Усовершенствованный алгоритм многоальтернативной фильтрации на основе байесовских сетей и его применение в интегрированной навигационной системе

Неопределенность параметров и нестабильность модели интегрированной навигационной системы типичны для неупорядоченной среды. В таких системах большие погрешности оценивания чаще всего возникают тогда, когда для решения навигационных задач используется одна модель. Для устранения этой проблемы предложен алгоритм взаимосвязанной многоальтернативной фильтрации (interacting multimodel – IMM), усовершенствованный за счет использования байесовских сетей (BNIMM). Алгоритм предусматривает введение параметров движения, определенных с помощью многоальтернативной оценки, и формирование байесовских сетей на основе причинно-следственной связи между переменными и моделью системы. Характеристики байесовской сети используются для модификации вероятностей переключения моделей при многоальтернативном оценивании, что может снизить зависимость распознавания действительной модели от априорной информации, задействуемой в алгоритме IMM.
Предлагаемый подход позволяет решать такие проблемы, как запаздывание смены модели и вероятное изменение модели в алгоритме IMM, и повышать адаптивность алгоритма IMM. Метод BNIMM использовался в качестве локального субфильтра в федеративном фильтре, что дало возможность сформировать архитектуру объединения информации, полученной от интегрированной навигационной системы в составе бесплатформенной инерциальной навигационной системы (БИНС), глобальной навигационной спутниковой системы (GPS) и одометра. В ходе испытаний выходные данные гироскопа и акселерометра были взяты в качестве характеристических переменных для построения байесовской сети, которая применялась для прогнозирования в динамическом режиме неопределенности в интегрированной навигационной системе. Полевые тесты на дорогах показали, что предложенный федеративный алгоритм BNIMM может значительно повысить стабильность и точность оценки состояния интегрированной навигационной системы.

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