A Mathematical Framework for an Improved Search Model.

Abstract

Search is currently modeled for DoD applications by a single exponential function. The two adjustable parameters are the time constant, t, characterizing the exponential; and the long time detection probability, P. Deficiencies of the classical model are: human performance data cannot typically be fit with a single exponent model; the probability of detection for short times is less than that predicted by the classical model; the effects of multiple targets and clutter can only be included by adjusting the two-model parameters, which is performed in an ad hoc manner and over-constrains the model. This paper introduces a neoclassical model that includes three processes: attending to the target, random wandering around the scene and attending to other targets/clutter. An expression involving three exponents associated with the three processes is derived and special cases are described. The new model provides uniform treatment of multiple targets and false detections and allows for the separate descriptions of multiple times scales within the search process. Searches can be separated into single region, field-of-view search, and multiple region, field-of-regard search. Field-of-view search can be further subdivided into long searches during which the observer may examine many targets and short searches which are completed after a few target examinations.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1994
Accession Number
ADA288858

Entities

People

  • Jeffrey Nicoll

Organizations

  • Institute for Defense Analyses

Tags

DTIC Thesaurus Topics

  • Computational Science
  • Data Analysis
  • Department Of Defense
  • Detection
  • Differential Equations
  • Dwell Time
  • Equations
  • Experimental Data
  • Markov Models
  • Mathematical Models
  • Motor Skills
  • Multiple Targets
  • Probability
  • Probability Distributions
  • Target Acquisition
  • Target Detection
  • Targets

Readers

  • Radar Systems Engineering.
  • Statistical inference.
  • Theoretical Analysis.