Development of hand-held devices based on UWB, mmWave, THz or WiFi signals to see through walls for human activity monitoring

Summary

This project focuses on developing portable hand-held devices that leverage UWB, mmWave, THz, or WiFi signals to enable through-wall sensing and human activity monitoring. The research aims to design innovative hardware and signal processing techniques for accurate, real-time, and non-intrusive detection in diverse environments.

Supervisor

Dr Zihuai Lin.

Research location

Electrical and Computer Engineering

Synopsis

The project aims to design and prototype portable sensing systems that utilise advanced wireless signals such as ultra-wideband (UWB), millimetre-wave (mmWave), terahertz (THz), and WiFi for non-intrusive human detection. By exploiting the unique propagation and penetration characteristics of these signals, the research will explore advanced channel modelling, radar signal processing, and machine learning algorithms to accurately capture human presence, gestures, and vital signs behind obstacles. The system will be designed for real-time operation with low power consumption, enabling applications in emergency rescue, elderly care, security, and smart living environments. The outcomes of this project will provide a foundation for next-generation intelligent sensing devices that combine hardware innovation with AI-driven signal interpretation to achieve robust and reliable human activity monitoring in complex indoor and outdoor scenarios.

Research Techniques/Methodologies/Technologies

The research will employ UWB, mmWave, THz, and WiFi-based sensing platforms, advanced radar signal processing, channel estimation, imaging algorithms, and AI/ML techniques (e.g., deep learning for activity recognition). Hardware prototyping with software-defined radios (SDR) or custom RF front-ends will be combined with simulation and experimental validation.

Successful candidates:

Candidates should have a strong background in wireless communications, signal processing, or RF engineering. Experience with programming (Python, MATLAB, or C/C++), machine learning frameworks, or hardware prototyping (SDR, RF circuits) is desirable. The candidate should be highly motivated, possess strong analytical skills, and demonstrate the ability to work independently as well as collaboratively within interdisciplinary teams.

How to apply:

To apply, please email [email protected] the following:

a detailed CV, including academic qualifications, research experience and publications
academic transcripts

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Opportunity ID

The opportunity ID for this research opportunity is 3700