基于三维点云SVT算法的MIMO雷达测流方法

doi:10.3969/j.issn.1006-2475.2025.12.001

摘要/Abstract

摘要： 摘要：河道流速是水文监测的关键参数，能够为水情掌握、水量调控及洪涝灾害预防提供重要信息。雷达流速测量技术相比传统接触式的流速测量方法具有非接触式测量不受水体条件影响和实时监测等优势。为提高河道流速测量的精度和实时性，本文采用一种基于三维点云SVT算法的MIMO雷达测流方法，通过空间、速度和时间3个维度对点云进行投影滤波、栅格分区去噪和尺度修正等处理，以减少噪声干扰和数据冗余，提高流速测量的准确性和稳定性，进而直观展示河道表面多点流速分布情况。雷达可以采用岸基侧向安装方式，降低对安装环境的要求，实现河道表面多点流速测量。实验结果表明，中高流速场景下（v≥0.5 m/s），相对误差小于5%；低流速场景下（0.3 m/s≤v≤0.5 m/s），绝对误差小于5 cm/s。综上，该方法能够有效提高河道流速测量的准确性和稳定性，为水文监测提供技术支持。

关键词: 关键词：MIMO雷达, 点云, 流速, 数据处理, 栅格分区, 去噪

Abstract: Abstract: River surface velocity is a key parameter in hydrological monitoring, as it provides essential information for understanding hydrological conditions, regulating water volume, and preventing flood disasters. Compared with traditional contact-based flow velocity measurement methods, radar-based flow velocity measurement technology boasts advantages such as non-contact measurement (which is not affected by water body conditions) and real-time monitoring capabilities. To improve the accuracy and real-time performance of river channel flow velocity measurement, this study adopts a MIMO radar flow measurement method based on the 3D point cloud SVT algorithm. Through processing steps including projection filtering, grid partition denoising, and scale correction applied to the point cloud across three dimensions—space, velocity, and time—this method reduces noise interference and data redundancy, enhances the accuracy and stability of flow velocity measurement, and further enables the intuitive presentation of multi-point flow velocity distribution on the river channel surface. The radar can be installed in a shore-based lateral manner, which lowers the requirements for the installation environment and realizes multi-point flow velocity measurement on the river channel surface. Experimental results show that in medium-to-high flow velocity scenarios (v≥0.5 m/s), the relative error is less than 5%; in low flow velocity scenarios (0.3 m/s≤v＜0.5 m/s), the absolute error is less than 5 cm/s. In summary, this method can effectively improve the accuracy and stability of river channel flow velocity measurement, providing technical support for hydrological monitoring.

Key words: Key words: MIMO radar, point cloud, flow velocity, data processing, grid partitioning, denoising

中图分类号:

中图分类号：TN95

李健, 宋钰, 张文鑫, 于然. 基于三维点云SVT算法的MIMO雷达测流方法[J]. 计算机与现代化, 2025, 0(12): 1-.

LI Jian, SONG Yu, ZHANG Wenxin, YU Ran. Flow Velocity Measurement Method Using MIMO Radar Based on 3D Point Cloud SVT Algorithm[J]. Computer and Modernization, 2025, 0(12): 1-.

