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Novel Adaptive Fuzzy Extended Kalman Filter for Attitude Estimation in Gps-Denied Environment

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Abstract—

This paper presents a Novel Adaptive Fuzzy Extended Kalman Filter namely (NAFEKF) which has been developed and applied for attitude estimation using only the outputs of strap-down IMU (Gyroscopes and Accelerometers) and strap-down magnetometer. The NAFEKF, which is based on EKF (Extended Kalman Filter) aided by FIS (Fuzzy Inference System), is validated in Matlab environment on simulated trip data and real data acquired during an UAV’s trip. Accuracy of estimated attitude is increased using NAFEKF compared to typical EKF and in addition the measurement noise covariance matrix is tuned, the proposed filter uses multiplicative error for quaternion. Simulation results show that estimated measurement noise covariance matrix is closed to its true value in cruise phase of flight (stationary phase), while in nonstationary phase it refers to the validity of accelerometer measurement model in the filter in NAFEKF; it neglects measurements from accelerometers in this case.

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Notes

  1. \(\sec = \frac{1}{{\cos }}.\)

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Authors and Affiliations

  1. Department of Electronic & Mechanical Systems, Higher Institute for Applied Sciences and Technology (HIAST), 31983, Damascus, Syria

    Ammar Assad, Wassim Khalaf & Ibrahim Chouaib

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  1. Ammar Assad
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  2. Wassim Khalaf
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  3. Ibrahim Chouaib
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Correspondence to Ammar Assad, Wassim Khalaf or Ibrahim Chouaib.

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Ammar Assad, Khalaf, W. & Chouaib, I. Novel Adaptive Fuzzy Extended Kalman Filter for Attitude Estimation in Gps-Denied Environment. Gyroscopy Navig. 10, 131–146 (2019). https://doi.org/10.1134/S2075108719030027

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