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Электронный каталог: Arefyev, N.N. - Filter Kalman for solving the problem of coordinates UAV
Arefyev, N.N. - Filter Kalman for solving the problem of coordinates UAV
Статья
Автор: Arefyev, N.N.
Системный анализ и прикладная информатика: Filter Kalman for solving the problem of coordinates UAV
Фильтр Калмана для оптимального получения координат беспилотных летательных аппаратов
б.г.
ISBN отсутствует
Автор: Arefyev, N.N.
Системный анализ и прикладная информатика: Filter Kalman for solving the problem of coordinates UAV
Фильтр Калмана для оптимального получения координат беспилотных летательных аппаратов
б.г.
ISBN отсутствует
На полку
Статья
Arefyev, N.N.
Filter Kalman for solving the problem of coordinates UAV = Фильтр Калмана для оптимального получения координат беспилотных летательных аппаратов / N. N. Arefyev. – DOI 10.21122/2309-4923-2019-1-26-34 // Системный анализ и прикладная информатика / гл. ред. Сергей Васильевич Харитончик; учредитель Белорусский национальный технический университет (Минск). – 2019. – №1. – P. 26-34. – Режим доступа : http://rep.bntu.by/handle/data/54664. – На англ. яз.
Unmanned aerial vehicles (UAVs) are increasingly used in military and scientific research. Some miniaturized UAVs rely entirely on the global positioning system (GPS) for navigation. GPS is vulnerable to accidental or deliberate interference that can cause it to fail. It is not unusual, even in a benign environment, for a GPS outage to occur for periods of seconds to minutes. For UAVs relying solely on GPS for navigation such an event can be catastrophic. This article proposes an extended Kalman filter approach to estimate the location of a UAV when its GPS connection is lost, using inter-UAV distance measurements Increasing the accuracy of coordinate’s determination is one of the most crucial tasks of the modern UAV navigation. This task can be solved by using different variants of integration of navigation systems. One of the modern variants of integration is the combination of GPS/GLONASS-navigation with the extended Kalman filter, which estimates the accuracy recursively with the help of incomplete and noisy measurements. Currently different variations of extended Kalman filter exist and are under development, which include various number of variable states [1]. This article will show the utilization efficiency of extended Kalman filter in modern developments.
623.7
общий = БД Техника
общий = БЕСПИЛОТНЫЕ ЛЕТАТЕЛЬНЫЕ АППАРАТЫ
общий = МАТЕМАТИЧЕСКОЕ МОДЕЛИРОВАНИЕ
общий = НАВИГАЦИЯ
общий = GPS
Arefyev, N.N.
Filter Kalman for solving the problem of coordinates UAV = Фильтр Калмана для оптимального получения координат беспилотных летательных аппаратов / N. N. Arefyev. – DOI 10.21122/2309-4923-2019-1-26-34 // Системный анализ и прикладная информатика / гл. ред. Сергей Васильевич Харитончик; учредитель Белорусский национальный технический университет (Минск). – 2019. – №1. – P. 26-34. – Режим доступа : http://rep.bntu.by/handle/data/54664. – На англ. яз.
Unmanned aerial vehicles (UAVs) are increasingly used in military and scientific research. Some miniaturized UAVs rely entirely on the global positioning system (GPS) for navigation. GPS is vulnerable to accidental or deliberate interference that can cause it to fail. It is not unusual, even in a benign environment, for a GPS outage to occur for periods of seconds to minutes. For UAVs relying solely on GPS for navigation such an event can be catastrophic. This article proposes an extended Kalman filter approach to estimate the location of a UAV when its GPS connection is lost, using inter-UAV distance measurements Increasing the accuracy of coordinate’s determination is one of the most crucial tasks of the modern UAV navigation. This task can be solved by using different variants of integration of navigation systems. One of the modern variants of integration is the combination of GPS/GLONASS-navigation with the extended Kalman filter, which estimates the accuracy recursively with the help of incomplete and noisy measurements. Currently different variations of extended Kalman filter exist and are under development, which include various number of variable states [1]. This article will show the utilization efficiency of extended Kalman filter in modern developments.
623.7
общий = БД Техника
общий = БЕСПИЛОТНЫЕ ЛЕТАТЕЛЬНЫЕ АППАРАТЫ
общий = МАТЕМАТИЧЕСКОЕ МОДЕЛИРОВАНИЕ
общий = НАВИГАЦИЯ
общий = GPS
