Monte Carlo Techniques for Automated Target Recognition

  • Anuj Srivastava
  • Aaron D. Lanterman
  • Ulf Grenander
  • Marc Loizeaux
  • Michael I. Miller
Chapter
Part of the Statistics for Engineering and Information Science book series (ISS)

Abstract

Motivated by the extraordinary performance of the human visual system, automated recognition of targets from their remotely sensed images has become an active area of research. In particular, Bayesian techniques based on statistical models of system components such as targets, sensors, and clutter have emerged. Since the probability models associated with such physical systems are complicated, general closed-form analytical solutions are ruled out, and computational approaches become vital. This chapter explores a family of computational solutions applied to finding the unknowns associated with Automated Target Recognition (ATR) problems. In general ATR, remote sensors (such as visual or infrared cameras, or microwave or laser radars) observe a scene containing a number of targets, either moving or stationary. These sensors produce measurements that are analyzed by computer algorithms to detect, track and recognise the targets of interest in that scene. (Dudgen and Lacoss 1993) and (Srivastava, Miller and Grenander 1999) offer more detailed introductions.

Keywords

Stochastic Differential Equation Monte Carlo Technique Prior Density Jump Time Automate Target Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Anuj Srivastava
  • Aaron D. Lanterman
  • Ulf Grenander
  • Marc Loizeaux
  • Michael I. Miller

There are no affiliations available

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