Enhance accuracy of kinematic GPS positioning with Kalman filtering

Abstract

Abstract: The article discusses the Kalman filter application for temporal random motion of the GPS receiver location. The motion of the GPS receiver is a space state model with time-varying. The spatial state model is usually represented by linear differential equations with white noise. When the state of space fluctuates over time, it is represented by Riccati equations, ie nonlinear differential equations.Kalman filter for optimal estimation reliable, even unstable system, respectively random moves of the GPS receiver. The mobile GPS coordinates over time are compared to the coordinates in a previous static test, confirming that the Kalman filter can apply an optimal estimate of the mobile GPS position. This reduces the investment cost and increases the efficiency of using a common GPS receiver.