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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

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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.

multi-object state estimationKalman filteringparameters initializationparameters online updating

刘明杰、陈俊虎、刘平、陈俊生、朴昌浩

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重庆邮电大学自动化学院 重庆 400065

多目标状态估计 卡尔曼滤波 参数初始化 参数在线更新

国家重点研发计划重庆市教委科学技术研究计划重庆市教委科学技术研究计划

2022YFE0101000KJQN202200630KJQN202100620

2024

仪器仪表学报
中国仪器仪表学会

仪器仪表学报

CSTPCD北大核心
影响因子:2.372
ISSN:0254-3087
年,卷(期):2024.45(1)
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