基于粒子滤波算法的汽车状态估计技术

Vehicle States Estimation Technology Based on Particle Filter Algorithm

将粒子滤波(particle filter,PF)算法应用到汽车的状态估计之中,建立了包含定常统计特性噪声和非线性轮胎的汽车动力学模型,根据汽车非线性状态转移函数完成对粒子的预测,基于当前时刻的量测值实现对预测粒子权重的评估,最后通过重采样完成对汽车关键状态量估计。将PF估计器与常见的EKF、UKF估计器进行了比较分析,基于ADAMS/Car的虚拟试验和实车试验验证了PF在汽车状态估计中的可行性。

Particle filter（PF） algorithm was used in vehicle states estimation.A vehicle dynamics system containing constant noise and non-linear tire model was established.First,the particles were predicted through non-linear state transition function;then the weights of the predicted particles were evaluated based on current measurements.Finally,the key states were estimated though resample step.The PF estimator was compared with other estimators based on extended Kalman filter（EKF） and unscented Kalman filter（UKF）.The results of virtual experiment based on ADAMS/Car and real vehicle experiment demonstrated that PF was available in vehicle states estimation.