Data reduction and image formation for future radio telescopes (DRIFT)Themes: Signal processing for communication
This project aims (A) to reduce the transportation bottleneck by time-domain compressive sampling techniques, allowing the recovery of full correlation data from significantly subsampled antenna signals, and (B) to introduce advanced algebraic techniques to speed up the image formation. Ideally, we would even skip the intermediate covariance reconstruction.
The project is carried out in context of the ASTRON-IBM DOME project and is part of the NWO "Big Bang, Big Data" program.
- Compressive covariance sampling: the objective is to enable non-uniform time-domain sampling at the stations, and thereby reduce the amount of sampled and transported data. This will result in a flexible sampling scheme with a trade-off between data rate (power consumption) and total integration time.
- Image formation: the objective is to derive new algebraic algorithms that are more efficient than existing algorithms, produce accurate wide-field images, allow flexible implementation of most currently proposed CS-based imaging schemes, and enable massively parallel processing.
For more information, see the project homepage.