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