On the use and misuse of Bayesian filters

Tiancheng Li, Javier Prieto, Juan M. Corchado, Javier Bajo

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

Since the groundbreaking work of the Kalman filter in the 1960s, considerable effort has been devoted to discrete time filters for dynamic state estimation, especially including a variety of suboptimal implementations of the Bayesian filter. The essence of the Bayesian filter is to make the (sub)optimum fusion of the observation information in time sequence based on the hidden Markov model of the state process. While admitting the success of filters in many cases, this study investigates the cases when they in fact loose to the deterministic observation-only (O2) inference that infers the estimate by using the observation information only without modeling the state dynamics. Special attention has been paid to quantitatively analyzing when and why the Bayesian filter will underperform the O2 inference from the information fusion perspective. Classic state space models have shown that the O2 inference can perform better (in terms of both accuracy and computing speed) than filters in certain cases. Therefore attention is desired for the use of a filter when the model is not guaranteed to be accurate and much approximation is used.

源语言英语
主期刊名2015 18th International Conference on Information Fusion, Fusion 2015
出版商Institute of Electrical and Electronics Engineers Inc.
838-845
页数8
ISBN(电子版)9780982443866
出版状态已出版 - 14 9月 2015
已对外发布
活动18th International Conference on Information Fusion, Fusion 2015 - Washington, 美国
期限: 6 7月 20159 7月 2015

出版系列

姓名2015 18th International Conference on Information Fusion, Fusion 2015

会议

会议18th International Conference on Information Fusion, Fusion 2015
国家/地区美国
Washington
时期6/07/159/07/15

指纹

探究 'On the use and misuse of Bayesian filters' 的科研主题。它们共同构成独一无二的指纹。

引用此