16 June 1997 Gyroscopic data fusion via a quaternion-based complementary filter
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Abstract
We present an application of a complementary filter system to the attitude determination of a remotely operated underwater vehicle (ROV). The main contribution of this paper is to combine existing complementary filter theory with quaternion attitude representation. The combination allows accurate attitude determination by a real-time system using low cost sensors. We fuse the estimate from an extended Kalman filter (EKF) with the output from a set of vibrating structure rate gyroscopes. The EKF supplies high-quality low frequency information, the gyroscopes supply corresponding high frequency information. The attitude is described via a quaternion representation. We discuss how the use of quaternions is beneficial for estimator design due to the low computational burden, and lack of discontinuities and singularities. The EKF combines the output from two inclinometers and a magnetometer with a vehicle process model The EKF assumes that sensor and process noise is broadband and that the process model captures all the important dynamics. An underwater vehicle is capable of rapid rotations, which are difficult to model, and would require computationally unattainable update rates to track effectively. We develop a filter, which uses the difference between the EKF and gyroscopic attitude estimates (an indirect filter) to correct for drift in the gyroscopic attitude estimate. We develop first a feedforward and then a feedback filter. The simplicity of the indirect filter permits very fast update rates, so the system may follow rapid vehicle rotations. We discuss the real-time implementation of the estimator on a transputer based system mounted within a small ROV. We present experimental results showing the system performance of the combined filter system.
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Robert Smith, Robert Smith, Andy Frost, Andy Frost, Penelope J. Probert, Penelope J. Probert, } "Gyroscopic data fusion via a quaternion-based complementary filter", Proc. SPIE 3067, Sensor Fusion: Architectures, Algorithms, and Applications, (16 June 1997); doi: 10.1117/12.276131; https://doi.org/10.1117/12.276131
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