Gaussian Filtering for Simultaneously Occurring Delayed and Missing Measurements

Approximate filtering algorithms in nonlinear systems assume Gaussian prior and predictive density and remain popular due to ease of implementation as well as acceptable performance. However, these algorithms are restricted by two major assumptions: they assume no missing or delayed measurements. Ho...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.100746-100762
Main Authors: Naik, Amit Kumar, Kumar, Guddu, Upadhyay, Prabhat Kumar, Date, Paresh, Singh, Abhinoy Kumar
Format: Article
Language:English
Subjects:
Algorithms
Bayes methods
Biomedical measurement
Delayed measurements
Delays
Electronic mail
Extended Kalman filter
Filtering
Gaussian filtering
Gaussian processes
Kalman filters
missing measurements
nonlinear Bayesian filtering
Nonlinear systems
Particle measurements
Time measurement
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Summary:Approximate filtering algorithms in nonlinear systems assume Gaussian prior and predictive density and remain popular due to ease of implementation as well as acceptable performance. However, these algorithms are restricted by two major assumptions: they assume no missing or delayed measurements. However, practical measurements are frequently delayed and intermittently missing. In this paper, we introduce a new extension of the Gaussian filtering to handle the simultaneous occurrence of the delay in measurements and intermittently missing measurements. Our proposed algorithm uses a novel modified measurement model to incorporate the possibility of the delayed and intermittently missing measurements. Subsequently, it redesigns the traditional Gaussian filtering for the modified measurement model. Our algorithm is a generalized extension of the Gaussian filtering, which applies to any of the traditional Gaussian filters, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), and cubature Kalman filter (CKF). A further contribution of this paper is that we study the stochastic stability of the proposed method for its EKF-based formulation. We compared the performance of the proposed filtering method with the traditional Gaussian filtering (particularly the CKF) and three extensions of the traditional Gaussian filtering that are designed to handle the delayed and missing measurements individually or simultaneously.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3208119