Accession Number:

ADA399420

Title:

A Comparison of Data Fusion, Neural Network and Statistical Pattern Recognition Technologies to a Multi-Sensor Target ID and Classification Problem

Descriptive Note:

Conference proceedings

Corporate Author:

LOCKHEED MARTIN AERONAUTICAL SYSTEMS MARIETTA GA

Personal Author(s):

Report Date:

1998-01-01

Pagination or Media Count:

17.0

Abstract:

It has been widely known that data fusion, neural network and statistical pattern recognition technologies can be applied to target identification and classification problems. The main objective of this paper is to find out which of these techniques would be easy to use and provide acceptable results. We had selected the Multi-sensor Correlation Model 1 from the field of data fusion technology. The concept of this model is based on the coefficient of similarity. For target identification problem, one have to estimate the coefficient of similarity between a known target X and the target Y to be identified. If the coefficient is closed to one , then it implied that target Y is the same as target X, otherwise if the coefficient is close to zero, then it implied that target Y is not the same as target X. It is mathematical simple and easy to implement. The Bayesian Model 2 was selected from the field of statistical pattern recognition technology, This is a conditional probability model. For target identification problem, one have to calculate the posterior probability of a known target X given the target Y to one to be identified. If the conditional probability is close to one , then it implied that target x and target Y is the same, otherwise if it is close to zero, then it implied that targetX and targetY is not the same. This model required multivariate normal assumption, probability density function, and apriori probability of the targets. It is not easy to apply. The Backpropagation Model 3 was selected from the field of neural network technology, It is a three layered network input, hidden and output layers. For target identification problem, one has to train the network with the known target X, then apply the unknown targetY to the trained network as an input layer, if the output layer has a higher energy value , thentecWe use two published 4 numerical dat

Subject Categories:

  • Cybernetics
  • Target Direction, Range and Position Finding

Distribution Statement:

APPROVED FOR PUBLIC RELEASE