This article presents the Labeled Random Finite Set (LRFS) framework for multi-object
systems–systems in which the number of objects and their states are unknown and vary …

The rapid development of Low Earth Orbit (LEO) mega constellations has significantly
contributed to several aspects of human scientific progress, such as communication …

This article presents a flexible modeling framework for multitarget tracking based on the
theory of outer probability measures. The notion of labeled uncertain finite set is introduced …

To ensure the secure operation of space assets, it is crucial to employ ground and/or space-
based surveillance sensors to observe a diverse array of anthropogenic space objects …

Bayesian filtering is a popular class of estimation algorithms for addressing the space object
tracking problem. Bayesian filters assume a random physical system with known statistics of …

In multi-sensor systems (MSSs), sensor selection is a critical technique for obtaining high-
quality sensing data. However, when the number of sensors to be selected is unknown in …

The space object catalog provides orbital state and characteristic information of space
objects for critical applications in space situational awareness. Maintaining accurate states …

SPACECRAFT proximity operations offer enhanced opportuni-ties for in-orbit services or
space surveillance of space objects, eg, operational spacecraft or space debris [1, 2]. Within …

The increased congestion of the orbital environment necessitates improved ground-based
telescope tasking, most forms of which focus on covariance minimization. Limited work has …

Sensor management is a crucial research subject for multi-sensor multi-target tracking in
wireless sensor networks (WSNs) with limited resources. Bearings-only tracking produces …