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04 May 2011 EEE Building, Imperial College LondonThe 6th UDRC Theme Meeting and Industry Day took place at Imperial College on 4 May 2011. The main purpose of this event was to provide opportunities to colleagues from industry to understand, interact and collaborate with researchers working on various UDRC projects. The goal is to exploit the practical use of our research results by the industry.
The project presentations from this event can be found at http://www.mod-udrc.org/announcement/328.
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15 Dec 2010 Room 503, EEE Building, Imperial College LondonThis was a joint “Classification and Multimodal Processing” and “Distributed Signal Processing” Themes meeting at Imperial College London, EEE Building (Room 503) on 15 December 2010.
Project O14 didn't attend
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30 Sep 2010 Room 403, EEE Building, Imperial College LondonThe purpose of this theme meeting was to give to the UDRC researchers an insight into what problems the military are working on and to get them thinking about these problems and how to tackle them. More specifically, Dstl presented two MOD research challenges and let people self-select into groups and go off to two rooms to discuss approaches, then reconvene and present their solutions back to the audience.
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19 May 2010 Room 611, EEE, Imperial College LondonThe 3rd "Distributed Signal Processing" Theme Meeting took place at Imperial College on 19 May 2010
This research programme will investigate and develop new distributed multi-target multi-source detection (DMMD) and tracking algorithms for sensor networks with constrained communication resources. Current approaches to DMMD have generalised distributed data fusion (DDF) algorithms by combining them with multiple hypothesis tracking (MHT) algorithms. However, the approximations inherent in MHT can lead to an unacceptable degradation in tracking performance. To overcome this difficulty, we propose to develop a new DMMD algorithm that builds upon Finite Set Statistics (FISST) and Exponential Mixture Densities (EMD). FISST provides a rigorous and numerical tractable model that unifies the problems of multi-object multi-sensor detection, classification and estimation. EMD is a suboptimal algorithm for fusing estimates when their marginal distributions are known but their joint distribution is not. It can be used to fuse estimates in fusion networks where the network topology is arbitrary, unknown and time varying.
There will be two main outcomes from this research programme:
First, we shall create an extremely general and generic mathematical framework within which a range of non-linear filtering algorithms can be deployed. Second, we shall develop implementations that, we believe, will show significant advantages over existing approaches in their ability to deal with high false alarm rates and data association ambiguity. We shall also strive for computational efficiency and practical applicability. The successful extension to distributed environments could have widespread applicability due to their simplicity to implement and low complexity.
This programme is in response to the Detection requirement and Challenge Number 13 of the EPSRC-DSTL call: "To develop general algorithms for distributed signal fusion in a network of sensors."
Project Supervisor
Dr Daniel Clark is a Lecturer at Heriot-Watt University in the newly formed Joint Research Initiative on Signal and Image Processing within the Edinburgh Research Partnership. In 2008 he was awarded the highly prestigious Royal Academy of Engineering/ EPSRC Research Fellowship to work on “Random Set Filtering Techniques for Multi-Sensor Multi-Object Tracking and Data Fusion”. His work has involved the derivation of practical algorithms for multi-object tracking, establishing their theoretical robustness, and deployed them on autonomous underwater vehicles for oil pipeline tracking. He was awarded his PhD entitled “Multiple Target Tracking with the Probability Hypothesis Density Filter” at Heriot-Watt University in 2006. Dr Clark has an EPSRC CASE PhD studentship in collaboration with SELEX SA&S to investigate collective multiobject filtering techniques for tracking groups of targets. His work has been published in the most highly cited journals in the field of statistical signal processing, including IEEE Transactions and IEE Proceedings. He serves as a reviewer for the IEEE Transactions on Signal Processing, IEEE Transactions on Aerospace and Electronic Systems, IEEE Signal Processing Letters and EURASIP Signal Processing.
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04 Feb 2010 Room 503, EEE Building, Imperial College LondonThe 2nd "Distributed Signal Processing" Theme Meeting took place at Imperial College (Room 503) on 4 February 2010
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05 Nov 2009 Imperial College LondonThis is the first meeting of the Distributed Signal Processing Theme
Critical locational information has long been used in a variety of military settings. In many of these settings, it would be advantageous if information concerning the spatial locations of particular entities and/or events could be conveyed. Localisation requirement has also become more pervasive and has found many strategic applications to the ground forces. For instance, the troops' safety can be directly linked to the degree of location-awareness of the troops and their enemies in a battlefield. For non-military applications, the first need to track a mobile user appeared as an essential public safety
feature from the order issued by the Federal Communications Commission (FCC) in the US in 1996, which mandated all wireless service providers to deliver accurate location of an emergency 911 (E-911) caller to public safety answering points. Since then, various location-based services (LBSs) have
emerged in the market, from identifying the whereabouts of a friend or employee to personalised LBS such as discovering the nearest cash machine. It is estimated that LBSs will generate annual revenues of the order of £10 billions worldwide.
