This work is broken down into 5 work packages:
- L_WP1: Automated Statistical Anomaly Detection and Classification in High Dimensions for the Networked Battlespace
- L_WP2: Handling Uncertainty and Incorporating Domain Knowledge
- L_WP3: Signal Separation and Broadband Distributed Beamforming
- L_WP4: MIMO and Distributed sensing
- L_WP5: Low Complexity Algorithms and Efficient Implementation
White Papers
- Adaptive Bayesian Sparse Representation for Underwater Acoustic Signal De-Noising
- Automatic Target Detection in Videos Using Incongruence
- Embedding Cognition within Multi-Static Radars
- Enabling Distributed Radar
- Ontology-based Framework for Video-Based Risk Assessment in Road Scenes
- Polynomial Eigenvalue Decomposition (PEVD) Algorithms for Broadband Sensor Array Processing
- Reducing Uncertainty by Incorporating Domain Knowledge Using a Bayesian Framework
- Statistical Anomaly Detection in Communication Networks
Research Summary
The LSSCN consortium comprises five internationally recognised signal processing groups that include:
- Advanced Signal Processing Group (ASPG), Loughborough University [LU]
- Centre for Vision Speech & Signal Processing (CVSSP), University of Surrey [SU]
- Centre for Excellence in Signal & Image Processing (CeSIP), University of Strathclyde [ST]
- Centre of Digital Signal Processing (CDSP), Cardiff University [CU]
- Communications, Sensors, Signal & Information Processing Research Group (ComS2IP), Newcastle University
The following industrial project partners are integrated into the LSSCN consortium research workplan:
- QinetiQ
- Thales
- Selex ES
- PrismTech
- Steepest Ascent
- Texas Instruments
This provides a unique capability from across the UK in the field of signal processing.
The Dstl identified the following ten research themes:
- T1: Weak signal detection in high volume of clutter
- T2: Signal processing in high dimension feature space
- T3: Signal processing in high uncertainty
- T4: Signal processing for sparse or fleeting signals
- T5: Signal processing to support sparse sampling of highly non-stationary signals
- T6: Extraction/separation of multiple overlapping/interwoven signals
- T7: Statistical anomaly detection
- T8: Distributed/decentralised signal processing
- T9: Algorithms to support dramatic reduction in computation
- T10: Accreditable machine learning or data-driven techniques
Our LSSCN consortium carefully designed a coherent programme of work on the basis of the following five strongly interlinked work packages (WPs) above.
The following connectivity matrix illustrates how the designed WPs map to the ten research themes:
| Theme: | [T1] | [T2] | [T3] | [T4] | [T5] | [T6] | [T7] | [T8] | [T9] | [T10] |
|---|---|---|---|---|---|---|---|---|---|---|
| AD | X | X | X | X | X | |||||
| HU | X | X | X | |||||||
| SS | X | X | X | X | X | X | X | |||
| MDS | X | X | X | X | X | |||||
| EI | X | X | X | X | X | X | X | X | X | X |
The LSSCN consortium is underpinned by an integrated approach to research where all the five universities and industrial project partners work seamlessly together building on their track record of successful collaboration which has already generated world-class outputs and commercial products.
