Investigating AI-Enabled Space Debris Characterization and Adversarial Resilience

Abstract

The accumulation of space debris in Earth’s orbit has emerged as a major concern for the safety and sustainability of space operations. As more satellites are launched and breakup events occur, the density of debris continues to grow, increasing the likelihood of collisions with active spacecraft. To maintain reliable space operations, there is a pressing need for methods that can accurately identify and characterize debris, enabling improved tracking, collision avoidance, and long-term management of the orbital environment. This thesis investigates the application of machine learning and deep learning models to classify and characterize space debris based on unique synthetic light curve data. Beyond the generation of the light curve dataset, three characterization experiments are presented in this work: (1) attitude classification using Extreme Gradient Boosting (XGBoost) and Wavelet Scattering Transform (WST) features, (2) shape classification using a Long Short-Term Memory (LSTM) network, Fully Convolutional Network (FCN), and a hybrid Long Short-Term Memory – Fully Convolutional Network (LSTM-FCN) model, and (3) multitask learning for simultaneous shape and material classification using the hybrid model. A fourth component of the thesis evaluates the LSTM-FCN model’s robustness against adversarial attacks generated using gradient-based methods. This adversarial study leveraged real publicly available debris and satellite light curve data from the Mini-MegaTORTORA (MMT) database. Results showed that the WST data augmentation method significantly improved classification performance for XGBoost by capturing multiscale frequency features. The LSTMFCN model outperformed both standalone LSTM and FCN models in shape classification tasks, while the multi-task architecture further enhanced performance by leveraging intertask dependencies. The adversarial study revealed that FCN-based surrogate models can produce highly effective attacks against the LSTM-FCN. It was also shown that when combating FCN-based attacks, common filtering-based defenses such as moving-average and wavelet filters are generally insufficient. This work concludes that integrating Artificial Intelligence (AI) into Space Domain Awareness (SDA) is crucial for managing the growing challenge of space debris, but also emphasizes the need to defend such systems from tampering and perturbation. Model reliability is foundational to the future of autonomous space operations and the protection of critical space-based services that support life on Earth.