An Improved Kalman Filter With Adaptive Estimate of Latency Probability

In this brief, an improved Kalman filter is proposed for a linear system with one-step randomly delayed measurement and unknown latency probability. The measurement likelihood function which is a weighted sum of two Gaussian distributions is transformed into an exponential multiplication form via im...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. II, Express briefs Express briefs, 2020-10, Vol.67 (10), p.2259-2263
Main Authors: Wang, Zhao, Huang, Yulong, Zhang, Yonggang, Jia, Guangle, Chambers, Jonathon
Format: Article
Language:English
Subjects:
Adaptive filters
Computer simulation
Covariance matrices
Estimation
Gaussian distribution
Kalman filter
Kalman filters
latency probability
Linear systems
Multiplication
one-step random delay
Probability density function
Random variables
State space models
State vectors
variational Bayesian
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Summary:In this brief, an improved Kalman filter is proposed for a linear system with one-step randomly delayed measurement and unknown latency probability. The measurement likelihood function which is a weighted sum of two Gaussian distributions is transformed into an exponential multiplication form via importing a discrete Bernoulli random variable. Then, an hierarchical Gaussian form of the state-space model is established. Finally, an improved Kalman filter is deduced to estimate jointly the augmented state vector and the unknown parameters employing the variational Bayesian and state augmentation approaches. Simulation study indicates that the improved method has superior performance in estimation accuracy than the existing methods on the basis of accurate estimation of the unknown and time-varying latency probability.
ISSN:1549-7747
1558-3791
DOI:10.1109/TCSII.2019.2952090