Ionospheric, Auroral and Ocean Clutter Models for Monostatic and Bistatic HF Sensing Systems
About the Project
High-frequency (HF) sensing is one of the few technologies able to detect objects and characterise environments far beyond the horizon. By using the ionosphere as a natural reflector, HF over-the-horizon sensing systems can observe regions that are inaccessible to conventional line-of-sight sensing technologies. This makes HF sensing an important capability for space domain awareness, atmospheric monitoring, defence science and resilient observation of the near-Earth environment. However, HF sensing systems do not observe a clean, empty sky. They receive a complex mixture of target echoes, ionospheric clutter, auroral clutter, sea clutter, radio interference and propagation effects driven by space weather. This PhD will develop the physical and computational models needed to understand, predict and mitigate that clutter.
The project will focus on monostatic and bistatic HF sensing systems, where the transmitter and receiver are either co-located or separated. Bistatic and networked HF sensing geometries offer major scientific and operational opportunities, but they also introduce new modelling challenges: different propagation paths, different scattering geometries, changing ionospheric conditions, and more complicated clutter environments. You will develop models of ionospheric and auroral clutter, including how space weather, geomagnetic activity, ionospheric irregularities, travelling ionospheric disturbances and high-latitude electrodynamics affect received signals. You will also develop ocean clutter models, linking HF propagation, sea-surface scattering, Doppler spectra, wave conditions and sensing geometry.
A distinctive feature of this studentship is access to a real HF sensing demonstration system. This means the project will not be purely theoretical. You will have the opportunity to compare models against measured system data, help design experiments, analyse real observations, and test whether modelling assumptions hold under realistic operating conditions. Depending on interest, the work could include ray tracing, signal processing, propagation and scattering modelling, numerical simulation, statistical clutter characterisation, machine learning for classification, or data assimilation using ionospheric observations.
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