Marginalized particle filters for Bayesian estimation of Gaussian noise parameters

Date

2010-07-26

Author

Zhao, Yuxin
Yin, Feng
Gunnarsson, Fredrik
Amirijoo, Mehdi
Özkan, Emre
Gustafsson, Fredrik

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The particle filter provides a general solution to the nonlinear filtering problem with arbitrarily accuracy. However, the curse of dimensionality prevents its application in cases where the state dimensionality is high. Further, estimation of stationary parameters is a known challenge in a particle filter framework. We suggest a marginalization approach for the case of unknown noise distribution parameters that avoid both aforementioned problem. First, the standard approach of augmenting the state vector with sensor offsets and scale factors is avoided, so the state dimension is not increased. Second, the mean and covariance of both process and measurement noises are represented with parametric distributions, whose statistics are updated adaptively and analytically using the concept of conjugate prior distributions. The resulting marginalized particle filter is applied to and illustrated with a standard example from literature.

Subject Keywords

Noise, Particle measurements, Atmospheric measurements, Noise measurement, Joints, Equations, Mathematical model

URI

https://hdl.handle.net/11511/39620

DOI

https://doi.org/10.1109/icif.2010.5712016

Conference Name

2010 13th International Conference on Information Fusion

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

Y. Zhao, F. Yin, F. Gunnarsson, M. Amirijoo, E. Özkan, and F. Gustafsson, “Marginalized particle filters for Bayesian estimation of Gaussian noise parameters,” presented at the 2010 13th International Conference on Information Fusion, Edinburgh, UK, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39620.