Abstract
After a few years of research in the field of through-the-wall radar (TWR) human activity recognition (HAR), I found that we seem to be stuck in the mindset of training on radar image data through neural network models. The earliest related works in this field based on template matching did not require a training process, and I believe they have never died. Because these methods possess a strong physical interpretability and are closer to the basis of theoretical signal processing research. In this paper, I would like to try to return to the original path by attempting to eschew neural networks to achieve the TWR HAR task and challenge to achieve intelligent recognition as neural network models. In detail, the range-time map and Doppler-time map of TWR are first generated. Then, the initial regions of the human target foreground and noise background on the maps are determined using corner detection method, and the micro-Doppler signature is segmented using the multiphase active contour model. The micro-Doppler segmentation feature is discretized into a two-dimensional point cloud. Finally, the topological similarity between the resulting point cloud and the point clouds of the template data is calculated using Mapper algorithm to obtain the recognition results. The effectiveness of the proposed method is demonstrated by numerical simulated and measured experiments. The open-source code of this work is released at: this https URL.
Abstract (translated)
经过几年对穿墙雷达（TWR）人体活动识别（HAR）领域的研究，我发现我们似乎陷入了通过神经网络模型训练雷达图像数据的思维定势中。最早的相关工作基于模板匹配方法，并不需要训练过程，我认为这些方法从未真正消失过。因为这些方法具有较强的物理可解释性，并且更接近于理论信号处理研究的基础。在这篇论文中，我试图回到原来的路径上，尝试摒弃神经网络来完成TWR HAR任务，并挑战仅依靠智能识别实现与基于神经网络模型的同等效果。 具体而言，在本方案中，首先生成TWR的范围-时间图和多普勒-时间图。然后，使用角点检测方法确定地图上的人体目标前景区域和噪声背景初始区域，接着利用多相活动轮廓模型对微多普勒特征进行分割。将微多普勒分割特征离散化为二维点云。最后，使用Mapper算法计算所得点云与模板数据点云之间的拓扑相似性以获得识别结果。 通过数值模拟实验和实测实验验证了所提出方法的有效性。本工作的开源代码可在以下网址获取：[此URL]（请将“this https URL”替换为实际的链接）。
URL
https://arxiv.org/abs/2506.05169
