A New Robust Kalman Filter with Adaptive Estimate of Time-Varying Measurement Bias

Yulong Huang; Guangle Jia; Badong Chen; Yonggang Zhang

doi:10.1109/LSP.2020.2983552

A New Robust Kalman Filter with Adaptive Estimate of Time-Varying Measurement Bias

Yulong Huang
, Guangle Jia
, Badong Chen
, Yonggang Zhang

Faculty of Electronic and Information Engineering

Harbin Engineering University

Research output: Contribution to journal › Article › peer-review

56 Scopus citations

Abstract

To better model the non-Gaussian heavy-tailed measurement noise with unknown and time-varying bias, a new Student's t-inverse-Wishart (STIW) distribution is presented. The STIW distribution is firstly written as a Gaussian, inverse-Wishart and normal-Gamma hierarchical form, from which a new robust Kalman filter is then derived based on the variational Bayesian method. Simulation results illustrate the potentials of the new derived robust Kalman filter for addressing the above measurement noise.

Original language	English
Article number	9050891
Pages (from-to)	700-704
Number of pages	5
Journal	IEEE Signal Processing Letters
Volume	27
DOIs	https://doi.org/10.1109/LSP.2020.2983552
State	Published - 2020

Keywords

Kalman filter
heavy-tailed measurement noise
linear system
unknown and time-varying measurement bias
variational Bayesian

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10.1109/LSP.2020.2983552

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abstract = "To better model the non-Gaussian heavy-tailed measurement noise with unknown and time-varying bias, a new Student's t-inverse-Wishart (STIW) distribution is presented. The STIW distribution is firstly written as a Gaussian, inverse-Wishart and normal-Gamma hierarchical form, from which a new robust Kalman filter is then derived based on the variational Bayesian method. Simulation results illustrate the potentials of the new derived robust Kalman filter for addressing the above measurement noise.",

keywords = "Kalman filter, heavy-tailed measurement noise, linear system, unknown and time-varying measurement bias, variational Bayesian",

author = "Yulong Huang and Guangle Jia and Badong Chen and Yonggang Zhang",

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AB - To better model the non-Gaussian heavy-tailed measurement noise with unknown and time-varying bias, a new Student's t-inverse-Wishart (STIW) distribution is presented. The STIW distribution is firstly written as a Gaussian, inverse-Wishart and normal-Gamma hierarchical form, from which a new robust Kalman filter is then derived based on the variational Bayesian method. Simulation results illustrate the potentials of the new derived robust Kalman filter for addressing the above measurement noise.

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KW - linear system

KW - unknown and time-varying measurement bias

KW - variational Bayesian

UR - https://www.scopus.com/pages/publications/85085617607

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JO - IEEE Signal Processing Letters

JF - IEEE Signal Processing Letters

M1 - 9050891

ER -

A New Robust Kalman Filter with Adaptive Estimate of Time-Varying Measurement Bias

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Keywords

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