利用雷达技术进行穿墙人体活动识别:综述

IF 3.5 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Jawad Yousaf;Satanai Yakoub;Sara Karkanawi;Taimur Hassan;Eqab Almajali;Huma Zia;Mohammed Ghazal
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引用次数: 0

摘要

超宽带雷达技术(UWB)通过在监控、搜救、健康监测和军事领域的各种应用,证明了它的重要作用。与传统雷达不同,UWB 雷达使用高频、宽带脉冲,可实现远距离探测和穿透障碍物。本作品深入评述了用于识别房间内人类活动和穿墙(TTW)的 UWB 雷达系统及其他各种应用。在简要讨论了不同的 UWB 雷达工作原理和架构后,本研究探讨了它们在现实世界各种 TTW 应用中的作用。研究对传统研究进行了广泛的性能比较,重点是探测工具、信号处理和成像算法。讨论包括对机器学习模型集成的分析。主要重点是穿墙 (TTW) 场景下的检测、移动、生命体征监测和非人类分类。这项研究通过整合人工智能(AI)和机器人技术,自动精确定位各种场景中的目标,有助于更好地理解不断发展的技术能力。此外,还讨论了 UWB 技术对社会的影响、未来行业趋势、商业前景以及需要理解的伦理问题和未来研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Through-the-Wall Human Activity Recognition Using Radar Technologies: A Review
Ultra-wideband radar technology (UWB) has demonstrated its vital role through various applications in surveillance, search and rescue, health monitoring, and the military. Unlike conventional radars, UWB radars use high-frequency, wide-bandwidth pulses, enabling long-range detection and penetrating obstacles. This work presents an in-depth review of UWB radar systems for recognizing human activities in a room and through-the-wall (TTW) with other diverse applications. After briefly discussing different UWB radar working principles and architectures, the study explores their role in various TTW applications in real-world scenarios. An extensive performance comparison of the legacy studies is presented, focusing on detection tools, signal processing, and imaging algorithms. The discussion includes an analysis of the integration of machine learning models. The primary focus is on the detection, movement, monitoring of vital signs, and nonhuman classifications in the context of Through-The-Wall (TTW) scenarios. This study contributes to a better understanding of evolving technology capabilities by integrating artificial intelligence (AI) and robotics to automate and precisely locate the target in various scenarios. Furthermore, the discussion includes the impact of UWB technology on society, future industry trends, the commercial landscape, and ethical issues to understand and future research.
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来源期刊
CiteScore
6.50
自引率
12.50%
发文量
90
审稿时长
8 weeks
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