ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
- Arizona State University
- 1 Bliss, Daniel W.
- 1 Dasarathy, Gautam
- 1 Moraffah, Bahman
- 1 Papandreou-Suppappola, Antonia
- 1 Richmond, Christ D.
- 1 English
- 1 Public
- Statistics
- Bayesian Nonparametrics
- 1 Computer science
- 1 Dependent Dirichlet Process
- 1 Dependent Pitman-Yor Process
- 1 Electrical engineering
- 1 Multiple Object Tracking
- 1 Scalable Bayesian Inference
- more
- Dwarf Galaxies as Laboratories of Protogalaxy Physics: Canonical Star Formation Laws at Low Metallicity
- Evolutionary Genetics of CORL Proteins
- Social Skills and Executive Functioning in Children with PCDH-19
- Deep Domain Fusion for Adaptive Image Classification
- Software Defined Pulse-Doppler Radar for Over-The-Air Applications: The Joint Radar-Communications Experiment
The problem of multiple object tracking seeks to jointly estimate the time-varying cardinality and trajectory of each object. There are numerous challenges that are encountered in tracking multiple objects including a time-varying number of measurements, under varying constraints, and environmental conditions. In this thesis, the proposed statistical methods integrate the use of physical-based models with Bayesian nonparametric methods to address the main challenges in a tracking problem. In particular, Bayesian nonparametric methods are exploited to efficiently and robustly infer object identity and learn time-dependent cardinality; together with Bayesian inference methods, they are also used to associate measurements to objects and …
- Contributors
- Moraffah, Bahman, Papandreou-Suppappola, Antonia, Bliss, Daniel W., et al.
- Created Date
- 2019