An Information Elasticity Framework For Constant False Alarm Rate Detection
Open Access
- Author:
- Liu, Andrew
- Graduate Program:
- Electrical Engineering
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- December 19, 2019
- Committee Members:
- Ram Mohan Narayanan, Thesis Advisor/Co-Advisor
Timothy Joseph Kane, Committee Member
Muralidhar Rangaswamy, Special Signatory
Kultegin Aydin, Program Head/Chair - Keywords:
- Information Elasticity
Constant False Alarm Rate
Radar Detection Theory
Space Time Adaptive Processing
Multi Objective Optimization
Information Overload
Information Value - Abstract:
- Within a decision making process, adjusting the amount of available information generally causes the effectiveness of decisions to change. Often, an increase in this information quantity causes the decision effectiveness to improve. However, under certain circumstances, increas- ing the amount of information beyond a certain point causes the decision effectiveness to suffer. This phenomenon, known as information overload, presents many important research problems. One major concern is determining how much information a decision maker needs for the decision effectiveness to be maximized. Another key problem is defining the metrics that are used to model information quantity and decision effectiveness, given the specific contextual factors and preferences of a decision maker. Recently, the concept of information elasticity has been proposed to address these problems. This thesis aims to design a framework using the concept of information elasticity to observe the usability of information within different constant false alarm rate detectors. Within this framework, the different factors which either benefit or hinder the performance of these detectors are studied, and are used along with contextual factors to characterize the effectiveness of decisions. Within this thesis, two different applications of this framework are studied. The first involves the ordered statistics constant false alarm rate detector, and the second involves the adaptive matched filter. The point at which information overload occurs is uncovered within each of these applications, allowing a decision maker to make choices that maximize the decision effectiveness.