Staff Profile

My research background is in Signal & Image Processing (machine learning) with applications to Radar Systems/ Autonomous Driving, Wireless Communications and Sensing Networks. I have supervised undergrad and postgrad students.

I have taught modules on Numerical Methods, Machine Learning and Sensor Fusion.

I am a Senior Member IEEE, and a Fellow HEA.

Research Themes/Projects

Machine/Deep Learning-based Radar Imaging

This is the current focus of my research. We are proposing new Machine/Deep Learning techniques for Radar imaging particularly in the Autonomous Driving scenarios, where we do not have the ground-truth. These techniques fall under the category of Unsupervised Learning. The problem of recovering an image or a sensed phenomenon from its measurements is known as the inverse-problem. Thus, in the proposed project, our aim is to solve the inverse problem of Radar imaging through Unsupervised Learning.

Enhanced Automotive Radar Imaging for Autonomous Driving

As a part of this Jaguar Land Rover and the UK-EPSRC jointly funded Towards Autonomy: Smart and Connected Control (TASCC) project, I developed signal & image processing algorithms for all-weather automotive low-THz (150-300GHz) radar for autonomous/self-driving cars, for radar azimuth resolution improvement, in order to create viable fusion strategies. This was a collaborative work between three universities, i.e., University of Edinburgh, University of Birmingham and Heriot-Watt University. This project provided the opportunity of working directly with industrial partners in order to develop implementable research strategies.

 Part of this work has been patented by Jaguar Land Rover.

Compressive Sampling for Wireless Communications

In this project, I designed sub-sampling methods for ultra-wideband signals to reduce the sampling rates much below the Nyquist rate in order to save power and reduce computational complexity, worked on different optimization problems, developed algorithms for solving the optimization problems in an efficient manner, performed theoretical performance analyses as well as MATLAB simulations to validate the derived results, results were shared with industrial users.

Student Supervision

PhD Students

Vijith Kotte (2020 - 2022): MIMO Radar Imaging for Automotive Scenarios (King Abdullah University of Science & Technology)

Sultan Alshirah (2018 - 2020): Waveform Design for Radar Target Classification (University of Edinburgh)

Postgraduate Teaching

Numerical Methods (Anglia Ruskin University)

Machine Learning and Computer Vision (Anglia Ruskin University)

Sensing and Sensor Fusion (Anglia Ruskin University)