Title page for ETD etd-06182012-134108


Type of Document Master's Thesis
Author Chin, Philip Allen
URN etd-06182012-134108
Title Sensor Networks: Studies on the Variance of Estimation, Improving Event/Anomaly Detection, and Sensor Reduction Techniques Using Probabilistic Models
Degree Master of Science
Department Mechanical Engineering
Advisory Committee
Advisor Name Title
Roach, John W. Committee Chair
Leonessa, Alexander Committee Member
Papenfuss, Cory M. Committee Member
Keywords
  • Cramer-Rao bounds
  • distributed sensor network
  • source localization
  • direction of arrival
  • sensor reduction
  • event monitoring
  • event detection
  • direction of travel
  • joint probability
  • geospatial intelligence
  • classifier
  • correlation
  • anomaly detection
Date of Defense 2012-06-15
Availability unrestricted
Abstract
Sensor network performance is governed by the physical placement of sensors and their geometric relationship to the events they measure. To illustrate this, the entirety of this thesis covers the following interconnected subjects: 1) graphical analysis of the variance of the estimation error caused by physical characteristics of an acoustic target source and its geometric location relative to sensor arrays, 2) event/anomaly detection method for time aggregated point sensor data using a parametric Poisson distribution data model, 3) a sensor reduction or placement technique using Bellman optimal estimates of target agent dynamics and probabilistic training data (Goode, Chin, & Roan, 2011), and 4) transforming event monitoring point sensor data into event detection and classification of the direction of travel using a contextual, joint probability, causal relationship, sliding window, and geospatial intelligence (GEOINT) method.
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