gyroscopyГироскопия и навигацияGiroskopiya i Navigatsiya / Gyroscopy and Navigation0869-70352075-0927AO «Концерн «ЦНИИ «Электроприбор»LULOGXgyroscopy-17Research ArticleСтатьиЭкспериментальное исследование адаптивной калмановской фильтрации при использовании U-D-метода квадратного корня и прогнозирование погрешностей БИНС при пропадании сигнала ГНССAn Experimental Study on Adaptive Sequential U-D Filtering and Propagation of SINS Errors during GNSS Outagehttps://orcid.org/0009-0007-7458-8571МураликришнаГ.MuralikrishnaG.

Мураликришна Гуррам. Научный сотрудник, отдел навигационных систем

Хайдарабад

Hyderabad

https://orcid.org/0009-0006-0004-4689МаллешамГ.MalleshamG.

Маллешам Г. Профессор, факультет электротехники

Хайдарабад

Hyderabad

https://orcid.org/0009-0003-1178-0604КаннанМ.KannanM.

Каннан М. Старший научный сотрудник, отдел навигационных систем

Хайдарабад

Hyderabad

Исследовательский центр «Имарат»ИндияResearch Centre Imarat, DRDOIndiaКолледж приборостроения, Османский университетИндияOsmania UniversityIndia2024160520253242873Copyright © Мураликришна Г., Маллешам Г., Каннан М., 20252025Мураликришна Г., Маллешам Г., Каннан М.Muralikrishna G., Mallesham G., Kannan M.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/17

Бесплатформенные инерциальные навигационные системы (БИНС) служат главным источником навигационной информации на борту летательного аппарата (ЛА) и должны обеспечивать решение задач навигации с высокой точностью. Обычно выработанные инерциальными методами данные интегрируются с данными глобальных навигационных спутниковых систем (ГНСС) с применением оптимальной фильтрации, что в конечном итоге и позволяет получить точную навигационную информацию. Некоторые предложенные ранее варианты адаптивного фильтра Калмана (АФК) предполагают моделирование или оценивание матриц ковариации погрешностей измерений Rk и ковариации Qk шума процесса, описывающего динамическую систему. Возможность одновременного оценивания матриц Rk и Qk ограничена, поскольку большая продолжительность полета оказывает влияние на точность реализации навигационных приложений. На практике матрица Rk во всех случаях подвержена воздействию внешних факторов. В настоящей работе производится адаптивное оценивание Rk с одновременным точным вычислением Qk . Далее предпринимается попытка спрогнозировать ковариацию погрешностей БИНС в случае пропадания сигнала ГНСС при точном моделировании матрицы динамики системы и вычислении соответствующей матрицы Qk . Проблема плохой обусловленности ковариационной матрицы Pk погрешностей БИНС решается применением U-D-модификации метода квадратного корня при реализации соотношений дискретного фильтра Калмана. Точность прогнозирования Pk оценивается на основании скорости дрейфа инерциального решения; результаты зависят от качества оценивания смещений нулей инерциальных датчиков. Эффективность применения оценок смещений нулей датчиков, а также адаптивного оценивания Rk и расчета Qk продемонстрирована во время летных испытаний. В заключение приводится обзор различных АФК и методик прогноза и делаются выводы о возможностях их практического использования.

Strapdown inertial navigation system (SINS) is used as a primary navigation information source on-board an aircraft and is expected to provide high accuracy navigation solution. Often, the pure-inertial navigation solution is blended with global navigation satellite system (GNSS) data through optimal filtering to provide bounded and accurate navigation information. Several adaptive Kalman filtering (AKF) algorithms published earlier have considered either the modelling or estimation of measurement error covariance matrix Rk and process covariance matrix Qk. The simultaneous estimation of both Rk and Qk is limited in their performance due to instability for long endurance high accuracy navigation applications. The measurement noise covariance matrix Rk under all practical conditions is influenced by external factors. In this manuscript, the adaptive estimation of Rk has been explored along with the accurate computation of Qk. Further, an attempt has been made to propagate the error state covariance during GNSS outage with an accurate modeling of system matrix and corresponding Qk matrix computations. The sequential U-D filtering approach is explored to handle the ill-conditioning of Pk. The effect of propagation of Pk is judged through the quantification of pure-inertial navigation drift rate under GNSS outage conditions which is further decided by the quality of estimation of sensor biases. The effectiveness of these estimated sensor biases along with adaptive estimation of Rk and computation of Qk, is demonstrated through aircraft flight testing. Finally, various AKF algorithms are validated along with the propagation studies and conclusions are drawn for practical use.

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