Iterative Smoother-Based Variance Estimation

被引：8
|
作者：
Einicke, G. A. [1 ]
Falco, G. [2 ]
Dunn, M. T. [1 ]
Reid, D. C. [1 ]
机构：
[1] CSIRO, Pullenvale 4069, Australia
[2] ISMB, I-10138 Turin, Italy
来源：
IEEE SIGNAL PROCESSING LETTERS | 2012年 / 19卷 / 05期
关键词：
EM algorithms; Kalman filtering; smoothing; EM ALGORITHM; NAVIGATION;
D O I：
10.1109/LSP.2012.2190278
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The minimum-variance smoother solution for input estimation is described and it is shown that the resulting estimates are unbiased. The smoothed input and state estimates are used to iteratively identify unknown process noise variances. The use of smoothed estimates, as opposed to filtered estimates, leads to improved approximate Cramer-Rao lower bounds for the unknown parameters. It is also shown that the sequence of iterates are monotonic and asymptotically approach the actual values under prescribed conditions. A nonlinear mining navigation application is described in which unknown parameters are estimated.
引用
收藏
页码：275 / 278
页数：4
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