On-Board Image Processing inside the RaioSat Experiment Mission – Early Results and Suitability Tests

Abstract

The RaioSat Experiment mission, coordinated by the Earth Science Coordination at the National Institute for Space Research (INPE) in Brazil, aims to monitor atmospheric discharges, also known as lightning flashes, using either a 6U CubeSat configuration or a piggyback configuration on a larger satellite. This mission will collect valuable data over the Brazilian territory, aiding in the analysis of correlations between lightning activity and extreme weather events. A critical component of this mission is the development and validation of an onboard computer (OBC) integrated with a LCD sensor for detecting and processing lightning flashes. The iOBC from ISISpace has been tested on this task. In order to ensure the iOBC’s suitability for the RaioSat mission, extensive testing is required, focusing on its processing speed and ability to handle the detection of lightning events using static images. This abstract outline the methodology and results of these tests. The tests simulate the real-time conditions the iOBC will encounter in orbit, focusing on the processing of example pictures containing randomly generated lightning events. The testing procedure involves generating synthetic images with varying densities and patterns of lightning events, reflecting potential in-orbit scenarios. The iOBC’s processing capabilities are evaluated based on its ability to accurately detect and process these lightning events within the images. Key performance metrics include the processing time per image, latency, and accuracy of lightning detection. Initial results indicate that the iOBC demonstrates very good accuracy and processing speed suitable for night-time detection of lightning events. However, during the day, the background noise is too heavy for effective real-life detection. To address this, an increase in processing speed is necessary for daytime operations. The current performance suggests that the iOBC may have the potential for further optimization to handle these conditions and contribute to a better understanding of extreme weather patterns in Brazil.