Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/120195
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Type: Journal article
Title: Robust Kalman filters based on Gaussian scale mixture distributions with application to target tracking
Author: Huang, Y.
Zhang, Y.
Shi, P.
Wu, Z.
Qian, J.
Chambers, J.
Citation: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017; OnlinePubl:1-15
Publisher: IEEE
Issue Date: 2017
ISSN: 2168-2216
2168-2232
Statement of
Responsibility: 
Yulong Huang, Yonggang Zhang, Peng Shi, Zhemin Wu, Junhui Qian, and Jonathon A. Chambers
Abstract: In this paper, a new robust Kalman filtering framework for a linear system with non-Gaussian heavy-tailed and/or skewed state and measurement noises is proposed through modeling one-step prediction and likelihood probability density functions as Gaussian scale mixture (GSM) distributions. The state vector, mixing parameters, scale matrices, and shape parameters are simultaneously inferred utilizing standard variational Bayesian approach. As the implementations of the proposed method, several solutions corresponding to some special GSM distributions are derived. The proposed robust Kalman filters are tested in a manoeuvring target tracking example. Simulation results show that the proposed robust Kalman filters have a better estimation accuracy and smaller biases compared to the existing state-of-the-art Kalman filters.
Keywords: Gaussian scale mixture (GSM) distribution; heavy-tailed noise, Kalman filter; skewed noise; state estimation; target tracking; variational Bayesian (VB)
Rights: © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
RMID: 0030083944
DOI: 10.1109/TSMC.2017.2778269
Grant ID: http://purl.org/au-research/grants/arc/DP170102644
Appears in Collections:Electrical and Electronic Engineering publications

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