Comparative Analysis of Resident Space Object (RSO) Detection Methods.

In recent years, there has been a significant increase in satellite launches, resulting in a proliferation of satellites in our near-Earth space environment. This surge has led to a multitude of resident space objects (RSOs). Thus, detecting RSOs is a crucial element of monitoring these objects and plays an important role in preventing collisions between them. Optical images captured from spacecraft and with ground-based telescopes provide valuable information for RSO detection and identification, thereby enhancing space situational awareness (SSA). However, datasets are not publicly available due to their sensitive nature. This scarcity of data has hindered the development of detection algorithms. In this paper, we present annotated RSO images, which constitute an internally curated dataset obtained from a low-resolution wide-field-of-view imager on a stratospheric balloon. In addition, we examine several frame differencing techniques, namely, adjacent frame differencing, median frame differencing, proximity filtering and tracking, and a streak detection method. These algorithms were applied to annotated images to detect RSOs. The proposed algorithms achieved a competitive degree of success with precision scores of 73%, 95%, 95%, and 100% and F1 scores of 68%, 77%, 82%, and 79%.

Classification of Low Earth Orbit (LEO) Resident Space Objects' (RSO) Light Curves Using a Support Vector Machine (SVM) and Long Short-Term Memory (LSTM).

Light curves are plots of brightness measured over time. In the field of Space Situational Awareness (SSA), light curves of Resident Space Objects (RSOs) can be utilized to infer information about an RSO such as the type of object, its attitude, and its shape. Light curves of RSOs in geostationary orbit (GEO) have been a main research focus for many years due to the availability of long time series data spanning hours. Given that a large portion of RSOs are in low Earth orbit (LEO), it is of great importance to study trends in LEO light curves as well. The challenge with LEO light curves is that they tend to be short, typically no longer than a few minutes, which makes them difficult to analyze with typical time series techniques. This study presents a novel approach to observational LEO light curve classification. We extract features from light curves using a wavelet scattering transformation which is used as an input for a machine learning classifier. We performed light curve classification using both a conventional machine learning approach, namely a support vector machine (SVM), and a deep learning technique, long short-term memory (LSTM), to compare the results. LSTM outperforms SVM for LEO light curve classification with a 92% accuracy. This proves the viability of RSO classification by object type and spin rate from real LEO light curves.