Title :
An improved method of adaptive Kalman filtering for vehicular kinematic positioning
Author :
Wang Meng ; Hu Huaen ; Wang Xiaofeng ; Zhang He ; Zhang Aijun
Author_Institution :
Sch. of Mech. Eng., Nanjing Univ. of Sci. & Technol., Nanjing, China
Abstract :
Due to the fact that the traditional Kalman filtering has difficulties in determining dynamic noise and observation noise in the application of vehicular GPS dynamic positioning, an improved adaptive Kalman filter algorithm is put forward for GPS dynamic positioning in this paper. The proposed algorithm has the ability of making a real-time correction on the parameters of system noise, avoiding the filter divergence during the traditional Kalman filtering. In addition, it overcomes the problems caused by the variable dimensions of the system positioning state. Experiment results have demonstrated the outstanding adaptive ability of the improved Kalman filter.
Keywords :
Global Positioning System; Kalman filters; adaptive filters; adaptive Kalman filter algorithm; dynamic noise; filter divergence; observation noise; real-time correction; traditional Kalman filtering; vehicular GPS dynamic positioning; vehicular kinematic positioning; Global Positioning System; Heuristic algorithms; Kalman filters; Mathematical model; Noise; Vehicle dynamics; Adaptive; GPS; Kalman Filtering; Vehicular;
Conference_Titel :
Control Conference (CCC), 2014 33rd Chinese
Conference_Location :
Nanjing
DOI :
10.1109/ChiCC.2014.6896726