[论文解读] Range-Max Enhanced Ultra-Wideband Micro-Doppler Signatures of Behind Wall Indoor Human Activities

Qiang An, Shuoguang Wang+5 more|arXiv (Cornell University)|Jan 28, 2020

Advanced SAR Imaging Techniques参考文献 69被引用 8

一句话总结

本文提出了一种增强最大距离（range-max）的超宽带（UWB）雷达方法，以提升在不透明墙体后检测和分类人体活动时的微多普勒特征提取效果。通过应用高通滤波器去除墙体引起的直流分量，并引入一种新颖的范围最大值增强策略，该策略在各距离门中选取最强的微多普勒特征，该方法显著提高了时频特征的清晰度，并在传统基于STFT的方法之上提升了运动分类的准确性。

ABSTRACT

Penetrating detection and recognition of behind wall indoor human activities has drawn great attentions from social security and emergency service department in recent years since intelligent surveillance aforehand could avail the proper decision making before operations being carried out. However, due to the influence of the wall effects, the obtained micro-Doppler signatures would be severely degenerated by strong near zero-frequency DC components, which would inevitably smear the detailed characteristic features of different behind wall motions in time-frequency (TF) map and further hinder the motion recognition and classification. In this paper, an ultra-wideband (UWB) radar system is first employed to probe through the opaque wall to detect the behind wall motions, which often span a certain number of range bin cells. By employing such a system, a high resolution range map can be obtained, in which the embedded rich range information is expected to be fully exploited to improve the subsequent recognition and classification performance. Secondly, a high-pass filter is applied to remove the effect of the wall in the raw range map. Then, with the aim of enhancing the characteristic features of different behind wall motions in TF maps, a novel range-max enhancement strategy is proposed to extract the most significant micro-Doppler feature of each TF cell along all range bins for a specific motion. Lastly, the effectiveness of the proposed micro-Doppler signature enhancement strategy is investigated by means of onsite experiments and comparative classification. Both the feature enhanced TF maps and classification results show that the proposed approach outperforms other state-of-art Short-Time Fourier Transform (STFT) based TF feature extraction methods.

研究动机与目标

• 解决UWB雷达系统中因墙体穿透导致的微多普勒特征退化问题，其主要表现为强烈的直流分量影响。
• 提升时频（TF）图中与运动相关的微多普勒特征的可见性和显著性，以实现更优的分类效果。
• 开发一种范围最大值增强策略，以从多个距离门中提取最显著的微多普勒特征。
• 通过现场实验和对比分类性能分析验证所提方法的有效性。
• 在识别墙体后人体运动方面，超越现有基于STFT的微多普勒特征提取技术。

提出的方法

• 使用超宽带（UWB）雷达系统获取墙体后人体活动的高分辨率距离图。
• 对原始距离图应用高通滤波器，以抑制由墙体反射引起的主导直流分量。
• 提出一种新颖的范围最大值增强策略，针对每个时频单元，从所有距离门中选取最大微多普勒能量。
• 通过时频分析（特别是短时傅里叶变换，STFT）提取增强后的微多普勒特征。
• 该方法强调从每个时频点中最具响应能力的距离门提取特征，以保留与运动相关的动态特性。
• 利用在真实室内环境中采集的实验数据评估分类性能，实验环境包含不透明墙体。

实验结果

研究问题

• RQ1 墙体引起的直流能量在多大程度上影响了墙体后人体活动检测中微多普勒特征的清晰度？
• RQ2 高通滤波与范围最大值增强在多大程度上提升了时频图中与运动相关的微多普勒特征的可见性？
• RQ3 所提出的范围最大值增强策略与传统基于STFT的特征提取方法相比，在分类墙体后人体运动时表现如何？
• RQ4 增强后的微多普勒特征是否能在真实世界墙体后场景中实现更高的分类准确率？
• RQ5 距离门的选择对微多普勒特征在运动识别中的鲁棒性和显著性有何影响？

主要发现

• 所提出的范围最大值增强策略显著提升了时频图中微多普勒特征的清晰度和显著性。
• 高通滤波有效抑制了墙体引起的直流分量，减少了频谱模糊，提升了特征分辨率。
• 增强后的微多普勒特征相较于标准STFT方法表现出更优的分类性能。
• 现场实验结果证实了该方法在真实墙体后检测场景中的有效性。
• 通过强调每个时频单元中最具信息量的距离门，该方法实现了更高的运动识别准确率。
• 高通滤波与范围最大值增强的结合，生成了更具鲁棒性和判别性的特征表示，适用于分类任务。