Bayesian Filtering with Unknown Process Noise Covariance

Laz E., ORGUNER U.

28th International Conference on Information Fusion, FUSION 2025, Rio de Janeiro, Brezilya, 7 - 11 Temmuz 2025, (Tam Metin Bildiri) identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.23919/fusion65864.2025.11123949
  • Basıldığı Şehir: Rio de Janeiro
  • Basıldığı Ülke: Brezilya
  • Anahtar Kelimeler: Bayesian filtering, in-verse Wishart distribution, linear Gaussian system, unknown process noise covariance
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.