dc.contributor.advisor | Chakravorty , Suman | |
dc.creator | Faber, Weston Ryan | |
dc.date.accessioned | 2019-01-17T17:13:06Z | |
dc.date.available | 2019-01-17T17:13:06Z | |
dc.date.created | 2018-05 | |
dc.date.issued | 2018-05-08 | |
dc.date.submitted | May 2018 | |
dc.identifier.uri | https://hdl.handle.net/1969.1/173387 | |
dc.description.abstract | In order to protect assets and operations in space, it is critical to collect and maintain accurate
information regarding Resident Space Objects (RSOs). This collection of information is typically
known as Space Situational Awareness (SSA). Ground-based and space-based sensors provide information
regarding the RSOs in the form of observations or measurement returns. However, the
distance between RSO and sensor can, at times, be tens of thousands of kilometers. This and other
factors lead to noisy measurements that, in turn, cause one to be uncertain about which RSO a
measurement belongs to. These ambiguities are known as data association ambiguities. Coupled
with uncertainty in RSO state and the vast number of objects in space, data association ambiguities
can cause the multiple space object-tracking problem to become computationally intractable.
Tracking the RSO can be framed as a recursive Bayesian multiple object tracking problem with
state space containing both continuous and discrete random variables. Using a Finite Set Statistics
(FISST) approach one can derive the Random Finite Set (RFS) based Bayesian multiple object
tracking recursions. These equations, known as the FISST multiple object tracking equations, are
computationally intractable when solved in full. This computational intractability provokes the
idea of the newly developed alternative hypothesis dependent derivation of the FISST equations.
This alternative derivation allows for a Markov Chain Monte Carlo (MCMC) based randomized
sampling technique, termed Randomized FISST (R-FISST). R-FISST is found to provide an accurate
approximation of the full FISST recursions while keeping the problem tractable. There are
many other benefits to this new derivation. For example, it can be used to connect and compare the
classical tracking methods to the modern FISST based approaches. This connection clearly defines
the relationships between different approaches and shows that they result in the same formulation
for scenarios with a fixed number of objects and are very similar in cases with a varying number
of objects. Findings also show that the R-FISST technique is compatible with many powerful
optimization tools and can be scaled to solve problems such as collisional cascading. | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.subject | Space Situational Awareness | en |
dc.subject | Multiple Object Tracking | en |
dc.subject | Randomized Hypothesis Generation | en |
dc.subject | Random Finite Sets | en |
dc.subject | Multiple Hypothesis Tracking | en |
dc.subject | Collisional Cascading | en |
dc.subject | Space Object Fragmentation | en |
dc.subject | Markov Chain Monte Carlo | en |
dc.title | Multiple Space Object Tracking Using A Randomized Hypothesis Generation Technique | en |
dc.type | Thesis | en |
thesis.degree.department | Aerospace Engineering | en |
thesis.degree.discipline | Aerospace Engineering | en |
thesis.degree.grantor | Texas A & M University | en |
thesis.degree.name | Doctor of Philosophy | en |
thesis.degree.level | Doctoral | en |
dc.contributor.committeeMember | Vadali, Srinivas R | |
dc.contributor.committeeMember | Bhattacharya, Raktim | |
dc.contributor.committeeMember | Kumar, P R | |
dc.type.material | text | en |
dc.date.updated | 2019-01-17T17:13:06Z | |
local.etdauthor.orcid | 0000-0003-4830-1614 | |