基于改进粒子滤波算法的车速估计

Vehicle speed estimation based on a modified particle filter algorithm

针对基于粒子滤波算法设计的车速估计器因提议分布与实际分布不一致导致粒子退化使估计误差变大的问题,提出了一种通过修正提议分布减弱粒子退化影响的改进粒子滤波车速估计器。首先,基于车辆运动学模型和传感器特性建立系统的状态转移方程和观测方程。然后,利用传感器测量值与粒子状态值的差值设计提议分布修正项对状态转移方程进行修正,并对过程噪声做自适应处理。最后,利用CarSim-Simulink联合仿真平台在双移线工况和正弦转角输入工况下进行仿真验证。与自适应粒子滤波器相比,双移线工况下改进粒子滤波估计器产生的纵向速度估计值和侧向速度估计值的平均绝对误差分别减小了40.25%和55.71%;正弦转角输入工况下,改进粒子滤波估计器产生的纵向速度估计值和侧向速度估计值的平均绝对误差分别减小了47.00%和41.21%。

For conventional vehicle speed estimators designed based on the particle-filter algorithm,the estimation performance deteriorates if the proposal distribution is inconsistent with the actual distribution.In this paper,an improved particle-filter speed estimator is proposed to tackle this problem by modifying the proposal distribution.Firstly,the state transition equation and the observation equation of the system are established based on vehicle kinematics and sensor characteristics.Then,the difference between sensor measurements and particle state values is employed to design a correction term for the proposal distribution,simultaneously adapting the process noise in the state transition equation.Finally,simulation validation is conducted using CarSim-Simulink co-simulation platform under the double-lane change and the sine-wave steer input maneuvers.Compared with the adaptive particle filter,the proposed estimator shows reductions of 40.25%and 55.71%in the mean absolute deviations(MAD)of the estimated longitudinal velocity and the estimated lateral velocity,respectively,under the double-lane change maneuver;and under the sine-wave steer input maneuver,the reductions are 47.00%and 41.21%,respectively.