Bayesian Filtering and Smoothing with Unknown Measurement Noise Covariance

Laz E., ORGUNER U.

2023 IEEE International Radar Conference, RADAR 2023, Sydney, Australia, 6 - 10 November 2023 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/radar54928.2023.10371025
  • City: Sydney
  • Country: Australia
  • Keywords: Bayesian filtering, Bayesian smoothing, inverse Wishart distribution, Linear Gaussian system, unknown measurement noise covariance
  • Middle East Technical University Affiliated: Yes

Abstract

Bayesian filtering and smoothing problems with unknown measurement noise covariance are investigated for linear Gaussian systems. The measurement noise covariance is assumed to be inverse Wishart distributed. A Bayesian filter and smoother calculating the joint posteriors for the state and the measurement noise covariance are derived by using a scale Gaussian approximation of t-distribution and moment matching. The proposed filter and smoother are non-iterative unlike the existing Bayesian solutions in the literature. The performance of the proposed algorithms is illustrated on a two-dimensional target tracking scenario. The simulation results show that the proposed filter and smoother have similar performance as the state of the art solutions with lower computational load.