Adaptive EKF-Based Estimator of Sideslip Angle Using Fusion of Inertial Sensors and GPS
SAE paper #2011-01-0953, SAE International Journal of Passenger Cars - Mechanical Systems, Vol. 4, No. 1, pp. 700-7122011This paper presents an adaptive extended Kalman filter (EKF)-based sideslip angle estimator, which utilizes a sensor fusion concept that combines the high-rate inertial sensors measurements with the low-rate GPS velocity measurements. The sideslip angle estimation is based on a vehicle kinematic model relying on the lateral accelerometer and yaw rate gyro measurements. The vehicle velocity measurements from low-cost, single antenna GPS receiver are used for compensation of potentially large drift-like estimation errors caused by inertial sensors offsets. Adaptation of EKF state covariance matrix ensures a fast convergence of inertial sensors offsets estimates, and consequently a more accurate sideslip angle estimate. By using a detailed simulation analysis, it is found out that the main sources of estimation errors include inaccuracies of pre-estimated vehicle longitudinal velocity obtained from nondriven wheel speed sensors, the GPS velocity signal latency, and the road bank-related disturbances. Several compensation methods are proposed to suppress the influence of these errors. sideslip; estimation; GPS; inertial; sensor fusion; Kalman filter
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SAE paper #2011-01-0953, SAE International Journal of Passenger Cars - Mechanical Systems, Vol. 4, No. 1, pp. 700-712
2011
Cited by 13
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[1] Force sensors for active safety, stability enhancement and lightweight construction of road vehicles🔗Vehicle System Dynamics, 2023
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[2] Autonomous Vehicle Kinematics and Dynamics Synthesis for Sideslip Angle Estimation Based on Consensus Kalman Filter🔗IEEE Transactions on Control Systems Technology, 2023
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[3] Advancing Estimation Accuracy of Sideslip Angle by Fusing Vehicle Kinematics and Dynamics Information With Fuzzy Logic🔗IEEE Transactions on Vehicular Technology, 2021
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[6] Tire lateral force estimation and grip potential identification using Neural Networks, Extended Kalman Filter, and Recursive Least Squares🔗Neural computing & applications (Print), 2018
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[7] On tyre force virtual sensing for future Automated Vehicle-Based Objective Tyre Testing (AVBOTT)🔗Vehicle System Dynamics, 2018
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[8] Robust Virtual Sensing for Vehicle Agile Manoeuvring: A Tyre-Model-Less Approach🔗IEEE Transactions on Vehicular Technology, 2018
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[9] Vehicle Dynamics Virtual Sensing Using Unscented Kalman Filter: Simulations and Experiments in a Driver-in-the-Loop Setup🔗International Conference on Informatics in Control, Automation and Robotics, 2017
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[10] A Virtual Sensor for Integral Tire Force Estimation using Tire Model-less Approaches and Adaptive Unscented Kalman Filter🔗International Conference on Informatics in Control, Automation and Robotics, 2017
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[12] Study on GPS/INS Loose and Tight Coupling🔗International Conference on Intelligent Human-Machine Systems and Cybernetics, 2015