BDS dynamics based on Kalman filtering research on pseudorange differential positioning algorithm

Aiming at the problem of insufficient positioning accuracy and stability of BDS least squares pseudorange difference method, a new pseudorange difference algorithm based on Kalman filtering method is proposed. Experiments under static and human walk dynamic conditions are carried out. After analyzing the measured data, the results from Kalman filtering method improves the accuracy in the east, north and up directions by 55％, 23％ and 48％ respectively under static conditions. Under dynamic conditions, the accuracy in the east, north, and up directions increased by 71％, 49％, and 33％, respectively.