Multiple IMU Fusion Algorithm Comparison for Sounding Rocket Attitude Applications

M.A. Gómez, Laura Train García, Alberto Solera Rico, Ignacio Gómez-Pérez, and Eusebio Valero Sánchez

Peer Reviewed

Abstract: Micro-electro-mechanical (MEMS) technology is known to offer a wide variety of advantages for navigation and guidance purposes in terms of cost, size and weight. However, sensor accuracy limits its applications. The use of a Redundant Inertial Measurement Unit (RIMU) configuration allows combining inertial observations not only to obtain a better performance for attitude estimation, but also to be able to detect and isolate a sensor not properly working. The output signals of uncorrelated IMU sensors can be integrated using a data fusion algorithm (e.g., Extended Kalman Filter, EKF). The aim of this study is to present the implementation of several filters for an array of consumer grade IMUs placed on a "skew-redundant" configuration in a sounding rocket vehicle. The filtering method allows designing an attitude estimator capable of improving the kinematic predictions from gyroscope measurement integration by applying a correction method from accelerometer and magnetometer data. Moreover, simulations are performed to analyze the resilience and fault detection, while laboratory tests check the performance of the two proposed architectures. With the aim of testing accuracy of the algorithms for the RIMU a software simulation tool and laboratory tests are presented.
Published in: Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022)
September 19 - 23, 2022
Hyatt Regency Denver
Denver, Colorado
Pages: 1694 - 1705
Cite this article: Gómez, M.A., García, Laura Train, Rico, Alberto Solera, Gómez-Pérez, Ignacio, Sánchez, Eusebio Valero, "Multiple IMU Fusion Algorithm Comparison for Sounding Rocket Attitude Applications," Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022), Denver, Colorado, September 2022, pp. 1694-1705. https://doi.org/10.33012/2022.18476
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