A New Triple Filtering Algorithm and Its Application for Aerial GNSS/INS-Integrated Direct Georeferencing System

Autor: Yu Wu, Keyi Xu, Qusen Chen, Leilei Li, Xiangdong An
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Journal of Sensors, Vol 2021 (2021)
ISSN: 1687-725X
DOI: 10.1155/2021/6527356
Popis: A global navigation satellite system and inertial navigation system- (GNSS/INS-) integrated system is employed to provide direct georeferencing (DG) in aerial photogrammetry. However, GNSS/INS suffers from stochastic error, strong nonlinearity, and weak observability problems in high dynamic or less maneuver scenarios. In this paper, we proposed a new triple filtering algorithm for aerial GNSS/INS integration. The new algorithm implements filtering in the sequence of forward, backward, and forward directions. Each filter is initialized by a previous filter to get a quick convergence, and the final result is combination of the last two filtering to smooth error. The proposed triple filtering strategy avoids inaccuracy in the 1st forward filtering when the system has not reached convergence. Moreover, it facilitates engineering implementation because backward filtering can employ the same equations with forward filtering. To assess stochastic error of the inertial measurement unit, the Allan variance method is used and abbreviated stochastic model is built. A real aerial testing is conducted, and the result indicates that DG can achieve horizontal accuracy of 5 cm by the proposed algorithm, which has 63% improvement compared to standard extended Kalman filter.
Databáze: OpenAIRE
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