An iterative nonlinear filter using variational Bayesian optimization

Hu, Y, Wang, X, Lan, H, Wang, Z, Moran, B and Pan, Q 2018, 'An iterative nonlinear filter using variational Bayesian optimization', Sensors, vol. 18, no. 12, pp. 1-17.


Document type: Journal Article
Collection: Journal Articles

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Title An iterative nonlinear filter using variational Bayesian optimization
Author(s) Hu, Y
Wang, X
Lan, H
Wang, Z
Moran, B
Pan, Q
Year 2018
Journal name Sensors
Volume number 18
Issue number 12
Start page 1
End page 17
Total pages 17
Publisher MDPIAG
Abstract We propose an iterative nonlinear estimator based on the technique of variational Bayesian optimization. The posterior distribution of the underlying system state is approximated by a solvable variational distribution approached iteratively using evidence lower bound optimization subject to a minimal weighted Kullback-Leibler divergence, where a penalty factor is considered to adjust the step size of the iteration. Based on linearization, the iterative nonlinear filter is derived in a closed-form. The performance of the proposed algorithm is compared with several nonlinear filters in the literature using simulated target tracking examples.
Subject Signal Processing
Stochastic Analysis and Modelling
Keyword(s) Kullback-Leibler divergence
Nonlinear filtering
Target tracking
Variational bayes
DOI - identifier 10.3390/s18124222
Copyright notice © 2018 by the authors. Licensee MDPI, Basel, Switzerland. Creative Commons Attribution (CC BY) license
ISSN 1424-8220
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