Improved robust Kalman filtering for uncertain systems with missing measurements
Rezaei, Hossein, Mohamed, Shady, Esfanjani, Reza Mahboobi and Nahavandi, Saeid 2014, Improved robust Kalman filtering for uncertain systems with missing measurements. In Loo, C. K., Yap, K. S., Wong, K. W., Teoh, A. and Huang, K. (ed), Neural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part III, Springer, Berlin, Germany, pp.509-518.
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Improved robust Kalman filtering for uncertain systems with missing measurements
In this paper, a novel robust finite-horizon Kalman filter is developed for discrete linear time-varying systems with missing measurements and normbounded parameter uncertainties. The missing measurements are modelled by a Bernoulli distributed sequence and the system parameter uncertainties are in the state and output matrices. A two stage recursive structure is considered for the Kalman filter and its parameters are determined guaranteeing that the covariances of the state estimation errorsare not more than the known upper bound. Finally, simulation results are presented to illustrate the outperformance of the proposed robust estimator compared with the previous results in the literature.
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