Processing of real-world analogue signals measured using a variety of sensors, such as audio microphones, and which propagate in multipath or reverberant environments is fundamental to a variety of applications. Within civilian and domestic settings it is important in applications such as for indoor teleconferencing and hands-free audio enhancement. Within the homeland security and defence sectors it is crucial in a wide variety of fields such as forensics and surveillance, both indoors and outdoors, as well as a number of problems requiring target detection and identification: for example outdoor gunshot detection and sniper localisation in urban environments.
Any signal radiated in a confined space exhibits reverberation, also known as multipath propagation, due to reflections off surrounding obstacles. Despite this being a well understood phenomenon, many existing signal processing technologies fail to explicitly model the multipath response. Blind multipath equalisation can be improved with accurate channel modelling which, in turn, depends on knowledge of the target-position, thereby requiring target tracking. However, many passive target tracking methods suffer from the presence of multipath leading to substantial tracking errors. Target tracking can thus be improved by modelling the effect of, or even equalising, the acoustic reverberation from the observations, thereby allowing identification of the true source from signal reflections. Target tracking and blind multipath equalisation should therefore be solved jointly rather than separately.
The objective of this 12-month research programme is to address the detrimental effect of multipath mitigation by developing algorithms for joint blind channel estimation and passive source localisation of speech sources in an indoor multipath environment. This proposal therefore covers the general UDRC research requirement topics of broadband signal separation, detection, high-resolution localisation, multipath mitigation, and nonstationary processing. In addition to domestic and homeland security applications, the proposed research will lead to solutions and insight for addressing problems arising in underwater acoustics such as propagation induced distortion in Challenge 16, as well as the joint detection and tracking problems of Challenge 14.
Project Supervisor
James Hopgood is a lecturer in the Institute for Digital Communications, within the School of Engineering, at the University of Edinburgh, Scotland. His research interests include nonstationary signal processing, speech and audio signal processing in adverse acoustic environments including blind reverberation and acoustic source localisation, single channel signal separation, medical imaging, and general statistical image processing. James received the M.A., M.Eng. degree in Electrical and Information Sciences in 1997 and a Ph.D. in July 2001 in Statistical Signal Processing, part of Information Engineering, both from the University of Cambridge, England. He was then a Post-Doctoral Research Associate for the year after his Ph.D within the same group, at which point he became a Research Fellow at Queens' College continuing his research in the Signal Processing Laboratory in Cambridge. James joined the University of Edinburgh in April 2004.