参考文献

[1] 许盈松,蔡俊锋,周湘俊,等. 旋杯式与旋浆式流速仪观测特性分析研究[J]. 中华水土保持学报，2007,38(2): 185-194.
[2] 梁岚珍,陈志军,王一波. 旋桨式流速仪信号检测器[J].自动化仪表, 2003(5):41-43.
[3] CALLEDE J, KOSUTH P, GUYOT J, et al. Discharge determination by acoustic Doppler current profilers (ADCP): A moving bottom error correction method and its application on the river amazon at Óbidos[J]. Hydrological Sciences Journal, 2000,45(6):911-924.
[4] PETRIE J, DIPLAS P, GUTIERREZ M, et al. Data evaluation for acoustic Doppler current profiler measurements obtained at fixed locations in a natural river[J]. Water Resources Research, 2013,49(2):1003-1016.
[5] NINOS A, HASCH J, HEIZMANN M, et al. Radar based robust people tracking and consumer applications[J]. IEEE Sensors Journal, 2022,22(4):3726-3735.
[6] PLANT W J, BRANCH R, CHATHAM G, et al. Remotely sensed river surface features compared with modeling and in situ measurements[J]. Journal of Geophysical Research: Oceans, 2009,114(C11). DOI: 10.1029/2009JC-
005440.
[7] 王才军,文必洋,李柯,等. 河图雷达武汉东湖表面波浪探测实验[DB/OL]. (2014-04-13)[2024-12-01]. https://www.doc88.com/p-8949046153834.html.
[8] PLANT W J, KELLER W C. Evidence of bragg scattering in microwave Doppler spectra of sea return[J]. Journal of Geophysical Research: Oceans, 1990,95(C9):16299-16310.
[9] COSTA J E, SPICER K R, CHENG R T, et al. Measuring stream discharge by non-contact methods: A proof-of-concept experiment[J]. Geophysical Research Letters, 2000,27(4):553-556.
[10] LEE M C, LAI C J, LEU J M, et al. Non-contact flood discharge measurements using an X-band pulse radar (I) theory[J]. Flow Measurement and Instrumentation, 2002,13(5-6):265-270.
[11] CHEN F W, LIU C W. Assessing the applicability of flow measurement by using non-contact observation methods in open channels[J]. Environ Monit Assess, 2020,192(5): DOI: 10.1007/s10661-020-8226-1.
[12] MA Z G, WEN B Y, WANG C J, et al. UHF surface velocities radar system design[C]// Proceedings of the 2005 IEEE Conference on Electron Devices and Solid-State Circuits. IEEE，2005:431-433.
[13] WANG C J, WEN B Y, MA Z G, et al. Measurement of river surface currents with UHF FMCW radar systems[J]. Journal of Electromagnetic Waves and Applications, 2007,21(3):375-386.
[14] 李自立,王才军,李永辉. 基于超高频雷达的流量测量算法研究:以长江武汉段为例[J]. 武汉大学学报(理学版), 2013,59(3):242-244.
[15] YANG Y H, WEN B Y, WANG C J, et al. Real-time and automatic river discharge measurement with UHF radar[J]. IEEE Geoscience and Remote Sensing Letters, 2020,17(11):1851-1855.
[16] 何子述,程子扬,李军,等. 集中式MIMO雷达研究综述[J]. 雷达学报, 2022,11(5):805-829.
[17] FUHRMANN D R, SAN ANTONIO G. Transmit beamforming for MIMO radar systems using signal cross-correlation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2008,44(1):171-186.
[18] BEKKERMAN I, TABRIKIAN J. Target detection and localization using MIMO radars and sonars[J]. IEEE Transactions on Signal Processing, 2006,54(10):3873-3883.
[19] XU L Z, LI J, STOICA P. Target detection and parameter estimation for MIMO radar systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 2008,44(3):927-939.
[20] FISHLER E, HAIMOVICH A, BLUM R. Spatial diversity in radars-models and detection performance[J]. IEEE Transactions on Signal Processing, 2006,54(3):823-838.
[21] 何子述,李军,刘红明,等. MIMO雷达[M]. 北京: 国防工业出版社, 2017.
[22] LI J, STOICA P. MIMO radar with collocated antennas[J]. IEEE Signal Processing Magazine, 2007,24(5):106-114.
[23] 何子述,韩春林,刘波. MIMO雷达概念及其技术特点分析[J]. 电子学报, 2005,33(12A):2441-2445.
[24] 冯心欣,李文龙,何兆,等. 基于调频连续波雷达的多维信息特征融合人体姿势识别方法[J]. 电子与信息学报, 2022,44 (10):3583-3591.
[25] 杨冬,曾春艳,郝丹妮,等．针对毫米波雷达人员目标稀疏点云的聚类算法[J]．计算机工程与应用, 2025,61(3):359-366.
[26] WANG M F, WANG F G, LIU C Y, et al. DBSCAN clustering algorithm of millimeter wave radar based on multi frame joint[C]// 2022 4th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP). IEEE, 2022:1049-1053.
[27] 赵夫群,汤慧. 层次化的散乱点云简化算法[J]. 激光与光电子学进展, 2022,59 (18):1811006-1-1811006-7.