Ongoing Call for Data
The use of data gathered in real-world situations presents opportunities and challenges for researchers in signal processing for defence. Well-calibrated data which includes ground truth remains key to successful research, development and exploitation and we are always on the look-out for new data challenges.
In particular we have identified data shortfalls in:
· Sensor fusion data
· Tracking data
· Hyperspectral data
· Inverse Synthetic Aperture Radar (ISAR) data.
· Mixed mode data
If you have data sets that could be hosted on the UDRC website to help further defence signal processing research we would be delighted to hear from you.
Please contact janet.forbes@ed.ac.uk for further details.
Phase 3 Data Centre
- Automatic Target Detection for 3D for Ground Penetrating Radar Data Challenge presentation: Contact janet.forbes@ed.ac.uk to request data
- Multiple Signal Tracking using a Simulated Passive Sonar Array in Real-time or Less: Download Data Zip file
-
RarePlane, an open-source machine learning dataset from CosmiQ Works and AI.Reverie that incorporates both real and synthetically generated satellite imagery
-
SAR-Ship Dataset, an open-source dataset for ship detection using SAR images
Phase 2 Data Centre
- US Air force Sensor Data Management System (GOTCHA data)
- Dstl Motion Imagery Challenge Data (published 2016)
- AFRL GOTCHA GMTI challenge data
- Matlab Demo Code for Learning Sparsifying Transforms on Image Data
- Demo for Sensor management with time, energy and communication constraints
- Sensor management with time, energy and communication constraints: (Authors: Cristian Rusu, John Thompson and Neil M. Robertson)
- Iterative Image Formation using Fast (Re/Back)-projection for Spotlight-mode SAR
- Themed meeting on space surveillance data challenge
Please find Phase 1 Data (2009 - 2013) from the UDRC available for you to download:
- Thermal Imaging Videos
- Hydravision Data Catalogue
- Imperial College SDR Array Testbed Dataset
- Imperial College Wide Aperture Array Test bed Dataset
- Maximum Chirplet Transform and Multiple Chirp Detection (published 2016)
