Robust Kalman Filter with Recursive Measurement Noise Covariance Estimation Against Measurement Faults
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Abstract
A new innovation-based recursive measurement noise covariance estimation method is proposed. The presented algorithm is used for Kalman filter tuning, as a result, the robust Kalman filter (RKF) against measurement malfunctions is derived. The proposed innovation-based RKF with recursive estimation of measurement noise covariance is applied for the model of Unmanned Aerial Vehicle (UAV) dynamics. Algorithms are examined for two types of measurement fault scenarios; constant bias at measurements (additive sensor faults) and measurement noise increments (multiplicative sensor faults). The simulation results show that the proposed RKF can accurately estimate UAV dynamics in real time in the presence of various types of sensor faults. Estimation accuracies of the proposed RKF and conventional KF are investigated and compared. In all investigated sensor fault sceneries, the Root Mean Square (RMS) errors of the proposed RKF estimates are lower. The conventional KF gives inaccurate estimation results in the presence of sensor faults.
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Kalman filter, sensor bias, measurement noise increment, robust estimation, covariance estimation, unmanned aerial vehicle
Ding, W., Wang, J., Rizos, C., and Kinlyside, D. (2007). Improving adaptive Kalman estimation in GPS/INS integration. The Journal of Navigation, vol. 60, no.3, pp. 517–529.
Geng, Y., and Wang, J. (2008). Adaptive estimation of multiple fading factors in Kalman filter for navigation applications. GPS Solutions, vol. 12, no.4, pp. 273–279.
Hajiyev, C. (2007). Adaptive filtration algorithm with the filter-gain correction applied to integrated INS/Radar altimeter. Proc. Inst. Mech. Eng., Part G, vol. 221, no.5, pp. 847–885.
Hajiyev, C., and Soken, H.E. (2012). Robust estimation of UAV dynamics in the presence of measurement faults. Journal of Aerospace Engıneerıng, vol.25, pp. 80-89.
Hajiyev, C., and Soken, H E. (2013). Robust Adaptive Kalman Filter for estimation of UAV dynamics in the presence of sensor/actuator faults. Aerospace Science and Technology, vol. 28, pp. 376–383.
Hajiyev, C., & Soken, H.E. (2021). Fault Tolerant Attitude Estimation for Small Satellites. Boca Raton, FL, USA and Abingdon, Oxon, England: Taylor & Francis Group, LLC, CRC Press,
Jwo, D.J., and Weng, T.P. (2008). An adaptive sensor fusion method with applications in integrated navigation. The Journal of Navigation, vol. 61, no.4, pp. 705–721.
Kalman, R.E. (1960). A new approach to linear filtering and prediction problems. ASME Journal of Basic Engineering, vol. 82, pp. 35-45.
Matthews, J.S. (2006). Adaptive Control of Micro Air Vehicles, M.Sc. Thesis, Department of Electrical and Computer Engineering, Brigham Young University, Utah, USA.
Maybeck, P.S. (1999). Multiple model adaptive algorithms for detecting and compensating sensor and actuator/surface failures in aircraft flight control systems. International Journal of Robust and Nonlinear Control, vol.9, no.14, pp. 1051-1070.
Mehra, R.K. (1970). On the identification of variance and adaptive Kalman filtering. IEEE Transactions on Automatic Control, vol.15, no.2, pp. 175-184.
Mohamed, A.H., and Schwarz, K.P. (1999). Adaptive Kalman filter for INS/GPS. Journal of Geodesy, vol.73, no.4, pp.193-203.
Sabzevari, D., and Chatraei, A. (2021). INS/GPS sensor fusion based on adaptive fuzzy EKF with sensitivity to disturbances. IET Radar, Sonar& Navigation, vol. 15, pp. 1535-1549.
Salychez, O.S. (1994). Special studies in dynamic estimation procedures with case studies in inertial surveying. ENGO 699.26 lecture notes, Department of Geomatics Engineering, University of Calgary, Canada.
Soken, H.E., Hajiyev, C., and Sakai, S. (2014). Robust Kalman filtering for small satellite attitude estimation in the presence of measurement faults. European Journal of Control, vol. 20, pp. 64-72.
Yechout, T. R., Morris, S.L., Bossert, D.E., and Hallgren, W.F. (2003). Introduction to Aircraft Flight Mechanics: Performance, Static Stability, Dynamic Stability, and Classical Feedback Control, AIAA Education Series, Virginia, USA.
Wang, L., Cheng, J., Qi, B., Cheng, S., Chen, S. (2023). An adaptive Kalman filtering algorithm based on maximum likelihood estimation. Measurement Science and Technology, vol. 34, paper 115114. https://doi.org/10.1088/1361-6501/ace9ef
White, N.A., Maybeck, P.S. and DeVilbiss, S.L. (1998). MMAE detection of interference/jamming and spoofing in a DGPS-aided inertial system. IEEE Transactions on Aerospace and Electronic Systems, vol. 34, no.4, pp. 1208-1217.
Zhang, Q., Zhao, Lu., and Zhao, Lo. (2021). A Two-step robust adaptive filtering algorithm for GNSS kinematic precise point positioning. Chinese Journal of Aeronautics, vol. 34, Iss. 10, pp. 210-219.
DOI:10.1016/j.cja.2020.10.033