Informatics and Applications
2026, Volume 20, Issue 3, pp 33-46
STABLE ALGORITHMS FOR ADAPTIVE STATE ESTIMATION OF DYNAMIC SYSTEMS UNDER RANDOM TIME DELAYS OF OBSERVATIONS
- A. V. Bosov
- S. A. Bosov
- I. V. Uryupin
Abstract
New computationally stable algorithms are proposed for adaptive filtering of the state of an object moving in an aquatic environment, based on acoustic observations with random time delays. A previously developed version of the extended Kalman filter, adapted to the time-delay model and built upon the method of linear pseudoobservations, is supplemented with two adaptive filter modifications for the case of unknown covariances of dynamic disturbances and measurement errors. To estimate the covariances, stable adaptive Kalman filter schemes are used, derived from the analysis of measurement residuals with respect to the filtering estimates. These schemes are improved by additional optimization in order to account for the positive diagonal structures of the unknown covariances assumed by the motion model and generated by the linear pseudoobservations. A large-scale numerical experiment was carried out, confirming the operability of the proposed adaptation schemes. The calculations employed the same model as in previous works, which made it possible to separately assess the influence of the adaptive formulation and to outline possible directions for further development of the methodology.
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[+] About this article
Title
STABLE ALGORITHMS FOR ADAPTIVE STATE ESTIMATION OF DYNAMIC SYSTEMS UNDER RANDOM TIME DELAYS OF OBSERVATIONS
Journal
Informatics and Applications
2026, Volume 20, Issue 3, pp 33-46
Cover Date
2026-30-09
DOI
10.14357/19922264260303
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
stochastic filtering; stochastic system with random observation delays; extended Kalman filter (EKF); EKF based on the method of linear pseudoobservations; adaptive Kalman filter (AKF)
Authors
A. V. Bosov  , S. A. Bosov , and I. V. Uryupin
Author Affiliations
Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
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