K.K. Vasiliev – Dr.Sc.(Eng.), Professor, 

Department «Telecommunications», Ulyanovsk State Technical University

E-mail: vkk@ulstu.ru

A.V. Bobkov – Post-graduate Student, 

Department «Information and communication technologies and communication systems»,  Mordovia Ogarev State University (Saransk)

E-mail: avbobkov@list.ru

L.Yu. Korolev – Post-graduate Student, 

Department «Information and communication technologies and communication systems», 

Mordovia Ogarev State University (Saransk)

E-mail: l.y.korolev@yandex.ru

The need to present special requirements for the accuracy of positioning of autonomous devices has been determined. Existing approaches to solving the problem are considered, among them application of the technology of simple localization and mapping, orientation by markers. Works on formation of the system and interaction of autonomous devices in the group are highlighted. The task is to form algorithms for complex estimates of natural and mutual coordinates and speeds of movement of an arbitrary number of sets, as well as to use SLAM technology when performing group tasks by autonomous sets.

Algorithm of estimation of coordinates and speeds of autonomous sets by method of static integration with subsequent application of simple Kalman filter is given. Static integration is based on formation of weight coefficient vectors for observations of navigation parameters of each autonomous apparatus. A method of complexing based on a Kalman vector filter using a state prediction vector of a group of autonomous apparatuses is considered. Described is a method of estimating a Kalman vector filter based on observations of fixed landmarks. Own navigation data are measured by navigation systems GPS, GLONASS or inertial systems. Mutual navigation parameters are determined by means of inter-flight navigation tools. Observation of fixed objects is performed by vision systems. The vector of navigation parameters can include eigen- and mutual coordinates and speeds, as well as coordinates of fixed objects. Equations of systems dynamics are given. Expressions of search for covariance matrices of navigation parameter integration errors are described.

Comparative analysis of the considered methods was carried out taking into account observations of own and mutual speeds of movement of autonomous vehicles and without such observations. The efficiency of the considered algorithms was compared. The dependence of integration efficiency on the number of autonomous sets in the group was analyzed. Simulation of algorithms based on examples of real navigation systems used in autonomous unmanned underwater vehicles and unmanned aerial vehicles.

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