Research ThemeAutonomous Sensing Platforms AimTo develop a combined radar cross-section and synthetic aperture radar machine learning based classifier. Objectives1. Adopt data fusion approaches combining RCS and ISAR information for the studied targets and for the various polarization cases with machine-learning (ML) based methods for target classification. 2. Assess classifier robustness with respect to aspect angle, noise levels, data sampling variations, target positional information, and potential reduced information scenarios. 3. Develop a preliminary data set using simulation, polarization-specific, and measured data of relevant targets while using measurement and modelling facilities within The Scottish Microelectronics Centre (SMC).DescriptionThere is an increasing defense requirement for classification techniques for airborne, terrestrial, and seaborne targets. Typical radar cross-section (RCS) signatures provides information on the electromagnetic scattering of targets while synthetic aperture radar (SAR) provides a two-dimensional representation of the distribution of scattering features in terms of 2D images. These two forms of information are complementary yet different: RCS signatures and measurements describe the aggregate scattering response under particular observation conditions and polarization states, whereas SAR can provide relative spatial details on the scattering features of targets. The PhD research work will aim to investigate a machine-learning (ML) framework for classifying a broad range of airborne, terrestrial, and sea-borne targets using both RCS signatures and ISAR representations. The main motivation will be to assess if non-imaging-type RCS signatures along with SAR-derived information, can provide more robust classification of targets, and compared to when these representations are independently examined. Numerical modelling, full-wave simulations using commercial software tools, and measurement information strategies will be adopted while using the existing facilities within the RF lab at UoE. Closing date: 31 Jan, 2027 Apply now Principal Supervisor Dr Symon Podilchak Assistant Supervisor Dr Mehrdad Yaghoobi Eligibility An engineering or physics background is suitable, but engineering is the preference.Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Funding This position is fully funded.
