Track-to-Track Data Association using Mutual Information
Abstract
In this paper, we build on recent work to further investigate the use of mutual information to solve various association problems in space situational awareness. Specifically, we solve the track-to-track association (TTTA) problem where we seek to associate a given set of tracks at one point in time with another set of tracks at a different time instance. Both sets of tracks are uncertain and are probabilistically described using multivariate normal distributions. This allows for a closed-form solution, based on the unscented transform and on mutual information. Future work will focus on developing a similar solution when uncertainty is analytic but not Gaussian or when it is completely non-analytic -e.g., when the uncertainty is described using a particle cloud. The proposed solution is inspired by a similar solution based on the unscented transform and mutual information for the observation-to-observation association (OTOA) problem that was developed by the authors in the past. This solution can be adjusted to address the classical observation-to-track association problem (OTTA), which will be the focus of future research. We will demonstrate the main result in simulation for LEO, MEO, GTO, and GEO orbit regimes to show general applicability.
- Publication:
-
Advanced Maui Optical and Space Surveillance Technologies Conference
- Pub Date:
- 2015
- Bibcode:
- 2015amos.confE..15H