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Trusted Autonomous Operations of Distributed Satellite Systems Using Optical Sensors.

Kathiravan Thangavel1,2,3,4, Dario Spiller2,3, Roberto Sabatini5,1,3,4

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Summary
This summary is machine-generated.

This study introduces intelligent Distributed Satellite Systems (iDSS) for autonomous wildfire management. These systems leverage AI for real-time data processing and satellite reconfiguration, enhancing mission effectiveness.

Keywords:
PRISMAastrionicsbushfiredisaster managementdistributed satellite systems (DSSs)edge computinghyperspectral imageryintelligent DSS (iDSS)mission managementoptical sensorstrusted autonomous satellite operations (TASO)wildfire

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Area of Science:

  • Spacecraft Systems Engineering
  • Artificial Intelligence in Space Applications
  • Earth Observation

Background:

  • Distributed Satellite Systems (DSS) offer enhanced mission value through reconfiguration and incremental updates.
  • Trusted Autonomous Satellite Operation (TASO) relies on AI for predictive and reactive integrity.
  • Autonomous reconfiguration is crucial for DSS in time-critical missions like disaster relief.

Purpose of the Study:

  • To explore the application of intelligent Distributed Satellite Systems (iDSS) for near-real-time wildfire management.
  • To propose a Low Earth Orbit (LEO) satellite constellation for continuous monitoring of Areas of Interest (AOI).
  • To demonstrate the feasibility of AI-based wildfire detection on-board iDSS satellites.

Main Methods:

  • Development of an iDSS architecture with reconfiguration capabilities and Inter-Satellite Links (ISL).
  • Integration of AI, advanced sensing, and computing technologies for trusted autonomy.
  • On-board AI-based data processing using hardware accelerators for wildfire detection.

Main Results:

  • Demonstrated the feasibility of AI-based data processing on-board satellite hardware.
  • Developed AI software for on-board wildfire detection within the iDSS framework.
  • Simulation case studies validated the iDSS architecture for wildfire management across diverse geographic locations.

Conclusions:

  • iDSS enables a responsive and resilient approach to Space Mission Management (SMM), particularly for data collection and processing.
  • The proposed iDSS architecture supports extensive coverage and rapid revisit intervals essential for dynamic environmental monitoring.
  • AI-powered on-board processing significantly enhances the capability of LEO constellations for near-real-time wildfire detection and management.