Year: 2024
Type: article
Abstract: The article is focused on the optimal state estimation fusion algorithm for discrete-continuous systems.The aim of the study is to create an effective fusion strategy for combining data obtained from continuousand discrete information sources to improve the accuracy and reliability of state estimation incomplex dynamic systems. The paper discusses in detail the theoretical foundations of the proposed method,including the mathematical description of the continuous and discrete system models, the optimizationcriterion formulation, and the derivation of equations for calculating the complementation weights. Particularattention is paid to the analysis of conditions under which the proposed algorithm provides animprovement in estimation accuracy compared to the use of only continuous or only discrete filter.The authors present the results of numerical modeling demonstrating the developed algorithm efficiencyon the example of autonomous underwater vehicle motion parameter estimation. It is shown that the proposedfusion method allows to significantly reduce the estimation errors compared to the use of separatefilters, especially in conditions of incompleteness and noise in measurements. In conclusion, it is statedthat the developed algorithm is promising for application in various fields related to information processingin complex technical systems, such as navigation, motion control, monitoring of the objects andprocesses. It is noted that the proposed approach can be generalized to the case of complexing data froma larger number of information sources and adapted to different types of discrete-continuous systems.The article is considered to be valuable for specialists in control theory, signal and information processing,as well as for developers of navigation and motion control systems. The research results can find practicalapplication in the creation of high-precision state estimation systems in various technical applications
Authors: М.М. Migalin, V.A. Obukhovets
Institution: Southern Federal University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4406765054