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arXiv 2403.15468eess.SP
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使用原始雷达ADC数据和参数化神经网络在真实穿墙环境中进行人体检测

Human Detection in Realistic Through-the-Wall Environments using Raw Radar ADC Data and Parametric Neural Networks

Wei Wang, Naike Du, Yuchao Guo, Chao Sun, Jingyang Liu, Rencheng Song, Xiuzhu Ye

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AI总结：

提出端到端穿墙雷达人体检测网络TWP-CNN，利用基于DFT的自适应特征提取模块处理原始ADC信号，结合相位信息与多任务学习提升检测精度，并构建真实穿墙数据集验证其优越性。

摘要：

雷达信号处理算法是穿墙雷达人体检测技术的核心组件之一。传统算法（如DFT和匹配滤波）难以自适应地处理具有挑战性和动态变化的现实世界穿墙应用环境中的低信噪比回波信号，这成为系统的主要瓶颈。本文提出了一种端到端的穿墙雷达人体检测网络（TWP-CNN），该网络以未经任何预处理的原始雷达模数转换器（ADC）信号作为输入。我们用所提出的基于DFT的自适应特征提取（DAFE）模块取代了传统的雷达信号处理流程。该模块采用可学习的参数化3D复数卷积层从ADC信号中提取优异的特征表示，这超越了传统预处理方法的局限。此外，通过在网络中嵌入雷达数据的相位信息并采用多任务学习，实现了更准确的检测。最后，由于缺乏包含原始ADC数据的穿墙雷达数据集，我们利用自研的穿墙雷达系统收集了一个真实穿墙（RTW）数据集。我们在该数据集上训练并验证了所提出的方法，以确认其在真实穿墙检测场景中的有效性和优越性。

英文摘要：

The radar signal processing algorithm is one of the core components in through-wall radar human detection technology. Traditional algorithms (e.g., DFT and matched filtering) struggle to adaptively handle low signal-to-noise ratio echo signals in challenging and dynamic real-world through-wall application environments, which becomes a major bottleneck in the system. In this paper, we introduce an end-to-end through-wall radar human detection network (TWP-CNN), which takes raw radar Analog-to-Digital Converter (ADC) signals without any preprocessing as input. We replace the conventional radar signal processing flow with the proposed DFT-based adaptive feature extraction (DAFE) module. This module employs learnable parameterized 3D complex convolution layers to extract superior feature representations from ADC signals, which is beyond the limitation of traditional preprocessing methods. Additionally, by embedding phase information from radar data within the network and employing multi-task learning, a more accurate detection is achieved. Finally, due to the absence of through-wall radar datasets containing raw ADC data, we gathered a realistic through-wall (RTW) dataset using our in-house developed through-wall radar system. We trained and validated our proposed method on this dataset to confirm its effectiveness and superiority in real through-wall detection scenarios.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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