A Statistical Decision Approach to Missile Payload Discrimination.

Abstract

AYLOAD, PENETRATION, PROBABILITY DENSITY FUNCTIONS, MONTE CARLO METHODBAYES TESTS, STATISTICAL DECISION THEORYThe report considers the statistical decision theory aspects of the problem of classifying each member of a fixed missile payload on the basis of observations of selected features. It is assumed that the payload contains two classes of objects, that the number of objects in each (payload mix) is known, and that the probability distribution of the feature observations for each class of objects is known. A simple Bayes classification test is first considered but makes no use of the payload mix information. This deficiency led to formulation of the improved test presented in this note which takes the mix information fully into account. The results of the two tests are than compared via simulation to discover the improvement gained by using the new test. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jul 18, 1973
Accession Number
AD0764723

Entities

People

  • N. J. Krikelis

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Classification
  • Data Science
  • Decision Theory
  • Deficiencies
  • Discrimination
  • Information Science
  • Mathematics
  • Observation
  • Probability
  • Probability Density Functions
  • Probability Distributions
  • Random Variables
  • Simulations
  • Statistical Decision Theory

Fields of Study

  • Mathematics

Readers

  • Computational Modeling and Simulation
  • Space Exploration and Orbital Mechanics.
  • Theoretical Analysis.