Identification of radar signals using neural network classifier with low-discrepancy optimisation

Nedyalko Petrov, Ivan Jordanov, Jon Roe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A hybrid low-discrepancy sequences optimisation approach is investigated and used for training neural network classifiers for recognition of radar signal emitters. Two sample case studies are developed in order to demonstrate and evaluate the presented approach. In the first one, generic intercepted radar signals are classified in two broad classes-with civil or military application, based on patterns in their pulse trains, whereas in the second one the classifier is trained to distinguish between several more specific radar functions. Very competitive results of about 84% accuracy are achieved on the testing data sets.

Original languageEnglish
Title of host publication2013 IEEE Congress on Evolutionary Computation, CEC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2658-2664
Number of pages7
ISBN (Electronic)9781479904549, 9781479904525
ISBN (Print)9781479904532
DOIs
Publication statusPublished - 15 Jul 2013
Event2013 IEEE Congress on Evolutionary Computation, CEC 2013 - Cancun, Mexico
Duration: 20 Jun 201323 Jun 2013

Publication series

NameIEEE Congress on Evolutionary Computation
PublisherIEEE
ISSN (Print)1089-778X
ISSN (Electronic)1941-0026

Conference

Conference2013 IEEE Congress on Evolutionary Computation, CEC 2013
Country/TerritoryMexico
CityCancun
Period20/06/1323/06/13

Keywords

  • global optimisation
  • neural network classification
  • radar signals identification
  • supervised learning

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