Accession Number : AD1011580


Title :   Statistical, Graphical, and Learning Methods for Sensing, Surveillance, and Navigation Systems


Descriptive Note : Technical Report,01 Jun 2012,31 May 2016


Corporate Author : MASSACHUSETTS INST OF TECH CAMBRIDGE CAMBRIDGE United States


Personal Author(s) : Willsky,Alan S ; Win,Moe Z


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1011580.pdf


Report Date : 28 Jun 2016


Pagination or Media Count : 21


Abstract : This final report summarizes our accomplishments over the four years of support under this grant. The first of two interrelated research areas focuses on scalable, high-performance inference algorithms for graphical and hierarchical models. This has clear applications to sensor exploitation applications such as tracking and distributed network fusion, including the development of message-passing algorithms for location-aware networks in complex (possibly GPS-denied)and often communications-limited environments. Our second thrust focuses on discovering graphical models not only relating different sensor observables but also discovering and linking them to higher-level hidden variables capturing the common context that relates them. One motivation here is to enhance both lower-level sensor processing (e.g., for object recognition) and higher-level context discovery through these models. A second motivation is the discovery of complex, possibly coordinated dynamic behavior exploiting emerging methods of Bayesian nonparametric modeling.


Descriptors :   statistical processes , signal processing , image processing , algorithms , sensor fusion , optimization


Subject Categories : Statistics and Probability
      Operations Research
      Cybernetics


Distribution Statement : APPROVED FOR PUBLIC RELEASE