ISSN 1991-2927
 

ACP № 4 (62) 2020

Author: "Leonid Iurevich Korolev"

Konstantin Konstantinovich Vasilev, Doctor of Sciences in Engineering; graduated from the Radioengineering Faculty of Leningrad Electrotechnical Institute n.a. V.I. Ulyanov (Lenin); Professor of the Department of Telecommunications at Ulyanovsk State Technical University, Honored Worker of Science and Technology of the Russian Federation, Corresponding Member of the Academy of Sciences of the Republic of Tatarstan; an author of monographs, manuals, and articles in the field of statistical synthesis and analysis of information systems. e-mail: vkk@ulstu.ruK. K. Vasilev,

Leonid Iurevich Korolev, graduated from Ogarev Mordovia State University; Postgraduate Student of the Department of Infocommunication Technologies and Communication Systems of Ogarev Mordovia State University; an author of articles in the field of the unmanned aerial vehicle navigation. e-mail: l.y.korolev@ yandex.ruL. I. Korolev

Opportunities for reduction of computational costs when determining the coordinates of autonomous vehicle group60_2.pdf

The article deals with the known and new algorithms proposed by authors for integrating the estimates of eigen and mutual coordinates and velocities of movement for an arbitrary number of autonomous vehicles. The impact of uncontrolled external disturbances on the system is taken into account. Four versions for developing the navigation-parameter processing algorithms on the basis of two-stage integration of observations, Kalman vector and scalar filters are proposed and examined. The first technique involves estimating the coordinates and speeds of all aerial vehicles with a Kalman vector filter. The second technique is to integrate velocities at the first stage and to use the Kalman vector filter at the second stage. The third one is based on separate integration of coordinates and velocities at the first stage with subsequent processing of estimates with a scalar Kalman filter. In the fourth technique, observations of eigen and mutual coordinates are integrated and processed by a scalar filter, taking into account direct observations of eigen speed of the apparatus. Expressions for covariance error matrices are derived, and the comparison results of the effectiveness of the considered approaches are given. Computational costs are analyzed when estimating the location by the use of each of the considered algorithms.

Integration of estimates, coordinates, state vector, observation model, stochastic equation, Kalman filter, covariance of errors.

2020_ 2

Sections: Automated control systems

Subjects: Automated control systems.



Konstantin Konstantinovich Vasilev, Doctor of Science in Engineering, Professor of the Department of Telecommunications at Ulyanovsk State Technical University, Honored Worker of Science and Technology of the Russian Federation, Corresponding Member of the Academy of Sciences of the Republic of Tatarstan; graduated from the Radioengineering Faculty of Leningrad Electrotechnical Institute; an author of monographs, manuals and articles in the field of statistical synthesis and the analysis of information systems. e-mail: vkk@ulstu.ru.K. Vasilev,

Leonid Iurevich Korolev, graduated from Ogarev Mordovia State University; Postgraduate Student of the Department of Infocommunication Technologies and Communication Systems of Ogarev Mordovia State University; an author of articles in the field of navigation of unmanned aerial vehicles. e-mail: l.y.korolev@yandex.ru.L. Korolev

Estimation Of Varying Coordinates Of the Group Of Autonomous Vehicles 57_10.pdf

The problem of integrating the estimation of own and mutual coordinates of different accuracy for an arbitrary number of independent devices considering impact of external indignations of different intensity is solved. The integration possibility of aircraft navigation parameters is considered in two ways. The first algorithm uses the vector linear Kalman filter to increase the accuracy of the dynamically changed coordinates of the group of unmanned aerial vehicles. The second method consists in dynamic estimating using the quasioptimum two-stage procedure that includes static assessment of coordinates and Kalman filtering. Expressions for detecting the covariation matrixes of integration errors are obtained by two methods. The efficiency of methods for estimating the navigation parameters of the group of unmanned aerial vehicles is compared under different initial conditions in a generic form and for the application case of the existing navigation systems. The results of comparative analysis of efficiency of two considered approaches are given.

Estimation integration, vehicle coordinates, model of observations, stochastic equation of a state, Kalman filter, covariances of mistakes.

2019_ 3

Sections: Mathematical modeling

Subjects: Mathematical modeling.



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