复杂交通流下基于卡尔曼滤波的多目标全生命周期状态估计

针对复杂行车环境下噪声干扰和车辆行车过程中状态变化导致交通场景中目标状态估计精度低的问题,以毫米波雷达 为检测传感器,提出涵盖参数初始化和在线更新的基于卡尔曼滤波的多目标全生命周期状态估计方法。 首先,建立交通流下多 目标运动状态的卡尔曼滤波状态估计模型;基于此,一方面提出基于数据驱动的卡尔曼滤波观测噪声协方差矩阵初始化的新方 法,另一方面采用变分贝叶斯方法对卡尔曼滤波参数进行在线更新,以此提高多目标状态估计精度;最后,在算法实现步骤的基 础上,利用实车数据开展测试验证工作。 实验结果表明,方法的目标状态估计均方误差为 0. 153,相较于传统卡尔曼滤波减小 了 36. 2% ,证明所提出方法对提升车辆感知精度的有效性。

Kalman filter-based multi-object full lifecycle state estimation in complex traffic flow scenario

Object state estimation always suffers low accuracy in complex traffic flow scenario due to noise interference and vehicle driving state changing. To solve these problems, a Kalman filter-based multi-object full lifecycle state estimation method is proposed for millimeter-wave radar, which includes both parameter initialization and online updating. Firstly, the Kalman filtering-based model is designed for multi-object full lifecycle state estimation in complex traffic flow scenario. Then, a data-driven approach is innovatively proposed for the observation noise covariance matrix initialization in Kalman filter. Furtherly, a variational Bayesian method is applied to update the Kalman filter parameters online for further enhancing the accuracy of multi-object full lifecycle state estimation. Finally, experimental data collecting from real vehicles are utilized to analyze the proposed method. The results show that the mean square error of this method is 0. 153 in multi-object state estimation, which is reduced by 36. 2% when compared with that of traditional Kalman filter. The comparison results evaluate the effectiveness of the proposed method on vehicle perception.