Abstract

The intelligent reduction of data is an important consideration in both civilian and defence applications. In such scenarios, which are often constrained by low size, weight, power, and cost (SWAP-C) requirements, effective compression strategies can be deployed. This talk will present recent information-theoretic strategies for the simultaneous compression of data from multiple information sources. The proposed framework can adapt to changing source statistics and can therefore yield compression strategies that mitigate or exploit new, potentially unforeseen, information sources in defence applications. As an example, this talk will describe the use of this framework in a network of distributed, low SWAP-C radio frequency sensors deployed in a contested electromagnetic environment. Here, the low-cost sharing of positioning, navigation, and timing (PNT) information from multiple sensors could ultimately aid rapid threat recognition and collaborative localisation.

Biography

Dr Fraser K. Coutts received the M.Eng. degree (with distinction) in Electronic and Electrical Engineering and the Ph.D. degree from the University of Strathclyde in 2015 and 2019, respectively. He was a Ph.D. Researcher within the Centre for Signal & Image Processing, University of Strathclyde, holding a prestigious Caledonian Scholarship from the Carnegie Trust. He is currently a Research Associate with the Institute for Imaging, Data, and Communications (IDCOM), University of Edinburgh, and has worked closely with members of the Defence Industry as part of the third phase of the University Defence Research Collaboration (UDRC) project. He successfully applied new techniques developed during the UDRC project to Defence scenarios through the completion of a Bright Corvus Defence and Security Accelerator (DASA) project and a recent Dstl secondment. His interests span information-theoretic compressive sensing, radar, hyperspectral imaging, and linear algebraic methods for signal processing. He has authored or co-authored more than 30 peer-reviewed conference and journal papers in his research field and was a recipient of the Best Paper Award at IEEE SAM 2018. He has collaborated widely and has extensive experience in algorithm development for signal and image processing.