University Defence Research Centre (UDRC) Research - Old Theme


This theme is concerned with the developing new multi-sensor signal processing algorithms for classification of objects/targets using one or more types of signals/information. The objects/targets may be rigid (e.g. a submarine) or diffused (e.g. convoys of vehicles, crowds of people, dust clouds) and a great deal of information can potentially be gained by analysing, for instance, radar or sonar imaging, visual and/or audio signals, micro-Doppler returns from a target which has been illuminated by electromagnetic energy or acoustic wavelength radiation - which this theme aims to exploit.

Some application areas include: Radar, Sonar, Person tracking, Robot Audition.


This theme is concerned with the investigation, analysis and design of distributed signal processing algorithms for detecting and locating single or multiple targets/sources using a wireless sensor network with constrained resources (e.g. minimum power, cooling requirements and cost of communication between sensors). Amongst other approaches, this involves the development of cooperative, temporal and spatial adaptive distributed and fusion algorithms.

Some application areas include: Persistent surveillance of complex environments using distributed signal processing techniques.


This theme is concerned with multi-sensor signal processing algorithms that are capable of operating in the presence of non-"standard" signals/noise. Such signals may be non-stationary, non-Gaussian and/or non-circular, spatially and temporarily distributed with various degrees of correlation. Detection, reception, signal extraction and modelling/simulation of such non-conventional signals is one of the main aims of this theme.


This theme is focusing on super-resolution detection and localisation/separation of sources (emitters/targets/signals) located close together in space (angle and/or range) using a multi-sensor system.

The resolution performance of such a system is, in general, a function of its aperture and number of sensors, N. For instance, the larger the multi-sensor aperture the better the resolution performance. In practice, these resources are limited and so the purpose of a super-resolution multi-sensor signal processing algorithm is to achieve high resolution performance without increasing N or the multi-sensor aperture.

Algorithms which belong to this category:

  • have, asymptotically, infinite resolving capability - as the observation interval (i.e. number of snapshots) tends to infinity.
  • suffer from the presence of system and/or modelling "uncertainties" - thus, eliminating the uncertainties (calibration) is an important problem for the operation of these algorithms.

Application areas include: Radar Signal Processing (e.g. SAR, MIMO), Acoustic Signal Processing, Single and Multi-Beam Array Signal Processing.