摘要
本文处理带来知噪声统计系统的自适应Kalman滤波问题.基于白噪声估值器,本文提出新的噪声统计递推估值器,比Sage和Husa的噪声统计估值器精度高,并改进了Yoshimura和Soeda的结果,两个仿真例子说明了本文结果的有效性.
This paper deals with adaptie Kalman filtering problem for systems with unknown nise statistics. Based on white noise estimators, some new kinds of constant or timevarying noise statistics estimtors are presented. The results obtained by Sage and Husa[1], Yoshimura and Soeda[2], are improved. Two simulation exmples show usefulness of the proposed results.
出处
《黑龙江大学自然科学学报》
CAS
1991年第3期66-72,共7页
Journal of Natural Science of Heilongjiang University
关键词
自适应
KALMAN滤波
白噪声
估值器
Adaptive Kalman filtering, Noise statistics extimators, suboptimal unbiased maximum a posteriori(MAP) estimation
