Electromagnetic Environment

Joint Imperial College London - University College London consortium - 3 year (April 2019-March 2022), in collaboration with underpinning UDRC consortium (UEDIN) and DSTL

Challenge
Information extraction and delivery of signals in EM environment
Accounting for specificities of Electronic Warfare:
high dimensionality, complex and congested EM environment, mix of threat and benign signals, wide range of platforms, systems and applications, lack of cooperation, covert transmission, demand of extremely high reliability

Aims
Addresses signal extraction and delivery jointly as integral parts of a single, coherent research programme
Brings together state-of-the-art expertise in signal processing for civilian and military RF applications

 

View Overview of Theme Tracks

Research

WP1 Sensing Signals and Extracting Information

Develop a unified framework to seamlessly extract four modes of information from signals: time, frequency, space, and waveform

T1.1. Super-resolution with Unknown Waveforms

Detect and locate threat signals in spectrum, time, and space, even when we don’t know what they look like

If waveforms are unknown, correlation based localisation does not work

New blind super-resolution framework

T1.2. Low-probability-of-intercept (LPI) Signal Detection/Classification

Specific instance of the framework

Develop a flexible RF chain model, generate example datasets, test super-resolution and subspace methods

T1.3. Learning for the Super-Resolution Framework

DNN to track the relationships among the four modes of information

Provide new insights and enhance the super-resolution framework

WP2 Signal Designs and Delivery

Study a network-wideand robust optimization of waveforms for sensing, signalling and joint sensing-signaling purposes

T2.1. Waveform Design for Sensing beyond the Ambiguity Function

Characterize the dependence of the Ambiguity Function on waveform and bistatic geometry (spatially distributed nodes)

Network-wide processing with spatially distributed nodes: optimal transmit receive pair, dynamic emitter selection, single/multi-target tracking

T2.2. Waveform Design for Precise Spatio-Temporal Signaling

Enabling precise spatiotemporal signalling and energy delivery while dynamically managing EM interference

Network-wide and robust optimisation of waveforms with imperfect knowledge of the channel state, with spatially distributed nodes

Time-reversal (TR) waveforms to enable high energy focusing WP2 Signal Designs and Delivery

T2.3. Joint Waveform Design for Sensing and Signaling

Decongesting the spectrum: Waveforms for remote sensing that carry covert/non-covert information

T2.4. Hardware and Nonlinearity ResilientWaveform Design

Hardware limitations: low-resolution ADCs, power amplifiers with low dynamic range and other low-spec circuits;

Nonlinear hardware and EM channel responses;

Constant Modulus (CM) waveforms.

People

Imperial College London

Dr Bruno Clerckx

Dr Wei Dai

Prof Kin Leung

 

University College London

Prof Hugh Griffiths

Dr Matthew Ritchie

Dr Christos Masouros

 

Partners

IBM Research

University of Kansas

Fraunhofer Institute of Communications

Consortium Members
Consortium Members