Providing a useful localisation will require, in some cases, metre-perfect resolution to be achieved over air. Yet, the fundamental physical challenges such as channel fading, low signal-to-noise ratios (SNRs), multiuser interference, and multipath conditions have put obstacles on meeting the objective. Our vision of next-generation (xG) localisation systems will be the provision of dynamic, distributed, robust and high-resolution LBSs. The realisation of these xG LBSs will require advanced signal processing and
intelligence at the nodes to resolve the problem of interference and to detect the presence of a direct line-of-sight (LoS) for reliable ranging and localisation. Advanced multi-antenna technologies such as multiple-input multiple-output (MIMO) are expected to be adopted at mobile terminals for providing enhanced locational information as well as mitigating the multipath and multiuser interference.
To achieve the needed LBSs, this project proposes to investigate the use of mobile user cooperation for localisation. The novelty of user or node cooperation lies in that nodes can work collaboratively by proper relaying to mitigate the multipath interference that can help identify the LoS for ranging in the presence of delay paths. The cooperation can, more importantly, exchange locational information from one node to another so that location ambiguity due to the lack of LoS signal paths could be removed and higher resolution can also be achieved. Another novelty of this proposal is the use of hypothesis testing based machine learning for the detection of LoS, which will be integrated with the cooperative signal processing for wireless localisation. This exceptionally challenging objective also has the potential to redefine the architecture of wireless networks, provide a novel system solution for organising the access of users to the system resources in this cooperative and self-regulating architecture, and revolutionise key areas of
the 21st century ICT.
Project Supervisor
Kai-Kit Wong received the BEng, the MPhil, and the PhD degrees, all in Electrical and Electronic Engineering, from the Hong Kong University of Science and Technology, Hong Kong, in 1996, 1998, and 2001, respectively. After graduation, he joined the Department of Electrical and Electronic Engineering, the University of Hong Kong as a Research Assistant Professor. From July 2003 to December 2003, he visited the Wireless Communications Research Department of Lucent Technologies, Bell-Labs, Holmdel, NJ, U.S., where he was a Visiting Research Scholar studying optimization in broadcast MIMO channels. After that, he then joined the Smart Antennas Research Group of Stanford University as a Visiting Assistant Professor conducting research on overloaded MIMO signal processing. From 2005 to August 2006, he was with the Department of Engineering, the University of Hull, U.K., as a Communications Lecturer. Since August 2006, he has been with the Department of Electrical and Electronic Engineering, University College London where he is a Senior Lecturer. Dr Wong won the IEEE Vehicular Technology Society Japan Chapter Award of the International IEEE Vehicular Technology Conference-Spring in 2000, and was also a co-recipient of the First Prize Paper Award in the IEEE Signal Processing Society Postgraduate Forum Hong Kong Chapter in 2004. In 2002 and 2003, he received, respectively, the SY King Fellowships and the WS Leung Fellowships from the University of Hong Kong. Also, he was awarded the Competitive Earmarked Research Grant Merit and Incentive Awards in 2003-2004. Recently, he also coauthored two papers that won the best paper awards for the International Conference on Wireless Communications and Signal Processing, 2009. Dr Wong is a Senior Member of IEEE and is also on the editorial board of IEEE Transactions on Wireless Communications, IEEE Communications Letters, IEEE Signal Processing Letters, and IET Communications. His current research interests center around cognitive radio, cooperative wireless networks, cross-layer optimisation, information theory and optimisation, multiuser communications theory, performance analysis of MIMO channels and secrecy capacity of wireless channels.
Visual analysis by human operators or service personnel is widely acknowledged to benefit from a fused representation, where images or video information from different spectral bands are combined into a single representation. To provide maximum utility fused data, or its constituent components, must be delivered in a timely manner, must facilitate simple and flexible processing and must be robust to loss and network congestion.
Non infrastructure-based Mobile Ad-Hoc Networks are emerging as suitable platforms for exchanging and fusing real-time multi-sensor content. Such networks are characterised by the highly dynamic behaviour of the transmission routes and high path outage probabilities. They exemplify the type of complex, heterogeneous end-end transmission environments which will be commonly encountered in future military scenarios. The low-bandwidth, noisy nature of the physical channel in many sensor networks represents the most serious challenge to implementation of the digital battlefield of the future. One of the key challenges in such complex networking environments is the need to reliably transport and fuse real time video. Video is acknowledged to be inherently difficult to transmit and this is compounded by the need to support multiple sources to aid fusion and situational awareness while maintaining data security.
