An Information-Geometric Approach to Sensor Scheduling

Abstract

An information-geometric approach to sensor management is introduced that is based on following geodesic curves in a manifold of possible sensor configurations. This perspective arises by observing that, given a parameter estimation problem to be addressed through management of sensor assets, any particular sensor configuration corresponds to a Riemannian metric on the parameter manifold. With this perspective, managing sensors involves navigation on the space of all Riemannian metrics on the parameter manifold, which is itself a Riemannian manifold. Existing work assumes the metric on the parameter manifold is one that, in statistical terms, corresponds to a Jeffreys prior on the parameter to be estimated. It is observed that informative priors, as arise in sensor management, can also be accommodated. Given an initial sensor configuration, the trajectory along which to move in sensor configuration space to gather most information is seen to be locally defined by the geodesic structure of this manifold. Further, divergences based on Fisher and Shannon information lead to the same Riemannian metric and geodesics.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2012
Accession Number
ADA587095

Entities

People

  • B. Moran
  • D. Cochran
  • S. D. Howard

Organizations

  • University of Michigan

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Algorithms
  • Computations
  • Construction
  • Coordinate Systems
  • Differential Equations
  • Equations
  • Geodesics
  • Geometry
  • Integrals
  • Measurement
  • Military Research
  • Probability
  • Probability Distributions
  • Signal Processing
  • Trajectories

Fields of Study

  • Computer science

Readers

  • Graph Algorithms and Convex Optimization.
  • Sensor Fusion and Tracking Systems.
  • Statistical inference.

Technology Areas

  • Space
  • Space - Space Objects