An Adaptive Kalman Filter With Inaccurate Noise Covariances in the Presence of Outliers
Hao Zhu, Guorui Zhang, Yongfu Li, Henry Leung
Abstract
In this article, a novel variational Bayesian (VB) adaptive Kalman filter with inaccurate nominal process and measurement noise covariances (PMNC) in the presence of outliers is proposed. The probability density functions of state transition and measurement likelihood are modeled as Gaussian–Gamma mixture distributions. The VB inference is used to perform the state and PMNC simultaneously. Simulations show that the effectiveness of the proposed method with inaccurate noise covariances in the presence of outliers environments.
Topics & Concepts
OutlierKalman filterNoise (video)Extended Kalman filterComputer scienceEnsemble Kalman filterAlgorithmGaussianNoise measurementProbability density functionGaussian noiseGaussian processMathematicsControl theory (sociology)Artificial intelligenceStatisticsNoise reductionPhysicsControl (management)Image (mathematics)Quantum mechanicsTarget Tracking and Data Fusion in Sensor NetworksBayesian Methods and Mixture ModelsFuzzy Systems and Optimization