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Self-tuning Information Fusion Kalman Filter with Input Estimation

  • Xiaojun Sun
  • Guangming Yan
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 129)

Abstract

For the multisensor linear discrete time-invariant systems with unknown constant input and unknown noise statistics, the on-line estimators of unknown input and filtering gain are obtained based on CARMA innovation model. For the multisensor stochastic control systems with known input and noise statistics, the optimal information fusion steady-state Kalman filter are presented based on Fadeeva formula. Furthermore, a self-tuning information fusion Kalman filter with input estimation is presented. Based on the dynamic error system analysis method, its asymptotic optimality is proved, i.e. it converges to the optimal fusion steady-state Kalman filter in a realization. A simulation example for a target tracking system with three sensors shows its effectiveness.

Keywords

Multisensor information fusion input estimation self-tuning Kalman filter convergence asymptotic optimality 

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.The Electrical Engineering Institute, Department of AutomationHeilongjiang UniversityHarbinChina
  2. 2.The College of Mechanical and Electrical Engineering, Department of AutomationHeilongjiang UniversityHarbinChina

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