The Medium Wavelength Infrared (MWIR) band is optimal for detecting high-intensity targets, e.g. fires. Infrared target detection and object recognition in the MWIR spectrum pose challenges for small satellites with resource constraints. The SeRANIS experimental satellite mission, under development at the University of the Bundeswehr Munich, features an MWIR sensor with a variable Field of View (FoV), but with a limited spatial resolution. This research aims to detect Hypersonic Glide Vehicle (HGV) traveling at speeds of approximately up to 7km/s in the upper atmosphere. The 1280*1024-pixel resolution offers a variable footprint ranging from 37km to 465km diagonally, facilitating the detection of intense heat signatures emitted by Hypersonic Glide Vehicles (HGVs). Combining the sensor’s spatial resolution, small target size, and high speed increases the complexity of developing detection methods. Additionally, the experimental satellite mission relies solely on a single MWIR band, increasing the complexity of precise target recognition. To address these concerns, this research proposes a three-part method: (a) hot spot detection, (b) computation and application of radiance threshold, and (c) filtering out static hot spots. However, the lack of a dataset for HGV detection poses a significant challenge for developing dedicated techniques. Therefore, this research introduces a strategy to create synthetic datasets, to replicate realistic sensor movement in orbit, inserting stationary targets with a range of radiance, and simulating moving targets with varying trajectories and radiance to validate the proposed methodology.
Recent developments in the military domain introduce the need to detect and track hypersonic glide vehicles in Earth’s atmosphere. The Multispectral Object Sensing by Artificial Intelligence-processed Cameras (MOSAIC) experiment is part of the small-satellite ATHENE-1 of the Universit¨at der Bundeswehr M¨unchen. The primary scientific objective of MOSAIC is to demonstrate reliable detection, identification and tracking of hypersonic glide vehicles using primarily a cooled infrared camera and complementary a visual camera. To cope with a large amount of data from both high-resolution cameras in real-time, state-of-the-art computer vision on-board processing methods are used for detection and tracking. The secondary scientific objective is to investigate the efficiency and reliability of Artificial Intelligence (AI) based image processing algorithms and data compression for space applications. This is of particular importance given the high volumes and rates of data. The application of such algorithms requires a reliable and resource-efficient On-Board Computer (OBC) that can withstand the harsh space environment. The approach outlined in this paper envisions a dedicated OBC to manage the AI-based experiments of the satellite, called the Artificial Intelligence capable On-Board Computer (AI-OBC). The AI-OBC includes multiple hardware-based AI accelerators to meet the computational requirements and ensure real-time processing for object detection and tracking. This paper describes the structure of the data processing pipeline and includes the AI-OBC architecture with its connections to both the cameras and the platform’s OBC. Further, the study discusses the training and validation steps of the intended use-cases.
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