Vehicle navigation algorithm based on unscented Kalman filter sensor information fusion

Abstract

A new autonomous vehicle navigation model was proposed based on multi-sensor system for vehicle navigation and Global Positioning System(GPS) under complex road conditions. And the Unscented Kalman Filter(UKF) was used to overcome some security issues due to the sudden error produced by the Kalman filters with extensions,which belonged to Sigma point based sensor fusion algorithm. It is more suitable than the Kalman filters with extensions that the UKF can calculate the evaluation satisfied the requirement in vehicle navigation. Comparison experiments with the Kalman filter based on polynomial expansion were given in terms of estimation accuracy and computational speed. The experimental results show that the Sigma-point Kalman filter is a reliable and computationally efficient approach to state estimation-based control.Moreover,it is faster to evaluate the motion state of the vehicle according to the current direction situations and the feedback information of vehicle sensor,and can calculate the control input of vehicle adaptively in real time.