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 …

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 …

Geosynchronous (GEO) satellites maneuver frequently to maintain their Earth-relative
position despite drift incurred from natural perturbations, but quantifying their diverse …

Multi-target state estimation refers to estimating the number of targets and their trajectories in
a surveillance area using measurements contaminated with noise and clutter. In the …

Abstract In the Initial Orbit Determination (IOD) context, Admissible Regions are subsets of
parameter space of orbital elements that are reckoned as functions of the tracking …

In the heavy clutter environment, the information capacity is large, the relationships among
information are complicated, and track initiation often has a high false alarm rate or missing …

Safe and sustainable operations in Geocentric Orbit require the acquisition, tracking, and
predictive use of a large amount of data pertaining to the existence, characterization, and …

The characteristics of non-Gaussian clutter in radar systems are different from standard
waveforms. To fully filter to achieve the accuracy of radar detection, the paper developed a …

Cell tracking is an essential tool for studying how cells behave and divide under different
conditions. This thesis proposes new approaches to track cells and their lineages using …

The dissertation proposes an online solution for separating an unknown and time-varying
number of moving sources using audio and visual data. The random finite set framework is …