A New Robust Kalman Filter With Adaptive Estimate of Time-Varying Measurement Bias
Yulong Huang, Guangle Jia, Badong Chen, Yonggang Zhang
Abstract
To better model the non-Gaussian heavy-tailed measurement noise with unknown and time-varying bias, a new Student's t-inverse-Wishart (STIW) distribution is presented. The STIW distribution is firstly written as a Gaussian, inverse-Wishart and normal-Gamma hierarchical form, from which a new robust Kalman filter is then derived based on the variational Bayesian method. Simulation results illustrate the potentials of the new derived robust Kalman filter for addressing the above measurement noise.
Topics & Concepts
Inverse-Wishart distributionWishart distributionKalman filterEnsemble Kalman filterFast Kalman filterGaussianInvariant extended Kalman filterNoise (video)Extended Kalman filterBayesian probabilityComputer scienceMathematicsGaussian noiseControl theory (sociology)AlgorithmArtificial intelligenceStatisticsMultivariate statisticsPhysicsImage (mathematics)Control (management)Quantum mechanicsTarget Tracking and Data Fusion in Sensor NetworksGaussian Processes and Bayesian InferenceFault Detection and Control Systems