We will focus our work on embedded video bitstreams (MPEG-4 (SVC) which offer scalability and enhanced flexibility for adaptation to varying channel types, interference levels, network structures and content types. These mitigate the need for highly inefficient video transrating processes and instead present a more tractable requirement in the form of dynamic bitstream management.
A multisource approach to streaming is proposed which will support video fusion in a bandwidth-efficient manner while having the potential to significantly increase the robustness of real-time transmission in complex heterogeneous networks. Source coding and fusion will be based on the concept of scalability using an embedded bitstream. This means that the source need only be encoded once and that the coded representation can be truncated to support multiple diverse terminal types and to provide inherent congestion management without feedback. Such a system must be designed to maintain optimum fusion performance and hence intelligibility in the presence of bitstream truncation.
The potential advantages of this scheme include:
- A joint framework for scalable fusion and compression supporting both lossless and lossy representations.
- Flexibility for optimisation depending on content type and application.
- Graceful degradation: the capability of the fused video bitstream to adapt to differing terminal types and dynamic network conditions
- Error resilience: the structure of the code stream can aid subsequent error correction systems alleviating catastrophic decoding failures.
- Secure delivery: the ability to design encryption schemes which support truncation.
- Region-of-Interest coding: supporting definition of ROIs for priority transmission.
Project Supervisor
Professor David Bull PhD, FIET, SMIEEE, CEng holds the Chair in Signal Processing at the University of Bristol. His previous roles include: Lecturer at the University of Wales (Cardiff) and Systems Engineer for Rolls Royce. He joined Bristol in 1992 and established its Signal Processing Research Group in 1994. He was Head of the Electrical and Electronic Engineering Department between 2001 and 2006 and is now Director of the Bristol Vision Institute (BVI). In 2001 he co-founded ProVision Communication Technologies Ltd. where he is now Chairman. David has worked widely in the fields of 1 and 2-D signal processing. He has won two IEE Premium awards for this work and has published numerous patents, several of which have been licensed and exploited commercially. His current activities are focused on the problems of image and video communications and analysis for low bit rate wireless, internet, military, consumer and broadcast applications. In particular he has worked on content-based video coding, error resilient source coding, linear and non-linear filterbanks, motion estimation, image and video fusion, architectural optimisation (for filters, transforms and wavelet filterbanks) and content description for video archiving. He is widely supported in these areas by both industry, Europe, MoD and EPSRC and has generated over £8M of research income in the past 10 years. He has contributed to several European Union projects including WINHOME, TRUST, SCOUT, MEDIANET, WCAM and ASTALS. He has published over 350 papers, various articles and 2 books and has also given numerous invited/keynote lectures and tutorials. In 1996 David helped to establish the UK DTI Virtual Centre of Excellence in Digital Broadcasting and Multimedia Technology and was one of its Directors from 1997-2000. He was also a founder member of the UK’s Defence Technology Centre in Data and Information Fusion, serving on its Science and Technology Board for 2 years. He was appointed as an independent member of UK Government’s Defence Scientific Advisory Council (DSAC) in 2002, contributing to the UK(MoD)-US(DoD) Working Group on Persistent Surveillance (2004). He has also advised Government through membership of the UK Foresight Panel and through the DTI/EPSRC steering Group on Digital Broadcasting and Multimedia Technology. Throughout his career, David has acted as a consultant to many companies including QinetiQ, BAe Systems, General Dynamics, MBDA, BSkyB, Sony, Group4 Security and the Metropolitan Police. He is also a member of the Steering Committee for the UK Technology Strategy Board’s Special Interest Group on Imaging.
non-hierarchical - so the network is robust to the removal and addition of sensors; globally convergent - so solutions are as accurate as those that would be obtained if each node had direct access to all the information across the network.
Project Supervisor
Bernard Mulgrew currently holds the SELEX Galileo/Royal Academy of Engineering Research Chair in Signal Processing and is Head of the Institute for Digital Communications at the University of Edinburgh. His research interests are in adaptive signal processing and estimation theory and in their application to communications, radar and audio systems. He is a co-author of three books on signal processing and over 70 journal papers. He is a Fellow of the Royal Academy of Engineering, a Fellow of the Royal Society of Edinburgh and a Fellow of the IET.
