Information-driven sensor planning: Navigating a statistical manifold

Douglas Cochran, Alfred O. Hero

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Many adaptive sensing and sensor management strategies seek to determine a sequence of sensor actions that successively optimizes an objective function. Frequently the goal is to adjust a sensor to best estimate a partially observed state variable, for example, the objective function may be the final mean-squared state estimation error. Information-driven sensor planning strategies adopt an objective function that measures the accumulation of information as defined by a suitable metric, such as Fisher information, Bhattacharyya affinity, or Kullback-Leibler divergence. These information measures are defined on the space of probability distributions of data acquired by the sensor, and there is a distribution in this space corresponding to each sensor configuration. Hence, sensor planning can be posed as a problem of optimally navigating over a statistical manifold of probability distributions. This information-geometric perspective presents new insights into adaptive sensing and sensor management.

Original languageEnglish (US)
Title of host publication2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
Pages1049-1052
Number of pages4
DOIs
StatePublished - Dec 1 2013
Event2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Austin, TX, United States
Duration: Dec 3 2013Dec 5 2013

Publication series

Name2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings

Other

Other2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
CountryUnited States
CityAustin, TX
Period12/3/1312/5/13

Keywords

  • Adaptive sensing
  • Hellinger distance
  • Information geometry
  • Multinomial class of distributions
  • Sensor management

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

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  • Cite this

    Cochran, D., & Hero, A. O. (2013). Information-driven sensor planning: Navigating a statistical manifold. In 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings (pp. 1049-1052). [6737074] (2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings). https://doi.org/10.1109/GlobalSIP.2013.6737074