Random Finite Sets and their Impact in Space Situational Awareness

Speaker: Martin Adams, Dept. Electrical Engineering, Advanced Mining Technology Centre (AMTC), University of Chile, Chile.

Abstract: An increased concern in space situational awareness has resulted from the rise in space debris and its threat to future space missions. For safety reasons, it is critically important to populate and maintain a catalog of orbiting objects. To detect and track space debris, telescopes and radars are typically used, resulting in multiple point measurements, which have to be processed in order to discriminate detections from clutter. Such processes are often based on Bayesian filtering. Conventional filters, such as the Kalman filter, cannot be directly applied to cluttered data problems because a multitarget estimate is required and/or multiple measurements are received. The recently introduced random finite set theory provides an elegant framework within which such problems can be naturally expressed and solved. The application of random sets in multi-target tracking has led to the development of Finite Set Statistics, which provide the basis for filters such as the Probability Hypothesis Density (PHD) filter and more recently the Generalized Labelled Multi-Bernoulli (GLMB) filter, which recently attracted considerable research interest as well as deployment in commercial applications. In the field of Space Situational Awareness, the identification and tracking of orbiting debris travelling at dangerously high speeds is of concern. Determining the number and state (e.g. position and velocity) of debris components which have passed through the field of view of a telescopic sensors, in the presence of measurement noise, clutter and false alarms, is a field of research which can be addressed with FISST based tools. Recent experimental results, demonstrating space debris tracking, based on radar and image data, will be demonstrated.