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An Iterative Nonlinear Filter Using Variational Bayesian Optimization
- Source :
- Sensors, Vol 18, Iss 12, p 4222 (2018)
- Publication Year :
- 2018
- Publisher :
- MDPI AG, 2018.
-
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.
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 18
- Issue :
- 12
- Database :
- Directory of Open Access Journals
- Journal :
- Sensors
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.83d78ecfc544cbfa415a6a69326cf5a
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/s18124222