Unscented Kalman Filter for Nonlinear Systems with One-step Randomly Delayed Measurements and Colored Measurement Noises

2018 ◽  
Author(s):  
Xinmei Wang ◽  
Zhenzhu Liu ◽  
Leimin Wang ◽  
Feng Liu ◽  
Wei Liu
Keyword(s):  
Kalman Filter ◽  
Nonlinear Systems ◽  
Unscented Kalman Filter ◽  
One Step ◽  
Delayed Measurements ◽  
Measurement Noises

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

2019 ◽  
Vol 23 (2) ◽  
pp. 165-174
Author(s):  
Xinmei Wang ◽  
Zhenzhu Liu ◽  
Feng Liu ◽  
Wei Liu ◽  
◽  
...  
Keyword(s):  
Nonlinear Systems ◽  
Kalman Filtering ◽  
Sampling Method ◽  
Unscented Transformation ◽  
Markov Sequence ◽  
One Step ◽  
Delayed Measurements ◽  
Measurement Noises ◽  
Symmetric Sampling ◽  
Filtering Problem

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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