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A robust Gaussian approximate filter for nonlinear systems with heavy tailed measurement noises

Lookup NU author(s): Professor Jonathon Chambers

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Abstract

The scale matrix and degrees of freedom (dof) parameter of a Student's t distribution are important for nonlinear robust inference, and it is difficult to determine exact values in practical application due to complex environments. To solve this problem, an improved robust Gaussian approximate (GA) filter is derived based on the variational Bayesian approach, where the state together with unknown scale matrix and dof parameter are inferred. The proposed filter is applied to a target tracking problem with measurement outliers, and its performance is compared with an existing robust GA filter with fixed scale matrix and dof parameter. The results show the efficiency and superiority of the proposed filter as compared with the existing filter.


Publication metadata

Author(s): Huang YL, Zhang YG, Li N, Chambers J

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year of Conference: 2016

Pages: 4209-4213

Online publication date: 19/05/2016

Acceptance date: 01/01/1900

ISSN: 2379-190X

Publisher: IEEE

URL: http://dx.doi.org/10.1109/ICASSP.2016.7472470

DOI: 10.1109/ICASSP.2016.7472470

Library holdings: Search Newcastle University Library for this item

ISBN: 9781479999880


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