基于卡尔曼滤波算法的轨迹预测

Trajectory Prediction Based on Kalman Filtering Algorithm

卡尔曼滤波可以通过运动方程及概率统计实现对一般事物发展的预测,因为不需要追溯历史数据,只需根据上一时刻的状态来预测下一时刻的状态,所以在故障诊断、巡航制导等方面应用广泛。基于此,将卡尔曼滤波作为预测模型来实现车辆的行车轨迹预测。首先阐述了卡尔曼滤波的预测过程及公式,其次将小车的运动区分为直线运动和转向运动,并通过简化阿克曼转向理论构建小车的运动模型,最后在Simulink及车辆运动工具箱中搭建物理模型对小车的运动轨迹进行预测,以此证明卡尔曼滤波预测小车行车轨迹的可行性。

Kalman filtering can predict the development of general things through the equation of motion and probability statistics.Because it does not need to trace historical data,it only needs to predict the state of the next moment according to the state of the previous moment,so it can be used in fault diagnosis,cruise guidance,etc.Wide range of applications.Based on this,the Kalman filter is used as a prediction model to realize the vehicle trajectory prediction.Firstly,the prediction process and formula of Kalman filter are expounded.Secondly,the motion of the car is divided into linear motion and steering motion,and the motion model of the car is constructed by simplifying the Ackerman steering theory.Finally,the physical model is built in Simulink and the vehicle motion toolbox.The trajectory of the car is predicted to prove the feasibility of the Kalman filter to predict the trajectory of the car.