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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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16 Dec 2010 Room 611, EEE Building, Imperial College LondonThis was a joint “Super-resolution Source Separation” and “Non-Conventional Signals” Themes meeting at Imperial College London, EEE Building (Room 610) on 16 December 2010.
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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 "Non-conventional Signals" Theme Meeting took place at Imperial College on 19 May 2010
The capability to generate spatially and temporally correlated non-stationary noise is becoming an increasingly important requirement for the assessment of advanced signal processing algorithms in the laboratory, for examination of usefulness of virtual sensor and for factory acceptance tests (FATS) of equipment. In the past, common methods of performing laboratory simulations utilised either measured data and/or spatially uncorrelated Gaussian noise. However, each of these methods have advantages and disadvantages for their use in signal processing schemes. For example, whilst white Gaussian, spatially uncorrelated noise is simple to generate, it suffers from a lack of realism. Measured noise includes realism, but it has other disadvantages such as; the potential for unknown events to occur; the inability to perform many numbers of tests and it is usually expensive to obtain. Therefore, there is a need for an intermediate stage of synthetic noise generation which retains the advantages of uncorrelated noise and real data whilst removing some of the disadvantages. This requirement is becoming more significant since signal processing schemes are becoming increasingly sophisticated and the emphasis is moving away from collecting real data due to cost/risk; sometimes real data is impossible to collect. And synthetic noise is even more important. This proposal is aimed at the requirement to produce synthetic non-stationary spatial/temporal noise.
Project Supervisor
Timothy Clarke, Defence Science and Research Laboratory (Dstl). Tim Clarke has a wealth of experience in acoustics, particularly in the underwater domain, having worked in academia, private industry and government research laboratories. One area of Tim’s expertise is the link and exploitation between the environment and signal processing for acoustic systems.
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04 Feb 2010 Room 503, EEE Building, Imperial College LondonThe 2nd "Non-conventional Signals" Theme Meeting took place at Imperial College (Room 503) on 4 February 2010
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05 Nov 2009 Imperial College LondonThe 1st "Non-conventional Signals" Theme Meeting took place at Imperial College on 5 November 2009
This project investigates new approaches of modelling for the non-stationary data sets, which are commonly generated in many systems including Radar, Sonar, communications, instrumentation, seismic exploration, speech processing and recognition, etc. Signal processing functions usually perform based on a pre-set model, or the system structure is fixed. Although this provides a simple solution, it is highly inefficient especially for non-stationary systems that are common in practice. This makes it highly desirable to adapt the model so that it can capture the true underlying dynamics and predicts accurately the output for unseen data.
Against this background, however, there is a lack of generic tools/ methodologies to deal with the problem as on how to perform the model structural changes as demanded by the processes. Some nonlinear model structure identification algorithms are too slow for real-time applications. Most current real-time algorithms, on the other hand, are ad hoc rather than principled approaches. Hence the resultant model quality is not optimal from a statistical point of view.
In this programme, we propose to develop a hybrid, flexible yet principled approach for optimum on-line adaptive modelling by means of minimal model structure determination and simultaneous parameter estimation. The aim of the proposal is to introduce a new technique for the adaptive modelling of complex nonlinear dynamical systems in real time and noisy environments.
Project Supervisor
Dr Gong obtained BEng and MEng at University of Electronic Science & Technology of China in 1992 and 1995 respectively, and PhD from National University of Singapore in 2002. From 2002 to 2003, he worked firstly as an engineer, and later a senior engineer, at Institute of Inforcom in Singapore. In 2003, he joined Queen’s University of Belfast as a Research fellow, and later took an engineer post at the Institute of Electronics, Communications and Information Technology (ECIT), QUB in 2005. Since 2006, Dr Gong has been with University of Reading as lecturer. His research interests include communications, signal processing, adaptive filtering etc.
The project will the address the UDRC challenge: "To distinguish between man-made echo-sounding pulses and those made by marine mammals (cetaceans)"
Ever since the early days of passive sonar (receive only) there has been a need to determine whether sounds in the ocean emanate from the native marine life or from mechanical sources which may turn out to be contacts of interest. Furthermore as modern Navies aspire to be ever more responsible environmental custodians of the underwater environment, it is necessary to have a situational awareness of local biological activity before any active sonar (transmit and receive) transmission. The idea is to develop frequency tracking schemes tailored to the underwater environment with specific regard to the tracking of cetacean tonal vocalisations. The approach will be block based and take advantage of the fractional Fourier transform to feed the particle filter with estimates of both centre frequency and chirp-rate. The Matlab based algorithms will be benchmarked against real datasets and the current best of breed.
Project Supervisor
Dstl Naval Systems, Portsdown west, Portsmouth, PO17 6AD. Tel: 023 9253 2668,
This research will provide a novel theoretical framework and enhanced practical solutions for adaptive processing (supervised and blind) of noncircular complex signals. Standard, widely used, solutions inherently assume second order circularity of signal distributions, and are therefore inadequate when the signals are observed through nonlinear sensors or as mixtures of sources, when the noise model is not doubly white, and when high resolution and enhanced separability are paramount. This will be achieved based on recent fundamental developments in the statistics of complex variables, called augmented complex statistics. The fundamental novelty in this work is the design of statistical signal processing techniques for the detection, estimation, and quanti cation of the degree of noncircularity of real world signals. This information will be used in conjunction with widely linear adaptive signal processing techniques, to enable optimal processing of complex signals with both circular and noncircular distributions.
The outcomes of this research will have an immediate impact in a variety of applications of signal detection, estimation, and adaptive signal processing, including those in interference supression, direction of arrival estimation, and blind extraction of sources of interest in real world scenarios.
Project Supervisor
Dr. Mandic received the Ph.D. degree in nonlinear adaptive signal processing in 1999 from Imperial College, London, London, U.K. He is now a Reader with the Department of Electrical and Electronic Engineering, Imperial College London, London, U.K. He has previously taught at the Universities of East Anglia, Norwich, Norfolk, U.K., and Banja Luka, Bosnia Herzegovina. He has written over 150 publications on a variety of aspects of signal processing and a research monograph on recurrent neural networks. He has been a Guest Professor at the Catholic University Leuven, Leuven, Belgium and Tokyo University of Agriculture and Technology (TUAT), and Frontier Researcher at the Brain Science Institute RIKEN, Tokyo, Japan. Dr. Mandic has been a Member of the IEEE Signal Processing Society Technical Committee on Machine Learning for Signal Processing, Associate Editor for IEEE Transactions on Circuits and Systems II, and Associate Editor for International Journal of Mathematical Modeling and Algorithms. He has won awards for his papers and for the products coming from his collaboration with industry.
