A low-complexity suboptimal filter for continuous-discrete linear systems with parametric uncertainties

Authors:
Vladimir Shin;Du Yong Kim;Georgy Shevlyakov;Kiseon Kim
Affiliations:
Department of Mechatronics, Gwangju Institute of Science and Technology, 1 Oryong-Dong Buk-Gu, Gwangju 500-712, South Korea;Department of Mechatronics, Gwangju Institute of Science and Technology, 1 Oryong-Dong Buk-Gu, Gwangju 500-712, South Korea;Department of Information and Communications,Gwangju Institute of Science and Technology, 1 Oryong-Dong Buk-Gu,Gwangju 500-712, South Korea;Department of Information and Communications,Gwangju Institute of Science and Technology, 1 Oryong-Dong Buk-Gu,Gwangju 500-712, South Korea
Venue:
Signal Processing
Year:
2007

Citing 3
Cited 1

Kalman filtering: with real-time applications (2nd ed.)

Kalman filtering: with real-time applications (2nd ed.)
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Abstract

We present a novel suboptimal filtering algorithm addressing estimation problems that arise in mixed continuous-discrete linear time-varying systems with stochastic parametric uncertainties. The suboptimal state estimate is formed by summing of local Kalman estimates with weights depending only on time instants t"k. In contrast to optimal weights, the suboptimal weights do not depend on current measurements, and thus the proposed filter is of a low-complexity and it can easily be implemented in real-time. High accuracy and efficiency of the suboptimal filter are demonstrated on the damper harmonic oscillator motion and the vehicle motion constrained to a plane.