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A multiple hypothesis rule-based automatic target recognizer

  • Knowledge-Based Methods
  • Conference paper
  • First Online:
Pattern Recognition (PAR 1988)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 301))

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Abstract

The majority of automatic target recognizers undertaking field evaluation today owe their internal structure to a classical statistical approach. Although the dimensionality of the variable parameters that each system is subject to is large, little use is made of context and ancillary information such as time of day, sensor, weather conditions and intelligence data. Such ancillary data can be profitably used to alleviate the algorithmic burden of accommodating the extreme ranges of conditions.

Presented here is a novel approach to include ancillary knowledge into the control structure of an automatic target recognizer (ATR).

J. Keller was partially supported by a contract A75786-67 from Emerson Electric Electronics and Space Division and an Air Force Office of Scientific Research grant AFOSR-87-0226.

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Author information

Authors and Affiliations

  1. Carl Carpenter Gregory Hobson Emerson Electric Electronics and Space Division, 8100 West Florissant, 63136, St. Louis, MO

    John Wootton

  2. Electrical and Computer Engineering Department, University of Missouri-Columbia, 65211, Columbia, MO

    James Keller

Authors
  1. John Wootton
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  2. James Keller
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Editor information

J. Kittler

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© 1988 Springer-Verlag Berlin Heidelberg

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Wootton, J., Keller, J. (1988). A multiple hypothesis rule-based automatic target recognizer. In: Kittler, J. (eds) Pattern Recognition. PAR 1988. Lecture Notes in Computer Science, vol 301. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-19036-8_31

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  • DOI: https://doi.org/10.1007/3-540-19036-8_31

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-19036-3

  • Online ISBN: 978-3-540-38947-7

  • eBook Packages: Springer Book Archive

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