Transactions of Nanjing University of Aeronautics & Astronautics
Vehicle State and Parameter Estimation Based on Dual Unscented Particle Filter Algorithm
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Abstract:
Acquisition of real-time and accurate vehicle state and parameter information is critical to the research of vehicle dynamic control system. By studying the defects of the former Kalman filter based estimation method, a new estimating method is proposed. First the nonlinear vehicle dynamics system, containing inaccurate model parameters and constant noise, is established. Then a dual unscented particle filter (DUPF) algorithm is proposed. In the algorithm two unscented particle filters run in parallel, states estimation and parameters estimation update each other. The results of simulation and vehicle ground testing indicate that the DUPF algorithm has higher state estimation accuracy than unscented Kalman filter (UKF) and dual extended Kalman filter (DEKF), and it also has good capability to revise model parameters.
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Project Supported:
the National Natural Science Foundation of China(10902049); the Chinese Postdoctoral Science Foundation (2012M521073); the Fundamental Research Funds for the Central Universities; the Jiangsu Planned Projects for Postdoctoral Research Funds (1302020C); the Nanjing University of Aeronautics and Astronautics Student Innovative Training Program (20120119101535); the Fundation of Graduate Innovation Center in Nanjing University of Aeronautics and Astronautics (kfjj201404)
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