基于无损卡尔曼滤波的车载双雷达目标位置估计方法

在无人驾驶汽车的研究中，对于传感器探测到的目标进行状态估计是环境感知技术的关键问题之一。本文提出了一种基于无损卡尔曼滤波器的算法，根据所获得的经过标记的雷达数据对目标的位置状态进行预测和更新，从而估计无人驾驶车辆双雷达系统的目标位置。本文中的车载雷达系统由四线激光雷达和毫米波雷达组成，标定后的车辆坐标系为与地面平行的二维坐标系，在此系统和坐标系基础上，在实验场地采集真实雷达数据并进行仿真计算。实验证明，相较于单一传感器，雷达组合模型的测量误差得到有效降低，融合数据精度提高。而相较于目前最常用的扩展卡尔曼滤波算法，车辆行驶方向上的平均位置均方误差从6.15‰下降到4.83‰，与车前轮轴平行的方向...

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Published in:Guang Dian Gong Cheng = Opto-Electronic Engineering 2019-07, Vol.46 (7), p.180339-104
Main Authors: 向易, 汪毅, 张佳琛, 蔡怀宇, 陈晓冬 / Xiang Yi, Wang, Yi, Zhang Jiachen, Cai Huaiyu, Chen, Xiaodong
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Language:chi ; eng
Subjects:
Algorithms
Automation
Computer simulation
Coordinates
Engineering
Error analysis
Error detection
Information technology
Kalman filters
Laboratories
Lasers
Mean square errors
Millimeter waves
Model accuracy
Radar data
Radar equipment
Radar systems
Sensors
Shafts (machine elements)
State estimation
Target detection
Unmanned vehicles
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container_end_page 104
container_issue 7
container_start_page 180339
container_title Guang Dian Gong Cheng = Opto-Electronic Engineering
container_volume 46
creator 向易
汪毅
张佳琛
蔡怀宇
陈晓冬 / Xiang Yi
Wang, Yi
Zhang Jiachen
Cai Huaiyu
Chen, Xiaodong
description 在无人驾驶汽车的研究中，对于传感器探测到的目标进行状态估计是环境感知技术的关键问题之一。本文提出了一种基于无损卡尔曼滤波器的算法，根据所获得的经过标记的雷达数据对目标的位置状态进行预测和更新，从而估计无人驾驶车辆双雷达系统的目标位置。本文中的车载雷达系统由四线激光雷达和毫米波雷达组成，标定后的车辆坐标系为与地面平行的二维坐标系，在此系统和坐标系基础上，在实验场地采集真实雷达数据并进行仿真计算。实验证明，相较于单一传感器，雷达组合模型的测量误差得到有效降低，融合数据精度提高。而相较于目前最常用的扩展卡尔曼滤波算法，车辆行驶方向上的平均位置均方误差从6.15‰下降到4.83‰，与车前轮轴平行的方向上，平均位置均方误差值从4.24‰下降到2.99‰，表明本文算法的目标位置估计更加精确，更接近实际值。此外，在同样的运行环境下，本文算法处理500组雷达数据的平均时间也从5.9 ms降低到了2.1 ms，证明其有更高的算法效率。
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subjects Algorithms
Automation
Computer simulation
Coordinates
Engineering
Error analysis
Error detection
Information technology
Kalman filters
Laboratories
Lasers
Mean square errors
Millimeter waves
Model accuracy
Radar data
Radar equipment
Radar systems
Sensors
Shafts (machine elements)
State estimation
Target detection
Unmanned vehicles
title 基于无损卡尔曼滤波的车载双雷达目标位置估计方法
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