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Использование графических языков программирования высокого уровня для реализации бесплатформенной инерциальной навигационной системы

Использование графических языков программирования высокого уровня для реализации бесплатформенной инерциальной навигационной системы

Abstract

В статье рассматривается бесплатформенная инерциальная навигационная система, которая позволяет рассчитывать углы наклона и крена мобильного робота или беспилотного летательного аппарата. Рассматриваются основные способы комплексирования и обработки информации на примере фильтра Калмана. Проводится расчёт сравнительной характеристики оценки состояния системы на примере математической модели и реального макетного образца. Предлагается способ комплексирования информации на основе интеллектуального фильтра.

The article views a strap down inertial navigation system for calculation tilting angle and the roll of the mobile robot and unmanned aerial vehicle . The basic ways of integration and information processing are viewed on the Kalman filter. The article also presents the calculation of comparative characteristic of the system state value on the mathematical model and a real prototype and suggests a method of information integration based on artificial intelligence method.

Keywords

БЕСПЛАТФОРМЕННАЯ ИНЕРЦИАЛЬНАЯ НАВИГАЦИОННАЯ СИСТЕМА,STRAP DOWN INERTIAL NAVIGATION SYSTEM,ФИЛЬТР КАЛМАНА,KALMAN FILTER,КОМПЛЕКСИРОВАНИЕ ИНФОРМАЦИИ,AGGREGATION OF INFORMATION,УСТОЙЧИВОСТЬ,STABILITY,ОЦЕНКА СОСТОЯНИЯ СИСТЕМЫ,SYSTEM STATE VALUE,МЕТОДЫ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА,ARTIFICIAL INTELLIGENCE METHODS

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average