This work is broken down into 6 work packages:
- E_WP1: Sparse Representation and Compressed Sensing
- E_WP2: Distributed Multi-sensor processing
- E_WP3: Unified Detection, Localization, and Classification (DLC) in complex environments
- E_WP4: Context-driven Behaviour Monitoring & Anomaly Detection
- E_WP5: Estimation Framework for Multi-target Detection/Tracking and Sensor Management
- E_WP6: Efficient Computation of Complex Signal Processing Algorithms
- A Scalable Sensor Localisation/Calibration Algorithm Using Objects of Opportunity in Wide Area Surveillance
- A Sparsity Based Raman Spectral Decomposition
- Improving Reliability of Object Detectors
- Towards Accurate and Reliable Detection of Anomalous Objects in Synthetic Aperture Sonar (SAS) Imagery
- Developing Automated Anomaly Detection for Wide Area Surveillance (WAS)
- Exploiting Visual Features for Improved Behaviour Based Target Tracking
- A Low Complexity Sensing System for Ultra Wideband Radar Electronic Surveillance
Sensors have for a long time played a vital role in battle awareness for all our armed forces, ranging from advanced imaging technologies, such as radar and sonar to acoustic and the electronic surveillance. Sensors are the "eyes and ears" of the military providing tactical information and assisting in the identification and assessment of threats. Integral in achieving these goals is signal processing. Indeed, through modern signal processing we have seen the basic radar transformed into a highly sophisticated sensing system with waveform agility and adaptive beam patterns, capable of high resolution imaging, and the detection and discrimination of multiple moving targets.
Today, the modern defence world aspires to a network of interconnected sensors providing persistent and wide area surveillance of scenes of interest. This requires the collection, dissemination and fusion of data from a range of sensors of widely varying complexity and scale - from satellite imaging to mobile phones. In order to achieve such interconnected sensing, and to avoid the dangers of data overload, it is necessary to re-examine the full signal processing chain from sensor to final decision.
The need to reconcile the use of more computationally demanding algorithms and the potential massive increase in data with fundamental resource limitations, both in terms of computation and bandwidth, provides new mathematical and computational challenges. This has led in recent years to the exploration of a number of new techniques, such as, compressed sensing, adaptive sensor management and distributed processing techniques to minimize the amount of data that is acquired or transmitted through the sensor network while maximizing its relevance. While there have been a number of targeted research programs to explore these new ideas, such as the US's "Integrated Sensing and Processing" program and their "Analog to Information" program, this field is still generally in its infancy.
This project will study the processing of multi-sensor systems in a coherent programme of work, from efficient sampling, through distributed data processing and fusion, to efficient implementations. Underpinning all this work, we will investigate the significant issues with implementing complex algorithms on small, lighter and lower power computing platforms. Exemplar challenges will be used throughout the project covering all major sensing domains - Radar/radio frequency, Sonar/acoustics, and electro-optics/infrared - to demonstrate the performance of the innovations we develop.