Game Theoretic Training Enabled Deep Learning Solutions for Rapid Discovery of Satellite Behaviors

Abstract

The chapter presents a game theoretic training model enabling a deep learning solution for rapid discovery of satellite behaviors from collected sensor data. The solution has two parts, namely, Part 1 and Part 2. Part 1 is a PE game model that enables data augmentation method, and Part 2 uses convolutional neural networks (CNNs) for satellite behavior classification. The sensor data are propagated with the various maneuver strategies from the proposed space game models. Under the PE game theoretic framework, various satellite behaviors are simulated to generate synthetic datasets with labels for the training to detect space object behaviors. To evaluate the performance of the proposed PE model, a CNN model is designed and implemented for satellite behavior classification. Python 3 and TensorFlow are used in this implementation. The simulation results show that the trained machine learning model can efficiently and correctly classify the satellite behaviors up to 99.8%.

Machine Learning and Deep Learning powered satellite communications: Enabling technologies, applications, open challenges, and future research directions

“…Generating labeled datasets from existing ones can improve the accuracy of ML prediction. Authors in 72 have used game theory for training ML models to predict satellite behavior (space situational awareness). It is used to control the satellite movement to maintain harmony.…”

Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

“…Other recent methods for effective SSA include time-delay Neural networks (TDNN) [ 65 ], ML and CNN networks [ 66 , 67 , 68 , 69 , 70 ], clustering [ 71 ], orbital control theory [ 72 ], a game theoretic approach, namely Adaptive Markov Inference Game Optimization (AMIGO) engine [ 73 , 74 , 75 , 76 ], and Deep Reinforcement Learning [ 77 , 78 , 79 ]. Even more recently, new trends in the literature are focusing on agile, intelligent, and efficient computer vision architectures operating on quantum neuromorphic computing as part of an SSA network [ 80 ].…”