Signal Procssing in the Information Age

Lead Research Organisation: University of Edinburgh
Department Name: Sch of Engineering


Persistent real-time, multi-sensor, multi-modal surveillance capabilities will be at the core of the future operating environment for the Ministry of Defence; such techniques will also be a core technology in modern society. In addition to traditional physics-based sensors, such as radar, sonar, and electro-optic, 'human sensors', e.g. from phones, analyst reports, social media, will provide new valuable signals and information that could advance situational awareness, information superiority, and autonomy. Transforming and processing this broad range of data into actionable information that meets these requirements presents many new challenges to existing sensor signal processing techniques.

In a future where a large-scale deployment of multi-modal, multi-source sensors will be distributed across a range of environments, new signal processing techniques are required. It is therefore timely to consider the fundamental questions of scalability, adaptability, and resource management of multi-source data, when dealing with data that is high-volume, high-velocity, from non-traditional sources, and with high uncertainty.

The UDRC Phase 3 project, Signal Processing in an Information Age is an ambitious initiative that brings together internationally leading experts from 5 leading centres for signal processing, data science and machine learning with 10 industry partners. Led by the Institute of Digital Communications at the University of Edinburgh, in collaboration with the School of Informatics at Edinburgh, Heriot-Watt University, University of Strathclyde and Queen's University Belfast. This multi-disciplinary consortium brings together unique expertise in sensing, processing and machine learning from across these research centres. The consortium has been involved in defence signal processing research through the UDRC phases 1 & 2, the MOD's Centre for Defence Enterprise, and the US Office of Naval Research. The team have significant experience in technology transfer, including: tracking and surveillance (Dstl), advanced radar processing (Leonardo, SEA); broadband beamforming (Thales); automotive Lidar and radar systems (ST Microelectronics, Jaguar Land Rover), and deep learning face recognition for security (AnyVision).

This project will investigate fundamental mathematical signal and data processing techniques that will underpin future technologies required in the future operating environment. We will develop the underpinning inference algorithms to provide actionable information, that are computationally efficient, scalable, and multi-dimensional, and incorporate non-conventional and heterogeneous information sources. We will investigate multi-objective resource management of dynamic sensor networks that include both physical and human sensors. We will also use powerful machine learning techniques, including deep learning, to enable faster and robust learning of new tasks, anomalies, threats, and opportunities, relevant to operational security.

Planned Impact

It has long been recognized that information superiority is a key goal in any conflict, and thus the Ministry of Defence aspires to a future capability of persistent real-time, multi-sensor, multi-modal sensing. Furthermore, in future operations, physical sensors will be augmented with non-physical sources of information, including, 'human sensors' and sources from the internet. There will also be a continued growth in the amount and variety of data acquired. Transforming this data into actionable information will help meet the requirements for improved situational awareness, information superiority, and autonomy. However, this necessitates new fundamental signal and information processing techniques that are: scalable, distributed, adaptable, and can simultaneously exploit data from a wide range and variety of sources. The research in this project aims to develop such underpinning techniques, hence providing an important operational advantage to our armed forces.

The primary beneficiaries of this research naturally include the stakeholders in defence sensing and information processing, from industry and government to the end users in the armed forces. As the proposed research aims to work closely with the UK defence industries there is also likely to be a significant economic benefit. A successful project will translate into greater technology pull-through of the research into the commercial sector and will help UK defence companies remain at the leading edge in the international defence market.

Furthermore, many of the defence challenges to be addressed in this project can also be found in different guises in new emerging technologies in modern society, such as: autonomous vehicles, smart cities and other highly sensorized environments. The proposed research is therefore likely to impact these broader domains, from robotics and security to medicine.


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Title Detecting LFM Parameters in Joint Communications and Radar Frequency Bands 
Description As the traditional radar waveform, linear frequency modulation (LFM) is widely used in military applications to detect targets. Recently, civilian applications such as internet of vehicle and unmanned aerial vehicle also apply LFM waveform to sense the nearby surroundings information. However, this complicated environment usually contain other waveforms, which may adversely influence LFM signal. Thus, there has been increasing interest in using the same radio spectrum to enable the radar and communication signals to coexist. In this poster, we select the orthogonal frequency division multiplexing (OFDM) signal as the communication waveform and discuss how to detect LFM parameters under communication and radar spectrum sharing scenarios. Firstly, the traditional method, the discrete chirp Fourier transform (DCFT), is applied in this scenario to estimated LFM parameters. Secondly, the alternative approach, the Hough transform, is proposed by considering the intrinsic feature of OFDM receivers. Through simulations, we demonstrate the DCFT method and the use of the Hough transform to confirm that these can be identified to a high degree of accuracy. 
Type Of Art Image 
Year Produced 2022 
Description This project is ongoing but has already achieved the following outcomes, published in the research literature:
(1) novel approximate Bayesian processing methods for dynamic low photon LIDAR imaging;
(2) new message passing algorithms for spectral separation, and sensor registration and tracking;
(3) new results in the framework of polynomial matrix algebra and its application to broadband beamforming;
(4) new Integrated framework with design choices for both low-level hardware/software, and high-level algorithmic approximations;
(5) Novel information-theoretic results relating to control of information flow when compressing data with multiple sources
(6) new Deep Learning methods for anomaly detection;
(7) Optimising Neural Network Architectures for Provable Adversarial Robustness
(8) learning solutions for transferring the knowledge from RGB image domain to other domains such as infra-red.
Exploitation Route Through technology transfer to our UDRC industrial partners, or through Dstl
Sectors Aerospace, Defence and Marine,Government, Democracy and Justice,Security and Diplomacy

