.png){kind=icon}
返回
State Sensitivity Evaluation Within UD Based Array Covariance Filters
.png){kind=icon}
OA
AI
DOI：10.1109/TAC.2013.2259093.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This technical note addresses the UD factorization based Kalman filtering (KF) algorithms. Using this important class of numerically stable KF schemes, we extend its functionality and develop an elegant and simple method for computation of sensitivities of the system state to unknown parameters required in a variety of applications. For instance, it can be used for efficient calculations in sensitivity analysis and in gradient-search optimization algorithms for the maximum likelihood estimation. The new theory presented in this technical note is a solution to the problem formulated by Bierman et al. in [1], which has been open since 1990s. As in the cited paper, our method avoids the standard approach based on the conventional KF (and its derivatives with respect to unknown system parameters) with its inherent numerical instabilities and, hence, improves the robustness of computations against roundoff errors.
Keyword:
Array algorithms
filter sensitivity equations
Kalman filter
UD factorization
期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
机构
引用论文
.png){kind=icon}
分享收藏Exercise interventions, physical function, and mobility after hip fracture: a systematic review and meta-analysis髋部骨折后的运动干预，身体功能和活动能力: 系统回顾和荟萃分析
PREAI
分享收藏分享收藏
查看更多内容