隧道環(huán)境毫米波雷達目標識別與分類算法

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中圖分類號：TN958.94 文獻標志碼：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

隧道屬于地下工程，地質結構、施工條件復雜惡劣，通訊交流不便，在狹小施工空間中人、機、車繁雜，難以進行有效管理，容易引發(fā)安全事故，使得施工過程中存在極大的安全隱患，因此需要對隧道施工過程中人員、車輛進行數(shù)字化、智能化監(jiān)測，對一些潛在危險及時預警，提高隧道施工過程中安全監(jiān)測水平。（剩余13888字）