Accession Number:

ADA623921

Title:

Bayesian Aggregation of Evidence for Detection and Characterization of Patterns in Multiple Noisy Observations

Descriptive Note:

Doctoral thesis

Corporate Author:

CARNEGIE-MELLON UNIV PITTSBURGH PA ROBOTICS INST

Personal Author(s):

Report Date:

2015-07-01

Pagination or Media Count:

150.0

Abstract:

Effective use of Machine Learning to support extracting maximal information from limited sensor data is one of the important research challenges in robotic sensing. This thesis develops techniques for detecting and characterizing patterns in noisy sensor data. Our Bayesian Aggregation BA algorithmic framework can leverage data fusion from multiple low Signal-To-Noise Ratio SNR sensor observations to boost the capability to detect and characterize the properties of a signal generating source or process of interest. We illustrate our research with application to the nuclear threat detection domain. Developed algorithms are applied to the problem of processing the large amounts of gamma ray spectroscopy data that can be produced in real-time by mobile radiation sensors. The thesis experimentally shows BAs capability to boost sensor performance in detecting radiation sources of interest, even if the source is faint, partiallyoccluded or enveloped in the noisy and variable radiation background characteristic of urban scenes. In addition, BA provides simultaneous inference of source parameters such as the source intensity or source type while detecting it. The thesis demonstrates this capability and also develops techniques to efficiently optimize these parameters over large possible setting spaces. Methods developed in this thesis are demonstrated both in simulation and in a radiation-sensing backpack that applies robotic localization techniques to enable indoor surveillance of radiation sources. The thesis further improves the BA algorithms capability to be robust under various detection scenarios. First, we augment BA with appropriate statistical models to improve estimation of signal components in low photon count detection, where the sensor may receive limited photon counts from either source andor background.

Subject Categories:

  • Statistics and Probability
  • Cybernetics
  • Miscellaneous Detection and Detectors
  • Nuclear Instrumentation

Distribution Statement:

APPROVED FOR PUBLIC RELEASE