4.7 Article

An Adaptive Complementary Kalman Filter Using Fuzzy Logic for a Hybrid Head Tracker System

Journal

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Volume 65, Issue 9, Pages 2163-2173

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2016.2575178

Keywords

Adaptive complementary Kalman filter (CKF); fuzzy logic; gyro/vision integrated attitude estimation; hybrid head tracker system

Funding

  1. Samsung Advanced Institute of Technology, Suwon, South Korea

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In this paper, a sensor fusion algorithm that integrates gyroscope and vision measurements using an adaptive complementary Kalman filter is proposed to estimate the attitude of a hybrid head tracker system. In order to make the filter more tolerant to vision measurement fault and more robust to system dynamics, an adaptive fading filter is implemented to the sensor fusion filter, and fuzzy logic is applied to adjust the fading factor, which adapts a Kalman gain of the sensor fusion filter. For recognizing the dynamic condition of the system and vision measurement fault, the normalized square error of attitude and the norm of gyroscope output with designed membership functions are used. The performance of the proposed algorithm is evaluated by simulations. It is confirmed that the proposed algorithm has better performance than the conventional algorithms in high-dynamic conditions and vision measurement fault case.

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