System framework for autonomous data processing onboard next generation of nanosatellite

Steve Greenland, Murray Ireland, Chisato Kobayashi, Peter Mendham, David White, Bill Crowther, Khris Kabbabe, Mark Post

Research output: Contribution to conferencePaper

Abstract

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.

Conference

Conference15th Reinventing Space Conference
Abbreviated titleRISpace 2017
CountryUnited Kingdom
CityGlasgow
Period24/10/1726/10/17

Fingerprint

Nanosatellites
Earth (planet)
Learning systems
Field programmable gate arrays (FPGA)
Data acquisition
Orbits
Image processing
Computer systems
Availability
Bandwidth
Costs

Keywords

  • CubeSat
  • nanosatellite
  • autonomy
  • onboard data processing
  • NewSpace
  • earth observation

Cite this

Greenland, S., Ireland, M., Kobayashi, C., Mendham, P., White, D., Crowther, B., ... Post, M. (2017). System framework for autonomous data processing onboard next generation of nanosatellite. Paper presented at 15th Reinventing Space Conference, Glasgow, United Kingdom.
Greenland, Steve ; Ireland, Murray ; Kobayashi, Chisato ; Mendham, Peter ; White, David ; Crowther, Bill ; Kabbabe, Khris ; Post, Mark. / System framework for autonomous data processing onboard next generation of nanosatellite. Paper presented at 15th Reinventing Space Conference, Glasgow, United Kingdom.8 p.
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Greenland, S, Ireland, M, Kobayashi, C, Mendham, P, White, D, Crowther, B, Kabbabe, K & Post, M 2017, 'System framework for autonomous data processing onboard next generation of nanosatellite' Paper presented at 15th Reinventing Space Conference, Glasgow, United Kingdom, 24/10/17 - 26/10/17, .

System framework for autonomous data processing onboard next generation of nanosatellite. / Greenland, Steve; Ireland, Murray; Kobayashi, Chisato; Mendham, Peter; White, David; Crowther, Bill; Kabbabe, Khris; Post, Mark.

2017. Paper presented at 15th Reinventing Space Conference, Glasgow, United Kingdom.

Research output: Contribution to conferencePaper

TY - CONF

T1 - System framework for autonomous data processing onboard next generation of nanosatellite

AU - Greenland, Steve

AU - Ireland, Murray

AU - Kobayashi, Chisato

AU - Mendham, Peter

AU - White, David

AU - Crowther, Bill

AU - Kabbabe, Khris

AU - Post, Mark

PY - 2017/10/24

Y1 - 2017/10/24

N2 - 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.

AB - 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.

KW - CubeSat

KW - nanosatellite

KW - autonomy

KW - onboard data processing

KW - NewSpace

KW - earth observation

UR - http://rispace.org/

M3 - Paper

ER -

Greenland S, Ireland M, Kobayashi C, Mendham P, White D, Crowther B et al. System framework for autonomous data processing onboard next generation of nanosatellite. 2017. Paper presented at 15th Reinventing Space Conference, Glasgow, United Kingdom.