Improved Adaptive Kalman Filtering Algorithm for Vehicular Positioning

Given that the traditional Kalman filtering algorithm is difficult to catch the characteristics of dynamic noise and observation noise in the application of vehicular GPS dynamic positioning, an improved adaptive Kalman filter model which is applied for GPS dynamic positioning is proposed in this paper. The proposed filtering algorithm has the ability of correcting the system noise parameter in real time during the filtering process. Thus it prevents the filtering divergence happening which often appears in the classical Kalman filtering. Furthermore, it works out the trade-off between the system positioning state variable dimension and computing speed. Experimental results have demonstrated that the proposed adaptive Kalman filtering algorithm has the excellent performance of adaptation.