Abstract
With the rapid increase in resident space objects (RSO), there is a growing demand for their identification and characterization to advance space simulation awareness (SSA) programs. Various AI-based technologies are proposed and demonstrated around the world to effectively and efficiently identify RSOs from ground and space-based observations; however, there remains a challenge in AI training due to the lack of labeled datasets for accurate RSO detection. In this paper, we present an overview of the starfield simulator to generate a realistic representation of images from space-borne imagers. In particular, we focus on low-resolution images such as those taken with a commercial-grade star tracker that contains various RSO in starfield images. The accuracy and computational efficiency of the simulator are compared to the commercial simulator, namely STK-EOIR to demonstrate the performance of the simulator. In comparing over 1000 images from the Fast Auroral Imager (FAI) onboard CASSIOPE satellite, the current simulator generates both stars and RSOs with approximately the same accuracy (compared to the real images) as STK-EOIR and, an order of magnitude faster in computational speed by leveraging parallel processing methodologies.
Highlights
Space Situational Awareness (SSA) is becoming an increasingly important issue around the world as the number of resident space objects (RSOs) is continually increasing
Detection, we focused on Fast Auroral Imager (FAI) parameters as the baseline imager, due to a large number of images being publicly available with well-known host satellite positions
In the case of debris where the shape is sometimes not known, the RSO’s are simulated as a perfectly reflective sphere. This is commonly done in different RSO simulators to estimate the effective light coming off the object which allows for the training and evaluation of detection algorithms [5], as well as detection prediction [1]
Summary
Space Situational Awareness (SSA) is becoming an increasingly important issue around the world as the number of resident space objects (RSOs) is continually increasing. SSA requires innovative, robust, and reliable solutions to identify, track, and characterize RSOs. Previously, we demonstrated the feasibility of using low-resolution on-orbit images (such as star tracker images) for RSO detection. SBOIS serves as the training tool to enable artificial intelligence (AI) algorithm design to identify and characterize the RSOs from low-resolution images. AI algorithms have been shown in recent years to perform accurately and efficiently after training, such as [5] for RSO detection and [6] for RSO characterization Research such as [7,8] highlights the need for high quality and quantity of training data about starfield images and corresponding labels. While STK-EOIR proves to be a versatile platform to simulate starfield images for mission planning and proof-of-concept demonstration, it still lacks the flexibility and efficiency we seek for the current studies applications. In developing SBOIS, three key parameters were considered to enable RSO tracking algorithms: (1) RSO centroid estimation; (2) implementation of parallel processing; and (3) versatility in simulation parameters
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