Research Information System
28th International Conference on Information Fusion, FUSION 2025, Rio de Janeiro, Brazil, 7 - 11 July 2025, (Full Text)
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.