An Integrated Deep Ensemble-Unscented Kalman Filter for Sideslip Angle Estimation With Sensor Filtering Network

• Published in: IEEE access Vol. 9; pp. 149681 - 149689
• Main Authors: Kim, Dongchan, Kim, Gihoon, Choi, Seungwon, Huh, Kunsoo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; deep ensemble; Estimation; Kalman filters; kinematic model; Kinematics; Neural networks; Recurrent neural networks; sensor filtering network; Sensors; Sideslip; Sideslip angle estimation; Uncertainty; unscented Kalman filter; Vehicle dynamics
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3125351

An integration scheme for sideslip angle estimation is proposed where a deep neural network and a simple kinematics-based model are combined in an unscented Kalman filter. The deep neural network contains two modules: a sensor filtering network designed to overcome the limitations of the kinematics-based model and a deep ensemble network to estimate the sideslip angle and its uncertainty. Both networks use recurrent neural networks with long short-term memory to analyze sequential sensor data. The networks were trained using only input signal sets that can be obtained from on- board sensor measurements. The filtering network reduces the noise and bias of the input signals to match the model used for the unscented Kalman filter. Next, the initial estimate and its uncertainty obtained from the deep ensemble network are utilized as a new measurement in the unscented Kalman filter, inducing an adaptive measurement variance. The algorithm was verified through both simulation and experiment, and the results demonstrate the effectiveness of the proposed algorithm.