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Improvements in computational aspects of Interval Kalman Filtering enhanced by constraint propagation

Jun Xiong 1 Carine Jauberthie 1 Louise Travé-Massuyès 1
1 LAAS-DISCO - Équipe DIagnostic, Supervision et COnduite
LAAS - Laboratoire d'analyse et d'architecture des systèmes
Abstract : This paper deals with computational aspects of interval kalman filtering of discrete time linear models with bounded uncertainties on parameters and gaussian measurement noise. In this work, we consider an extension of conventional Kalman filtering to interval linear models [1]. As the expressions for deriving the Kalman filter involve matrix inversion which is known to be a difficult problem. One must hence find a way to implement or avoid this tricky algebraic operation within an interval framework. To solve the interval matrix inversion problem and other problems due to interval calculus, we propose an original approach combining the set inversion algorithm SIVIA and constraint satisfaction propagation. Several contractors are proposed to limit overestimation effects propagating within the interval Kalman filter recursive structure. Thus the description of our approach is followed by an application and we compare the proposed approach with interval kalman filtering developped in [1].
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Submitted on : Friday, December 28, 2018 - 11:00:30 AM
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  • HAL Id : hal-01966341, version 1


Jun Xiong, Carine Jauberthie, Louise Travé-Massuyès. Improvements in computational aspects of Interval Kalman Filtering enhanced by constraint propagation. IEEE International Workshop Electronics, Control, Measurement, Signals and Their Application to Mechatronics, Jun 2013, Toulouse, France. 6p. ⟨hal-01966341⟩



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