A generalized Kalman filter with its precision in recursive form when the stochastic model is misspecified
P. J. G. Teunissen, Amir Khodabandeh, Dimitrios Psychas
Abstract
Abstract In this contribution, we introduce a generalized Kalman filter with precision in recursive form when the stochastic model is misspecified. The filter allows for a relaxed dynamic model in which not all state vector elements are connected in time. The filter is equipped with a recursion of the actual error-variance matrices so as to provide an easy-to-use tool for the efficient and rigorous precision analysis of the filter in case the underlying stochastic model is misspecified. Different mechanizations of the filter are presented, including a generalization of the concept of predicted residuals as needed for the recursive quality control of the filter.
Topics & Concepts
Recursion (computer science)Kalman filterRecursive filterGeneralizationFilter (signal processing)Recursive Bayesian estimationComputer scienceControl theory (sociology)Ensemble Kalman filterFilter designKernel adaptive filterMathematicsExtended Kalman filterVariance (accounting)Applied mathematicsState vectorAlgorithmStatisticsControl (management)Root-raised-cosine filterBayesian probabilityArtificial intelligenceMathematical analysisComputer visionAccountingClassical mechanicsPhysicsBusinessGNSS positioning and interferenceInertial Sensor and NavigationTarget Tracking and Data Fusion in Sensor Networks