Accession Number : AD0764723
Title : A Statistical Decision Approach to Missile Payload Discrimination.
Descriptive Note : Technical note,
Corporate Author : MASSACHUSETTS INST OF TECH LEXINGTON LINCOLN LAB
Personal Author(s) : Krikelis,N. J.
Report Date : 18 JUL 1973
Pagination or Media Count : 23
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)
Descriptors : (*ANTIMISSILE DEFENSE SYSTEMS, DECISION MAKING), (*DECOYS, INTERCEPTION PROBABILITIES), (*REENTRY VEHICLES, INTERCEPTION PROBABILITIES), PAYLOAD, PENETRATION, PROBABILITY DENSITY FUNCTIONS, MONTE CARLO METHOD
Subject Categories : Antimissile Defense Systems
Distribution Statement : APPROVED FOR PUBLIC RELEASE