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DOI: 10.14569/IJARAI.2012.010503
PDF

A Comparative Study on different AI Techniques towards Performance Evaluation in RRM(Radar Resource Management)

Author 1: Madhusudhan H S
Author 2: Khalid Nazim S.A

International Journal of Advanced Research in Artificial Intelligence(IJARAI), Volume 1 Issue 5, 2012.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: The multifunction radar (MFR) has to make a decision as to which functions are to be performed first or which must be degraded or even not done at all when there are not enough resources to be allocated. The process of making these decisions and determining their allocation as a function of time is known as Radar Resource Management (RRM). The RRM has two basic issues: task prioritization and task scheduling. The task prioritization is an important factor in the task scheduler. The other factor is the required scheduling time, which is decided by the environment, the target scenario and the performance requirements of radar functions. The required scheduling time could be improved by using advanced algorithm [1, 6].

Keywords: Radar; RRM (Radar Resource Management); Artificial Intelligence (AI); Neural Network (NN); Fuzzy Logic (FL).

Madhusudhan H S and Khalid Nazim S.A, “A Comparative Study on different AI Techniques towards Performance Evaluation in RRM(Radar Resource Management)” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 1(5), 2012. http://dx.doi.org/10.14569/IJARAI.2012.010503

@article{S2012,
title = {A Comparative Study on different AI Techniques towards Performance Evaluation in RRM(Radar Resource Management)},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2012.010503},
url = {http://dx.doi.org/10.14569/IJARAI.2012.010503},
year = {2012},
publisher = {The Science and Information Organization},
volume = {1},
number = {5},
author = {Madhusudhan H S and Khalid Nazim S.A}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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