10 December 2020

SIGNets to advance fundamental research in distributed sensing

Universities of Cambridge, Surrey and Sheffield will partner to address fundamental challenges in distributed sensing, multimodal data fusion and autonomous sensor management that the defence sector is facing.

SIGNetS Logo

The project titled, “SIGNetS: signal and information gathering for networked surveillance", is funded under the scheme “Signal and Information Processing for Decentralised Intelligence, Surveillance, and Reconnaissance”, aims to address fundamental challenges associated with information processing and fusion in decentralised network of sensors in “Intelligence, Surveillance and Reconnaissance “ (ISR). The project is being funded by a $1.2M grant awarded by US Army and UK Ministry of Defence (MoD).

Heterogeneous and distributed sensors often are used to perform a variety of tasks such as detection, classification, recognition, localisation or tracking of objects and their states, in a contested environment, with jamming, spoofing, or destructive attacks. This raises challenges for distributed multimodal information fusion with uncertainty quantification. In addition, the sensors used may be low-cost, of limited capabilities (e.g. in processing, storage, battery), this raises the challenges of scalability of computations. In complex environments, it will be important to meet the sensing demand with adaptive management of sensors, such as resource allocation, and sensor processing and coordination. This raises the challenges of autonomous sensor management. This project aims to address these challenges, and develop new methods for uncertainty quantification, scalable Bayesian inference, intent prediction, and autonomous sensor management and communication.

This project will form a new “Application Theme” under the University Defence Research Collaboration (UDRC) in signal processing (Phase 3).

Professor Simon Godsill, the project lead, and Principal Investigator at University of Cambridge,  said,

“We are very happy to have been awarded this grant under a very competitive application process. The topic of the project in decentralised processing of multiple sensor platforms is an excellent fit with our current research programmes at Cambridge in large scale probabilistic calculations, inference about networks and groups of objects, and in analysis of intentionality  in object dynamics. We look forward to fruitful interactions with our project partners at Sheffield and Surrey, as well as the works sponsors.”

I am delighted to receive this prestigious grant award. I am grateful to have this opportunity to work with a multidisciplinary team and international collaboration. Creating trustworthy methods that are able to work in dynamic environments, subject to changes and by making sense from data is in the heart of SIGNeTs project. Resilience, safety and reliability are key aspects to such trustworthy autonomous systems. This is a fantastic opportunity for co-creation and collaborations.

Professor Lyudmila Mihaylova

Professor of Signal Processing and Control, Principal Investigator at University of Sheffield

Professor Wenwu Wang, Professor in Signal Processing and Machine Learning, and Principal Investigator at University of Surrey, said,

“I am delighted for receiving this very competitive grant to pursue research in addressing fundamental challenges in distributed sensor fusion and autonomous sensor management. I have received funding support from both Phase 1 and Phase 2 of UDRC. With the Phase 3 grant, I will be able to continue working with Dstl, UDRC community, and the wide defence sector.”

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