
self-tuning α-β tracking filter