Description Research Themed meetings 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Industry/Business
Results and Impact The UDRC run bi-annual research themed meetings for the defence industry and government on topics related to signal processing for defence.
We have run UDRC Themed meetings for both grants "Signal Processing in a networked Battlespace" and Signal Processing in the Information age".
For the grant Signal Processing in a networked Battlespace, we have delivered the following themed meetings:
• Source separation and sparsity
• Network and Information Sciences International Technology Alliance
• Autonomous systems and signal processing
• Hardware and implementation
• Image and video processing
• MIMO and radar signal processing
• Uncertainty and anomaly detection
• Space surveillance and tracking
• Underwater sensing, signal processing and communications
• Data science and signal processing (with Alan Turing Institute)
For the grant Signal Processing in the Information Age we have delivered the following themed meetings:
1. Scalable Signal Processing with Bayesian Graphical Models and was held on 20th February 2019. 40 people attended and there was a good mix of academia and defence industry (50:50 split).
2. Deep Learning and Defence was held on 14th November 2019. 70 people attended and there was a good mix of academia and defence industry (50:50 split).
3. UDRC Themed Meeting on Imaging through Obscure Media on 22nd July 2020 - online and 66 attendees (50:50 split).
4. UDRC Themed Meeting on Electromagnetic Environment on 25th November 2020 - - online and 110 attendees (50:50 split).
5. UDRC Themed meeting on Underwater Signal Processing on 25th March 2021 - online and 140 attendees (50:50 split).
6. UDRC Themed meeting on Autonomous Systems on 24 November 2021 - Heriot-Watt University and online and 90 attendees (50:50 split).
6. UDRC Themed meeting on Multiple Object Tracking and Decentralised Processing on 14th January 2022 - online and 90 attendees (50:50 split).
Year(s) Of Engagement Activity 2013,2014,2015,2016,2017,2018,2019,2020,2021,2022
Description Sensor Signal Processing for Defence Conference (SSPD) 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Industry/Business
Results and Impact The SSPD is held annually and organised across the two grants listed. The 2019 event was held in Brighton on the 9th and 10th May and we had 122 people attending. Al Hero from the University of Michigan and Andy Bell from Dstl were the keynote speakers. We also had a number of invited Speakers: Daniele Faccio, University of Glasgow; Simon Maskell, University of Liverpool; and, Peter Willet, University of Connecticut. As well as this we had our Industrial and Military Sessions. 29 Papers were submitted to the conference and 23 papers were accepted. We secured both IEEE signal Processing Society and IEEE Aerospace and Electronic systems Society Technical sponsorship. All the presentations and posters can be found on the SSPD website and the papers are now published in IEEE XPlore.
SSPD2020 was an online conference and attracted 153 attendees. Keynote speakers were: Vivek Goyal, University of Boston; Daniel D. Sternlicht, Naval Surface Warfare Center Panama CityInvites speakers were Paul Thomas, Dstl; Athina Petropulu, Rutgers University; Sean Gong, Queen Mary University of London; Paul White, University of Southampton.
Year(s) Of Engagement Activity 2013,2014,2015,2016,2017,2018,2019,2020,2021
Description UDRC Summer School 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Industry/Business
Results and Impact The Summer school is organised under the two grants listed. The last Summer School took place 24th - 27th June 2019 at Heriot-Watt University. There were 74 people attending with a 50:50 split between industry and academia with 29 different organisations attending with applicants from the UK, Germany, Kazakhstan, France Netherlands, Sweden and Norway registered over the four days. We have also successfully applied for EURASIP funding. Topics were taught:
• Statistical Signal Processing
• Sensing and Tracking
• Machine Learning
• Source Separation and Beamforming
People who attended the Summer school fed back that they really enjoyed it. A few of their comments are below:
• Really enjoyed each day; the lectures were all well suited to my level of experience
• High level of content, informing attendees of concepts and state-of-the-art
Year(s) Of Engagement Activity 2013,2014,2015,2016,2017,2019,2021