Annual Report 2020
Extension of Key Signal Processing Techniques to the Processing of Broadband Multi-Sensor Data
Eigen- and singular value decompositions (EVD and SVD) are mathematical tools that form the core of many information technology systems, and are well-established optimal narrowband analytical techniques. This project aims to extend these algorithms to the broadband case, where data acquired by multiple sensors may span frequencies across several octaves. The theoretical component of this project will extend the EVD and SVD to so-called polynomial matrices, which can describe and formulate broadband problems elegantly. This theoretical research will design a novel set of algorithms for such polynomial matrices, enabling a transformative way in which broadband problems can be described. This project will provide an innovative processing technique to hyperspectral imaging, where a wealth of information is hidden in a wide frequency range. A further application will be sonar signal processing, where the proposed techniques will provide a tool to extract the information acquired by many sensors.
Awarded: Carnegie-Caledonian PhD Scholarship
Field: Electrical Engineering
University: University of Strathclyde