Optimum Complexing of Measurements when Maintaining a Maneuvering Object in Statistically Uncertain Situations

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Abstract

The problem of synthesizing optimal and quasi-optimal algorithms for complex information processing is solved using the methods of Markov theory for estimating random processes when maintaining a maneuvering object and two-channel vector observation with violations in statistically uncertain situations. The problem is solved in relation to a discrete-continuous Markov process for the case when its continuous part is a vector Markov sequence, and the discrete part is characterized by a three-component discrete Markov process, each component of which is described by a Markov chain to several positions. A block diagram of quasi-optimal complex information processing is given. Using a simple example, simulation modeling shows the performance of a quasi-optimal algorithm in statistically uncertain situations.

About the authors

A. N. Detkov

Federal State Unitary Enterprise “State Research Institute of Aviation Systems”, 125167, Moscow, Russia

Author for correspondence.
Email: detkov@gosniias.ru
Россия, Москва

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