Less conservative robust Kalman filtering using noise corrupted measurement matrix for discrete linear time-varying system

Won Sang Ra, Ick Ho Whang, Jin Bae Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, a new class of robust Kalman filtering problem is tackled for time-varying linear systems. Aside from the conventional problem settings, it is assumed that the measurement matrix be unknown and only a noise corrupted observation of it be available for state estimation. The influence of the noise contaminated measurement matrix on the Kalman filter estimate is analyzed in the sense of classical weighted leastsquares criterion. Stochastic approximations of estimation errors due to noisy measurement matrix make it possible to develop a less conservative robust estimation scheme. Reinterpreting the stochastic error compensation procedure, the less conservative robust Kalman filtering problem is defined as finding a unique minimum of an indefinite quadratic cost. By solving the single stage optimization problem, the robust filter recursion is derived. As well, its existence condition is recursively checked using the estimation error covariance. It is also shown that the proposed filter is consistent in probability. A practical design example related to frequency estimation of noisy sinusoidal signal is given to verify the estimation performance of the proposed scheme.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Industrial Technology, ICIT 2009
DOIs
Publication statusPublished - 2009 Jul 17
Event2009 IEEE International Conference on Industrial Technology, ICIT 2009 - Churchill, VIC, Australia
Duration: 2009 Feb 102009 Feb 13

Publication series

NameProceedings of the IEEE International Conference on Industrial Technology

Other

Other2009 IEEE International Conference on Industrial Technology, ICIT 2009
CountryAustralia
CityChurchill, VIC
Period09/2/1009/2/13

Fingerprint

Time varying systems
Error analysis
Error compensation
Frequency estimation
State estimation
Kalman filters
Linear systems
Costs

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Ra, W. S., Whang, I. H., & Park, J. B. (2009). Less conservative robust Kalman filtering using noise corrupted measurement matrix for discrete linear time-varying system. In 2009 IEEE International Conference on Industrial Technology, ICIT 2009 [4939683] (Proceedings of the IEEE International Conference on Industrial Technology). https://doi.org/10.1109/ICIT.2009.4939683
Ra, Won Sang ; Whang, Ick Ho ; Park, Jin Bae. / Less conservative robust Kalman filtering using noise corrupted measurement matrix for discrete linear time-varying system. 2009 IEEE International Conference on Industrial Technology, ICIT 2009. 2009. (Proceedings of the IEEE International Conference on Industrial Technology).
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Ra, WS, Whang, IH & Park, JB 2009, Less conservative robust Kalman filtering using noise corrupted measurement matrix for discrete linear time-varying system. in 2009 IEEE International Conference on Industrial Technology, ICIT 2009., 4939683, Proceedings of the IEEE International Conference on Industrial Technology, 2009 IEEE International Conference on Industrial Technology, ICIT 2009, Churchill, VIC, Australia, 09/2/10. https://doi.org/10.1109/ICIT.2009.4939683

Less conservative robust Kalman filtering using noise corrupted measurement matrix for discrete linear time-varying system. / Ra, Won Sang; Whang, Ick Ho; Park, Jin Bae.

2009 IEEE International Conference on Industrial Technology, ICIT 2009. 2009. 4939683 (Proceedings of the IEEE International Conference on Industrial Technology).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Ra WS, Whang IH, Park JB. Less conservative robust Kalman filtering using noise corrupted measurement matrix for discrete linear time-varying system. In 2009 IEEE International Conference on Industrial Technology, ICIT 2009. 2009. 4939683. (Proceedings of the IEEE International Conference on Industrial Technology). https://doi.org/10.1109/ICIT.2009.4939683