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| 毫米波雷达自适应门限点云成像方法研究 | |
| 引用本文： | 兰吕鸿康,黄岩,郑凯航,刘江,刘育铭,张慧,洪伟. 毫米波雷达自适应门限点云成像方法研究[J]. 信号处理, 2022, 38(10): 2009-2020. DOI: 10.16798/j.issn.1003-0530.2022.10.002 |
| 作者姓名： | 兰吕鸿康 黄岩 郑凯航 刘江 刘育铭 张慧 洪伟 |
| 作者单位： | 东南大学毫米波国家重点实验室，江苏 南京 210096 |
| 基金项目： | 国家自然科学基金61901112江苏省自然科学基金BK20190330上海航天科技创新基金SAST2021-043航空基金20200020069001 |
| 摘 要： | 毫米波雷达作为一种重要的车载传感器，在自动驾驶领域得到了广泛地应用。近年来随着汽车智能化程度的提高，高质量雷达点云的生成受到了人们的极大关注。传统毫米波雷达点云成像由于存在杂波点太多、有效点云稀疏等缺点而限制了其在自动驾驶领域的发展。因此，如何提高毫米波雷达点云密度和质量成为了业界研究的重点问题。近年来，随着多输入多输出（MIMO）技术以及控制多片级联同步技术的成熟，使得毫米波雷达天线的角度分辨率得到了极大提升，推动了毫米波雷达在点云成像上的发展。在此基础上，本文设计了一套完整的毫米波雷达系统级点云成像算法，并使用TI公司的AWR2243级联雷达开发套件对实际场景进行数据采集，生成了较为致密可信的毫米波雷达三维点云图像，基本实现了对车载平台侧面场景的有效还原。 |
| 关 键 词： | 毫米波雷达 点云成像 自动驾驶 多输入多输出(MIMO) |
| 收稿时间： | 2022-07-13 |
Research on Adaptive Threshold Point Cloud Imaging Method of Millimeter-Wave Radar |
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| Affiliation: | State Key Lab of Millimeter Waves，Southeast University，Nanjing，Jiangsu 210096，China |
| Abstract: | ? ?As an important vehicle-mounted sensor， millimeter-wave radar has been widely used in the field of autonomous driving. In recent years， with the improvement of automobile intelligence， the generation of high-quality radar point clouds has received great attention. Traditional millimeter-wave radar point cloud imaging has many shortcomings such as too many clutter points and sparse effective point clouds， which limit its development in the field of autonomous driving. Therefore， how to improve the density and quality of millimeter-wave radar point clouds has become a key issue in industry research. In recent years， with the maturity of multiple-input multiple-output （MIMO） technology and control of multi-chip cascaded synchronization technology， the angular resolution of millimeter-wave radar antennas has been greatly improved， which has promoted the application of millimeter-wave radar in point cloud imaging. develop. On this basis， this paper designs a complete set of millimeter-wave radar system-level point cloud imaging algorithms， and uses TI's AWR2243 cascaded radar development kit to collect data from the actual scene， generating a relatively compact and reliable millimeter-wave radar. The three-dimensional point cloud image basically realizes the effective restoration of the side scene of the vehicle platform. |
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