The Adaptive Filtering Algorithms' Research On Dynamic Relative Position

The dynamic relative positioning for moving vehicles in GPS (Global Positioning System) is an important direction in positioning applications. This paper mainly discusses GPS in the dynamic conditions, and the integrate navigation system of GPS and SINS (Strapdown Inertial Navigation System), and the integrate navigation system of BeiDou and SINS.First, the research on the dynamic positioning of GPS. A model was built based on the features of the land vehicle movement and the error-correction about GPS, at the same time, Kalman filter was constructed according to the model. After analyzing Sage-Husa Adaptive algorithm and Modified Strong Tracking Kalman Filter algorithm, the author proposed an adaptive Kalman filtering algorithm, which was proved more appropriate to the dynamic positioning by experiments in the really dynamic environment.Second, the research on the SINS and GPS integrate navigation system. This paper analyzed the model error brought by the IMU (Inertial Measurement Unit), and constructed Kalman filter. Because the precision of Kalman filter is not accurate, the author introduced BP (Back-Propagation) neural Network to aid Kalman filtering after improving the velocity and accuracy of training BP network. Experiments showed that Kalman filter aided by BP Network is better than other filter algorithm.The third, the research on the SINS and BeiDou integrate navigation system. After using the method of BP aids Kalman adaptive algorithm, some useful conclusions were got.