Space Domain Awareness

What is SDA?

Resident space objects (RSO) are man-made objects that orbit the Earth. These include satellites, spacecraft and other space installations. Another class of RSOs are junk objects or debris that form as byproduct of space launch, space flight, or spacecraft malfunction/destruction. Currently, there are more than 23,000 space debris of size as small as 5 cm. However, it is known that there are smaller objects that exist in even greater numbers. Space domain awareness (SDA) is the capability to detect, track, and predict the motion of all RSOs, including space debris.

Graphic from ESA plotting known RSOs as of 2013.

Why is SDA important?

Virtually all aspects of modern life depend on satellite technology, including communications, media, commerce, navigation, agriculture and meteorology. There are more than a thousand operating satellites in orbit, amounting to Trillions of dollars worth of investments, that underpin modern technology. Protecting these assets from interference and destruction is of utmost importance. Developing SDA is critically important to prevent the loss of space assets via unintended collisions.

The worsening pollution of space by junk and debris makes the prospect of widespread space asset destruction realistic. Regular collisions involving active satellites and space junk occur; in fact, one to two operating satellites are lost per year due to collisions. More recently, the increasing militarisation of space, which involves covertly placing weapons in orbit and the potential usage of space itself as a battlefield, makes it vitally important to monitor space utilisation.

Our work on SDA

In collaboration with Inovor Technologies and DST Group, we are developing a space-based space surveillance (SBSS) system to achieve SDA. The idea is to deploy a network of CubeSats that collectively observe and near-Earth orbit using optical sensors (cameras). The CubeSats will be placed in Low Earth Orbit (LEO) (altitudes of 500–700 km). Unlike most imaging satellites which fixate on the Earth’s surface, the SDA CubeSats will be pointing outwards. This allows the CubeSats to clearly observe up to the farthest orbital belts, in particular, the Geosynchronous Orbit (GEO) that contains the most critical space assets (satellites for communications, broadcasting, intelligence, etc.).

SSA CubeSat concept of operations (diagram not to scale).

Surveillance of GEO

The concept of operations involve each CubeSat orienting itself (by means of an attitude determination and control system or ADCS) to “lock on” a predefined region in GEO while in orbit. Any RSO that passes through the CubeSat field of view (FOV) can thus be detected. Fixating on a region in GEO while imaging causes stars to be imaged as streaks, and RSOs in the surveiled GEO region to be observed as dots.

Imaging model for RSO detection in GEO.

Surveillance of LEO

The speed of RSOs in LEO relative to the SBSS satellites will be much higher, hence they tend to form “streaks” when imaged under long exposures with the optical sensing payload. The detection of LEO objects is typically accomplished by examining the linear signature of the streaks.

Streak from an RSO in LEO.

Based on the above settings, we are investigating the mathematical foundations of optical detection of RSOs in GEO and LEO, and the application of computer vision and machine learning algorithms to perform the detection. In particular, we have developed several track-before-detect (TBD) algorithms that are specialised for optical detection of RSOs in GEO, and deep learning-based techniques for streak finding in LEO surveillance.


Challenge Yourself

With the Advanced Concepts Team (ACT) of the European Space Agency (ESA), we organised an international competition on SDA. The dataset is still available for downloading to support your own algorithm development.

Media Coverage

Relevant Publications

  • D. Liu, B. Chen, T.-J. Chin, and M. Rutten, "Topological Sweep for Multi-Target Detection of Geostationary Space Objects", IEEE Transactions on Signal Processing, Vol 68, pp 5166-5177, 2020. preprint
  • H. N. Do, T.-J. Chin, N. Moretti, M. K. Jah, M. Tetlow. "Robust Foreground Segmentation and Image Registration for Optical Detection of GEO Objects Robust", Advances in Space Research, Volume 64, Issue 3, 1 August 2019, Pages 733-746. DOI
  • S. Bagchi and T.-J. Chin. Event-based Star Tracking via Multiresolution Progressive Hough Transforms. WACV 2020. preprint
  • T.-J. Chin, S. Bagchi, A. Eriksson and A. van Schaik. Star Tracking using an Event Camera. CVPR 2019 Workshop on Event-based Vision and Smart Cameras. preprint
  • T.-J. Chin, H. N. Do, N. Moretti and M. R. Tetlow. "Robust Geometric Algorithms for Space Object Detection", 15th IAA Symposium on Space Debris, IAC 2017.
  • R. Marker, T.-J. Chin, and G. Newsam. Efficient geometric matching with polar bounds for aligning star field images. ACRA 2016. preprint
  • T.-J. Chin and M. R. Tetlow. "Robust Attitude Estimation to Support Space Monitoring Using NanoSatellites", AIAA Space Forum 2014.