基于无迹卡尔曼滤波传感器信息融合的车辆导航算法

针对复杂道路条件下车辆的导航问题，将全球定位系统(GPS)与车载终端传感器系统相结合，提出了基于多传感器系统的车辆精确定位模型，并针对扩展类卡尔曼滤波易产生突发性误差而导致的安全问题，采用基于Sigma点的无迹卡尔曼滤波器(UKF)传感器信息融合算法。根据实时的道路状况和车辆自身的运动状态给出符合要求的状态估值，实验与基于多项式扩展卡尔曼滤波车辆传感器信息融合算法在精度和效率方面进行了比较，结果表明，基于UKF传感器信息融合的算法在复杂路况下的估计精度和运行效率都有显著提高，能够根据当前的路线情况和车载传感器的反馈信息快速地估计出车辆的运动状态，实时计算出动态的车辆控制输入。

Vehicle navigation algorithm based on unscented Kalman filter sensor information fusion

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.