Bayesian Filtering with Unknown Process Noise Covariance

Date

2025-01-01

Author

Laz, Eray
Orguner, Umut

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Bayesian filtering problem is considered in linear Gaussian systems with unknown inverse Wishart distributed process noise covariance. A Bayesian filter is formulated to approximate the joint posterior for the state and the process noise covariance. This involves utilizing moment matching and a scale Gaussian mixture approximation of the t-distribution. The proposed filter distinguishes itself by being non-iterative, setting it apart from existing Bayesian solutions given in the literature. The algorithm's performance is demonstrated through its application to a scenario where a target is tracked in two dimensions. Simulation results indicate that the proposed filter achieves similar or better performance compared to state-of-theart solutions while demanding a reduced computational load.

Subject Keywords

Bayesian filtering, in-verse Wishart distribution, linear Gaussian system, unknown process noise covariance

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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105015862266&origin=inward
https://hdl.handle.net/11511/115835

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Department of Electrical and Electronics Engineering, Conference / Seminar