An AEGIS-FISST Algorithm for Multiple Object Tracking in Space Situational Awareness
Islam I. Hussein, Kyle J. DeMars, Carolin Früh, Moriba Kemessia Jah, Richard Scott Erwin · AIAA/AAS Astrodynamics Specialist Conference · 2012
In this paper we use Finite Set Statistics (FISST) to track an arbitrary but known number of objects in the presence of clutter for Space SituationalAwareness (SSA). The sensors are assumed faulty with possible misdetections, false alarms and/or noisy data. The measurements are unassociated and, hence, we also solve the data association problem, which is integral to the FISST algorithm. The main contribution of the paper is that, in order to reduce the computational burden entailed in FISST, we employ a Gaussian mixture approximation,not to the first-moment of the full FISST updateequations(knownasGM-PHD),butapplytheapproximationdirectlytothefullFISSTequations. The specific GM technique we employ is the Adaptive Entropy-based Gaussian-mixture Information Synthesis (AEGIS). The approachis demonstratedin two a multiple object SSA applicationexamples.