Picture of boy being examining by doctor at a tuberculosis sanatorium

Understanding our future through Open Access research about our past...

Strathprints makes available scholarly Open Access content by researchers in the Centre for the Social History of Health & Healthcare (CSHHH), based within the School of Humanities, and considered Scotland's leading centre for the history of health and medicine.

Research at CSHHH explores the modern world since 1800 in locations as diverse as the UK, Asia, Africa, North America, and Europe. Areas of specialism include contraception and sexuality; family health and medical services; occupational health and medicine; disability; the history of psychiatry; conflict and warfare; and, drugs, pharmaceuticals and intoxicants.

Explore the Open Access research of the Centre for the Social History of Health and Healthcare. Or explore all of Strathclyde's Open Access research...

Image: Heart of England NHS Foundation Trust. Wellcome Collection - CC-BY.

System framework for autonomous data processing onboard next generation of nanosatellite

Greenland, Steve and Ireland, Murray and Kobayashi, Chisato and Mendham, Peter and White, David and Crowther, Bill and Kabbabe, Khris and Post, Mark (2017) System framework for autonomous data processing onboard next generation of nanosatellite. In: 15th Reinventing Space Conference, 2017-10-24 - 2017-10-26, University of Strathclyde Technology & Innovation Centre.

Text (Greenland-etal-RSC-2017-System-framework-for-autonomous-data-processing-onboard)
Accepted Author Manuscript

Download (611kB) | Preview


Progress within nanosatellite systems development makes niche commercial Earth observing missions feasible; however, despite advances in demonstrated data rates, these systems will remain downlink limited able to capture more data than can be returned to the ground cost-effectively in traditional raw or near-raw forms. The embedding of existing ground-based image processing algorithms into onboard systems is non-trivial especially in limited resource nanosatellites, necessitating new approaches. In addition, mission opportunities for systems beyond Earth orbit present additional challenges around relay availability and bandwidth, and delay-tolerance, leading to more autonomous approaches. This paper describes a framework for implementing autonomous data processing onboard resource-constrained nanosatellites, covering data selection, reduction, prioritization and distribution. The framework is based on high level requirements and aligned to existing off-the-shelf software and international standards. It is intended to target low-resource algorithms developed in other sectors including autonomous vehicles and commercial machine learning. Techniques such as deep learning and heuristic code optimization have been identified as both value-adding to the use cases studied and technically feasible. With the framework in place, work is now progressing within the consortium under UKSA Centre for Earth Observation and Instrument funding to deliver an initial prototype data chain implemented within a representative FPGA-based flight computer system.