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《Computer Simulation》 2015-12
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Study on Vehicle Driving State Estimation Based on Multiple Information Fusion

LI Ning;LI Gang;XIE Rui-chun;YUAN Hang;School of Automobile and Traffic Engineering,Liaoning University of Technology;  
Vehicle state information is the prerequisite for vehicle active safety control. The single centralized Kalman filter algorithm for vehicle driving state is poor in fault tolerance and stability theoretically. The federal cubature Kalman filter algorithm which combines the federal Kalman filter and the cubature Kalman filter is proposed to estimate the vehicle driving state. The nonlinear 3- DOF model and Dugoff tire model are adopted as the vehicle driving state estimation model. The Federal Kalman Filter and the cubature Kalman filter theory are combined to design the algorithm which estimates the vehicle driving state accurately through information fusion of the low cost sensor signals. The vehicle simulation results based on the Car Sim and Matlab / Simulink show that federal cubature Kalman filter theory can accurately and stably estimate the vehicle driving state.
【Fund】: 国家自然科学基金青年科学基金项目(51305190);; 辽宁省教育厅项目(L2013253)
【CateGory Index】: U463.6
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