JACIII Vol.23 No.2 pp. 165-174
doi: 10.20965/jaciii.2019.p0165


Unscented Kalman Filtering for Nonlinear Systems with Colored Measurement Noises and One-Step Randomly Delayed Measurements

Xinmei Wang*,**, Zhenzhu Liu*,**, Feng Liu*,**, and Wei Liu*,**

*School of Automation, China University of Geosciences
No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

**Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems
No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

March 21, 2018
September 28, 2018
March 20, 2019
unscented Kalman filtering, colored measurement noises, one-step randomly delayed measurements

Traditional unscented Kalman filtering (UKF) cannot solve the filtering problem for nonlinear systems with colored measurement noises and one-step randomly delayed measurements. To fix this problem, a new UKF algorithm is proposed in this paper. First, a system model with one-step randomly delayed measurements and colored measurement noises is established, wherein a first order Markov sequence model for whitening colored noises and an independently identical distributed Bernoulli variable for modeling one-step randomly delayed measurements is introduced. Second, an UKF is proposed for the above established models through unscented transformation by calculating the nonlinear states posterior mean and covariance based on the Bayesian filter framework. Specially, the proportional symmetric sampling method is used in the new UKF algorithm. Finally, the effectiveness and superiority of the proposed method is verified via simulation.

Cite this article as:
X. Wang, Z. Liu, F. Liu, and W. Liu, “Unscented Kalman Filtering for Nonlinear Systems with Colored Measurement Noises and One-Step Randomly Delayed Measurements,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.2, pp. 165-174, 2019.
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Last updated on Apr. 19, 2019