Comparative analysis of information integration architectures of strapdown inertial navigation systems for unmanned aerial vehicles


DOI: 10.34759/trd-2021-117-11

Аuthors

Ermakov P. G.*, Gogolev A. A.**

State Institute of Aviation Systems, 7, Victorenko str., Moscow, 125319, Russia

*e-mail: pavel-ermakov-1998@mail.ru
**e-mail: kirbizz8@yandex.ru

Abstract

The article describes a comparative analysis of separate and loosely coupled architectures of navigation information integration in the case of the following sensors application:

– inertial sensors based on micro-electro-mechanical systems (MEMS) technologies comprising accelerometers measuring proper acceleration, and gyros, measuring rotation speed of a body;

– barometer, measuring air pressure under certain conditions; and, finally,

– GNSS-receiver forming information on position and velocity of the unmanned aerial vehicle (UAV).

Let us pay attention to the fact that the MEMS-based inertial sensors exhibit large errors that can be compensated using data on the position (latitude and longitude) and velocity of the UAV from the GNSS-receiver, as well as on the altitude, being updated by the barometer using hypsometric expression. However, navigation information about the UAV from inertial system is basic since it possesses a high refresh rate and property of independence on the external interference.

In the case of a separate integration architecture, the algorithm of the strapdown inertial navigation system restarts with new initial conditions for the UAV position and velocity from the GNSS-receiver and barometer, respectively. This approach requires minimal hardware and software costs, though the precision of the integrated navigation solution herewith is getting worse with the restart time increasing.

A loosely coupled integration architecture employs the integrated Kalman filter to evaluate the UAV coordinates, velocity and orientation, as well as systematic errors of accelerometers and angular rate sensors. Computed estimates of the inertial system errors are being subtracted from its indications for errors compensation. Thus, this approach is being characterized by high precision and reliability of navigation solution, but, at the same time, tuning integrated Kalman filter is not a trivial task.

Keywords:

simulation modelling, unmanned aerial vehicle, navigation system, separate integration architecture, loosely coupled integration architecture, Kalman filter

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