Parallelizing On-Board Data Analysis Applications for a Distributed Processing Architecture

Abstract

Satellite-based applications produce ever-increasing quantities of data, challenging the capabilities of existing telemetry and on-board processing systems, especially when results must be transmitted quickly to ground. The Scalable On-Board Computing for Space Avionics (ScOSA) platform contributes the processing capability necessary to perform such computationally intensive analysis on-board. This platform offers a high-performance on-board computer by combining multiple commercial off-the-shelf processors and space-grade processors into a distributed computer. Middleware ensures reliability by detecting and mitigating faults, while allowing applications to effectively use multiple, distributed processors. The current work aims to demonstrate the use and advantages of utilizing the data-flow programming paradigm supported by the ScOSA platform to provide high-throughput on-board analysis. This enables rapid analysis even for applications requiring high frame rates, high resolutions, multi-spectral imaging or in-depth processing. The On-Board Data Analysis and Real-Time Information System (ODARIS) is used to demonstrate this method. ODARIS is a system for providing low-latency access to satellite-based observations, even when large quantities of sensor data are involved. By performing on-board processing of the data from the satellite-borne instruments, the amount of data which must be sent to ground is drastically reduced. This allows the use of low-latency telecommunication-satellite constellations for communicating with ground to achieve query-response times of only a few minutes. The current application combines an Earth-observation camera with AI-based image processing to provide real-time object detection. In the data-flow driven implementation of ODARIS on the ScOSA platform, images are captured by a camera and sent to any of several processors for the computationally intensive image processing. This allows multiple images to be processed in parallel by as many processors as are available, while avoiding the need to divide each image across several processors. The results are transferred to an on-board database from which queries can be served asynchronously. The system will be tested in configurations with one, two and three processors and the resulting image throughput presented. Testing is performed on a ground-based prototype system using pre-recorded images. This paper presents the necessary details of the underlying ScOSA and ODARIS systems as well as the implementation of the objection-detection algorithm using a parallelized, data-flow model. The results of executing the system using a variable number of processors are presented to demonstrate the improvement in image throughput and its potential application to other computationally-intensive tasks.