Research of methods and development of algorithms for integration of navigation information


Аuthors

Ovakimyan D. N.*, Zelenskiy V. A.**, Kapalin M. V.***, Yereskin I. S.****

Samara National Research University named after Academician S.P. Korolev, 34, Moskovskoye shosse, Samara, 443086, Russia

*e-mail: dd55@bk.ru
**e-mail: vaz-3@yandex.ru
***e-mail: no95typem@yandex.ru
****e-mail: sergeyeldoradotlt@gmail.com

Abstract

The issues of improving the accuracy and reliability of navigation information by combining signals received from different sources are considered. It is proposed to solve the problem using a modified Kalman filter selected as a result of a comparative analysis of known implementations in terms of accuracy and computational complexity. The software implementation of the algorithm is performed in the MAVROS environment. One of the most important tasks of the flight controller of an unmanned aerial vehicle is to evaluate the state vector. In general, this vector is multidimensional. The aim of the work is to select a method and develop an algorithm for determining the state vector by combining navigation information. The solution of the problem of aggregation of data obtained from independent sources is provided, as a rule, by nonlinear Kalman filtering. The classical algorithm is the extended Kalman filter EKF (extended Kalman filter), which is based on the linearization of the right part of the stochastic model to estimate the mathematical expectation of an unknown state vector and covariance matrix. EKF is one of the very first algorithms proposed for solving such problems. To date, there are more modern Kalman filters – sigma-point UKF (Unscented Kalman Filter), invariant extended IEKF (invariant extended Kalman filter), quadrature QKF. In this study, Nvidia Jetson Xavier NX was used as a hardware platform, which allows the use of resource-intensive algorithms for integrating navigation information. The use of special computing modules makes it possible to unload the flight controller and makes it relevant to study the effectiveness of modern Kalman filtering algorithms.

Keywords:

flight controller, algorithm, software, unmanned aerial vehicle

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