An Unsupervised Learning-Based Manoeuvre Detection Method for Resident Space Object Pattern of Life Characterisation

The problem revolving around the status of the near-Earth environment is complex and ever-changing, requiring a collective effort to monitor the evolution of the resident space object population from both institutions and industry. Reaching a comprehensive Space Situational Awareness has thus become of utmost importance, due to the immediate effects on every phase of a space mission, from its design to the operations foreseen after its deployment. A fundamental aspect involved in the surveillance and tracking of space objects is taking operational ones into account in terms of their manoeuvring capabilities. Actively controlled objects can, in fact, hamper the correlation of new tracks, leading to false or missed associations, and reduce orbit determination accuracy, introducing unmodeled contributions to the assumed dynamics. One way of addressing the problem consists in characterising the pattern of life of operative objects in terms of manoeuvres in order to understand how likely it is for a target to be actively controlled within a specific time frame. The main showstopper identified in the literature is the shortage of manoeuvre histories used to generate labels for supervised learning algorithms. This severely reduces a method’s generalization capability. The current work aims to overcome this limitation by resorting to unsupervised learning techniques commonly used for anomaly detection purposes to identify manoeuvres with no prior information on the target’s control history via a sequential autoencoder. The proposed application considers ballistic motion as the ‘normal behaviour’ while identifying manoeuvring arcs as out-of-nominal samples.