Abstract

Electronic Support Measures consist of passive receivers which can identify emitters which, in turn, can be related to platforms that belong to 3 classes: Friend, Neutral, or Hostile. Decision makers prefer results presented in STANAG 1241 allegiance form, which adds 2 new classes: Assumed Friend, and Suspect. Dezert-Smarandache (DSm) theory is particularly suited to this problem, since it allows for intersections between the original 3 classes. However, as we know, the DSm hybrid combination rule is highly complex to execute and requires high amounts of resources. We have applied and studied a Matlab implementation of Tessem's k-l-x, Lowrance's Summarization and Simard's approximation techniques in the DSm theory for the fusion of ESM reports. Results are presented showing that we can improve on the time of execution while maintaining or getting better rates of good decisions in some cases.

Highlights

  • In terms of classification, the Dezert-Smarandache theory (DSmT) can become quite useful, especially for the direct resolution of classification for cases of hierarchical classes structures

  • It should be noted that the original form of the DSm hybrid combination rule (DSmH) tends to accumulates masses to intersections as is the case for any rule based on conjunction [14]

  • For more on the behavior of the DSmH on similar cases the reader is referred to [14, 15, 16], as we are focused on exploring the effect of approximations on DSm here

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Summary

Introduction

The Dezert-Smarandache theory (DSmT) can become quite useful, especially for the direct resolution of classification for cases of hierarchical classes structures. The DSmT is able to output to any of those classes without modifications to its fusion process. This example is still a simple one and both DSmT theories, with or without approximation, can solve it quite which wouldn’t be the case for classification problems of higher dimension. That is a classification of a problem having six main classes and up to, in the worst case scenario, a total of 7,828,353 possible derived classes. We study the use of an approximation technique to restrain the staggering amount of data that the DSmT can generate in its fusion process. We will compare the good decision rate in the two cases, with and without the use of approximation

Realistic Case Study
Dezert-Smarandache Theory
Lowrance’s approximation
Implementation of approximations
A typical simulation scenario
Results for the simulated scenario
Effects of varying the k-l-x parameters
Monte-Carlo Simulations with k-l-x approximation
Monte-Carlo simulations using various approximation rules
Conclusions
Full Text
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