gyroscopyГироскопия и навигацияGiroskopiya i Navigatsiya / Gyroscopy and Navigation0869-70352075-0927AO «Концерн «ЦНИИ «Электроприбор»10.17285/0869-7035.2017.25.4.035-059gyroscopy-328Research ArticleСтатьиМодели стохастических погрешностей инерциальных датчиков смартфонов, учитывающие динамику движенияStochastic Error Modeling of Smartphone Inertial Sensors for Navigation in Varying Dynamic ConditionsРадиА.RadiA.

Ради Ахмед. Аспирант

НассарС.NassarS.

Нассар Самех. Научный сотрудник 

Эль-ШеймиН.El-SheimyN.

Эль-Шейми Насер. Доктор наук, профессор

Университет Калгари, факультет инженерной геоматикиКанадаDepartment of Geomatics Engineering, University of CalgaryCanada2017261120252543559Copyright © Ради А., Нассар С., Эль-Шейми Н., 20252025Ради А., Нассар С., Эль-Шейми Н.Radi A., Nassar S., El-Sheimy N.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/328

Проведено исследование стохастических погрешностей инерциальных датчиков на основе технологии микроэлектромеханических систем (МЭМС) в статических и динамических условиях. В качестве образцов использовались датчики инерциальных измерительных модулей (ИИМ) двух моделей смартфонов. Оценка характеристик стохастических погрешностей производилась двумя методами: методом вариации Аллана (ВА) и обобщенным методом моментов вейвлетов (ОММВ). Параметры модели, определенные в динамических условиях в лаборатории, сопоставляются с параметрами, полученными на неподвижном основании. В используемых для идентификации моделей реализациях исключены аномальные данные на основе доверительного интервала (ДИ). Предложен новый алгоритм инерциально-спутниковой навигационной системы ИНС/GPS, адаптивный к динамике движения. Представлен анализ эффективности использования различных стохастических моделей на основе ВА и ОММВ с применением смоделированных данных инерциально-спутниковой навигационной системы при пропадании сигналов GPS. Анализ выполнен с помощью предложенного алгоритма. Точность определения местоположения, полученная с помощью адаптивного алгоритма комплексирования ИНС/GPS, превосходит точность, достигнутую при использовании стандартного алгоритма комплексирования ИНС/GPS на основе статической модели погрешностей. Кроме того, модели стохастической погрешности, полученные на основе ОММВ, показывают лучшие результаты, чем модели на основе ВА.

This paper aims at investigating and analyzing the behavior of Micro Electromechanical Systems (MEMS) inertial sensors stochastic errors in both static and varying dynamic conditions using two MEMS-based Inertial Measurement Units (IMUs) of two different smartphones. The corresponding stochastic error processes were estimated using two different methods, the Allan Variance (AV) and the Generalized Method of Wavelets Moments (GMWM). The developed model parameters related to laboratory dynamic environment are compared to those obtained under static conditions. A contamination test was applied to all data sets to distinguish between clean and corrupted ones using a Confidence Interval (CI) investigation approach. A detailed analysis is presented to define the link between the error model parameters and the augmented dynamics of the tested smartphone platform. The paper proposes a new dynamically dependent integrated navigation algorithm which is capable of switching between different stochastic error parameters values according to the platform dynamics to eliminate dynamics-dependent effects. Finally, the performance of different stochastic models based on AV and GMWM were analyzed using simulated Inertial Navigation System (INS)/ Global Positioning System (GPS) data with induced GPS signal outages through the new proposed dynamically dependent algorithm.

The results showed that the obtained position accuracy is improved when using dynamic-dependent stochastic error models, through the adaptive integrated algorithm, instead of the commonly used static one, through the non-adaptive integrated one. The results also show that the stochastic error models from GMWM-based model structure offer better performance than those estimated from the AV-based model.

Стохастическая погрешностькалибровка инерциальных датчиковинерциальный измерительный модульвариация Алланаобобщенный метод моментов вейвлетовкомплексирование ИНС/GPS.Stochastic error modelinginertial sensors calibrationIMUsAllan variance (AV)generalized method of wavelet moments (GMWM)dynamically dependent adaptive INS/GPS algorithm.ReferencesJensen, A.B., GNSS satellite orbits, Technical University of Denmark, 2010.Jensen, A.B., GNSS satellite orbits, Technical University of Denmark, 2010.El-Sheimy, N., Lecture note 623 – Inertial techniques and INS/DGPS integration, Department of Geomatics Engineering, University of Calgary, 2014.El-Sheimy, N., Lecture note 623 – Inertial techniques and INS/DGPS integration, Department of Geomatics Engineering, University of Calgary, 2014.Nassar, S., Syed, Z., Niu, X., and El-Sheimy, N., Improving MEMS IMU/GPS systems for accurate land-based navigation applications, ION NTM, 2006, pp. 523–529.Nassar, S., Syed, Z., Niu, X., and El-Sheimy, N., Improving MEMS IMU/GPS systems for accurate land-based navigation applications, ION NTM, 2006, pp. 523–529.Woodman, O.J., An introduction to inertial navigation, University of Cambridge, Computer Laboratory, Tech. 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The authors declare that there are no conflicts of interest present.