隧道环境毫米波雷达目标识别与分类算法

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中图分类号：TN958.94 文献标志码：A DOI：10.12305/j.issn.1001-506X.2025.05.08

Abstract：Milimeter wave（MMW）radar exhibits all-weather capability，high precision，low cost，and non-contact sensing，rendering it highly suitable for safety monitoring in tunnels.However，due to severe multipath interference in tunnels，classical signal processing algorithms have a high error rate in target recognition.The high computational complexityand poor real-time performance of three-dimensional（3D） convolutional deep learning algorithms hinder the application of MMW radar in tunnels.To regard this，an eficientdeep-learning algorithm scheme is proposed，which can achieve high-precision real-timepositioningand classification of targets suchas individuals，vehicles，and other targets.The algorithmutilizesa signal processing method to compress and encode the radar intermediate frequency data across multiple dimensions， uses Mamba network to extract features from radar spatio-temporal sequence data，uses heatmap of field data to estimate target location，and uses only local regional features of target location to estimate target category， avoidingincoherent regional signal interferenceand improving target recognition accuracy.The algorithm is designed based on two-dimensional （2D） convolution，and a nonlinear mapping relationship between radar data and target locationand category is established，effctively managing computational complexity.Experiments in thetunnel show that the mean intersection over union （mIoU），average precision（AP）and speedof the algorithm are respectively 0.68， 65.26% ， 22.5ms /frame，compared with the 3D convolutional CenterRadarNet algorithm，mIoU is reduced by 3% ，APis increased by 9% ，and speed is increased by 53.3% ，which proves that the algorithm has good performance and has application value in actual application.

Keywords：millimeter wave（MMW） radar；target recognition；real time；deep learning；tunnel

0 引言

隧道属于地下工程，地质结构、施工条件复杂恶劣，通讯交流不便，在狭小施工空间中人、机、车繁杂，难以进行有效管理，容易引发安全事故，使得施工过程中存在极大的安全隐患，因此需要对隧道施工过程中人员、车辆进行数字化、智能化监测，对一些潜在危险及时预警，提高隧道施工过程中安全监测水平。（剩余13888字）