A Modified Bayesian Filter for Randomly Delayed Measurements

The traditional Bayesian approximation framework for filtering in discrete time systems assumes that the measurement is available at every time instant. But in practice, the measurements could be randomly delayed. In the literature, the problem has been examined and solution is provided by restricti...

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Bibliographic Details
Published in:IEEE transactions on automatic control Vol. 62; no. 1; pp. 419 - 424
Main Authors: Singh, Abhinoy Kumar, Date, Paresh, Bhaumik, Shovan
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Approximation
Bayes methods
Bayesian analysis
Bayesian framework of filtering
Covariance matrix
cubature quadrature Kalman filter
Current measurement
Delay
Delays
Discrete time systems
Filtration
Kalman filters
Loss measurement
Mathematical analysis
Mathematical model
nonlinear filtering
Quadratures
random delay in measurements
ISSN:0018-9286, 1558-2523
Online Access:Get full text
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Summary:The traditional Bayesian approximation framework for filtering in discrete time systems assumes that the measurement is available at every time instant. But in practice, the measurements could be randomly delayed. In the literature, the problem has been examined and solution is provided by restricting the maximum number of delay to one or two time steps. This technical note develops an approach to deal with the filtering problems with an arbitrary number of delays in measurement. Pursuing this objective, traditional Bayesian approximation to nonlinear filtering problem is modified by reformulating the expressions of mean and covariances which appear during the measurement update. We use the cubature quadrature rule to evaluate the multivariate integral expressions for the mean vector and the covariance matrix which appear in the developed filtering algorithm. We compare the new algorithm which accounts for delay with the existing CQKF heuristics on two different examples and demonstrate how accounting for a random delay improves the filtering performance.
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ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2016.2531418