gyroscopyГироскопия и навигацияGiroskopiya i Navigatsiya0869-70352075-0927AO «Концерн «ЦНИИ «Электроприбор»10.17285/0869-7035.2017.25.1.033-048gyroscopy-301Research ArticleСтатьиПрецизионное позиционирование пешехода в помещении на основе контроля за стадиями его походкиMotion Monitoring based on a Finite State Machine for Precise Indoor LocalizationКроненветтН.KronenwettN.

Кроненветт Николай

РуппельтЯ.RuppeltJ.

Руппельт Ян

ТроммерГ.Ф.TrommerG.F.

Троммер Герт Франц.

Действительный член международной общественной организации «Академия навигации и управления движением».

Институт оптимизации систем, Технологический институт КарлсруэГерманияInstitute of Systems Optimization (ITE), Karlsruhe Institute of Technology (KIT),GermanyИнститут оптимизации систем, Технологический институт Карлсруэ.РоссияInstitute of Systems Optimization (ITE), Karlsruhe Institute of Technology (KIT),Russian FederationИнститут оптимизации систем, Технологический институт Карлсруэ. Университет ИТМО (С.-Петербург).ГерманияInstitute of Systems Optimization, Karlsruhe Institute of Technology; ITMO University, St. Petersburg, RussiaGermany2017251120252513348Copyright © Кроненветт Н., Руппельт Я., Троммер Г., 20252025Кроненветт Н., Руппельт Я., Троммер Г.Kronenwett N., Ruppelt J., Trommer G.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.gyroscopy.ru/jour/article/view/301

В статье представлен метод точного определения при ходьбе момента опорной фазы для позиционирования пешехода с помощью инерциального измерительного модуля (ИИМ), размещаемого на ноге. Корректная классификация стадий при движении пешехода реализуется с использованием специального механизма оценки конечных состояний (МОКС) походки человека. МОКС обеспечивает высокоточное и робастное определение моментов коррекций по нулевой скорости (ZUPT), которые используются в навигационном фильтре. При этом применяется фильтр Калмана в версии стохастического клонирования с ограничениями, демонстрирующий эффективность использования ZUPT-коррекций в реальных условиях, включая движение вперед, назад, а также по лестнице. Даже для такого типа движения, как бег, и при применении сверхбюджетного ИИМ предложенный подход в среднем демонстрирует погрешность местоопределения на уровне не более 1,5% от пройденного расстояния.

This paper presents a precise stance detection method for accurate personal localization using a foot-mounted inertial measurement unit. The exact classification of the stance phases of the foot is realized with a finite state machine (FSM), which separates the human gait circle in different sub-states. The FSM-based approach provides high accurate and robust detections of Zero Velocity Updates (ZUPTs) which can be applied to the navigation filter. We use a constraint stochastic cloning (SC) Kalman filter to show the performance of the high precise ZUPT intervals with real world sensor data including forward, backward and staircase motion. Even for the movement type running and the signals of an ultra-low cost inertial measurement unit we achieve with our motion monitoring system a position estimation with an average error of less than 1.5% of the travelled distance.

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The authors declare that there are no conflicts of interest present.