The Sparse Signal Processing Group, a team of ten researchers at UCD, is focussed on the application of time-frequency/time-scale analysis, sparse signal representations, and finite field theory to solve challenging signal processing problems. The application of the mathematics in these areas is leading to new methods and techniques with applications in source localization and separation, mobile wireless communication systems, and multiaccess communication and RADAR/SONAR systems.

Current research in the SSPG is clustered into three thematic areas:
(1) Sparsity and Source Separation,
(2) Efficient Time-varying Channel Models, and
(3) Signals with Ideal Ambiguity Properties.

The common thread linking these three themes is sparsity in that we leverage sparse representations to perform the source separation, desire sparse (efficient) representations of mobile channels, and design waveforms and families of waveforms with sparse (ideal thumbtack) auto-ambiguity properties.