PhD in Electronic Engineering

Climate change is driving more frequent and severe wildfires in the UK and globally. Even small ignition events can escalate rapidly, yet many landscapes still lack affordable and continuous monitoring./nThis PhD will explore a novel solution: using ambient Radio Frequency (RF) signals as passive environmental probes. The proposed system will use Signals of Opportunity (SoO), such as TV and radio broadcasts, cellular networks, and other existing transmitters, to detect small perturbations in the RF propagation channel caused by wildfire effluents. Inspired by passive radar principles, this approach could enable low-cost, wide-area coverage without the constraints of optical visibility./nThe PhD will develop RF propagation models that incorporate the effects of fire effluents, validated through controlled experimentation. You will develop tomographic inversion methods and anomaly-detection algorithms capable of separating fire-induced signatures from natural environmental variability (weather, canopy changes, tree motion) and fluctuations in the SoO sources themselves. Machine-learning methods will help improve long-term robustness and minimise false alarms./nPrimary supervisor: Dr Chinthana Panagamuwa/nSecondary supervisor: Dr Kostas Kyriakopoulos/nLocation: Loughborough.