An Iterative Nonlinear Filter Using Variational Bayesian Optimization

Sensors

Bill Moran¹  Zengfu Wang²  Quan Pan²  Hua Lan²  Yumei Hu²  Xuezhi Wang³ 

[1] Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC 3010, Australia;School of Automation, Northwestern Polytechnical University, Xi’an 710072, China;School of Engineering, RMIT University, Melbourne 3000, Australia;

关键词: target tracking;    nonlinear filtering;    variational Bayes;    Kullback-Leibler divergence;   

DOI  :  10.3390/s18124222

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown