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

Xinmei Wang | Zendy; Zhenzhu Liu | Zendy; Feng Liu | Zendy; Liu Wei | Zendy

Open Access

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

Author(s) -

Xinmei Wang,

Zhenzhu Liu,

Feng Liu,

Liu Wei

Publication year - 2019

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2019.p0165

Subject(s) - kalman filter , computer science , nonlinear system , bernoulli's principle , control theory (sociology) , colored , unscented transform , covariance , transformation (genetics) , algorithm , colors of noise , extended kalman filter , filter (signal processing) , artificial intelligence , mathematics , invariant extended kalman filter , computer vision , statistics , engineering , materials science , composite material , control (management) , quantum mechanics , physics , aerospace engineering , chemistry , biochemistry , gene

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.

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