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Effectiveness of Bayesian filters: An information fusion perspective.
- Source :
-
Information Sciences . Feb2016, Vol. 329, p670-689. 20p. - Publication Year :
- 2016
-
Abstract
- The general solution for dynamic state estimation is to model the system as a hidden Markov process and then employ a recursive estimator of the prediction–correction format (of which the best known is the Bayesian filter) to statistically fuse the time-series observations via models. The performance of the estimator greatly depends on the quality of the statistical mode assumed. In contrast, this paper presents a modeling-free solution, referred to as the observation-only (O 2 ) inference, which infers the state directly from the observations. A Monte Carlo sampling approach is correspondingly proposed for unbiased nonlinear O 2 inference. With faster computational speed, the performance of the O 2 inference has identified a benchmark to assess the effectiveness of conventional recursive estimators where an estimator is defined as effective only when it outperforms on average the O 2 inference (if applicable). It has been quantitatively demonstrated, from the perspective of information fusion, that a prior “biased” information (which inevitably accompanies inaccurate modelling) can be counterproductive for a filter, resulting in an ineffective estimator. Classic state space models have shown that a variety of Kalman filters and particle filters can easily be ineffective (inferior to the O 2 inference) in certain situations, although this has been omitted somewhat in the literature. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 329
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 111344322
- Full Text :
- https://doi.org/10.1016/j.ins.2015.09.041