Intelligent radar signal recognition and classification

Ivan Nikolov Jordanov, Nedyalko Petrov

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review


This chapter investigates a classification problem for timely and reliable identification of radar signal emitters by implementing and following a neural network based approach. A large data set of intercepted generic radar signals, containing records of their pulse train characteristics (such as operational frequencies, modulation types, pulse repetition intervals, scanning period, etc.), is used for this research. Due to the nature of the available signals, the data entries consist of a mixture of continuous, discrete and categorical data, with a considerable number of records containing missing values.
To solve the classification problem, two separate approaches are investigated, implemented, tested and validated on a number of case studies. In the first approach, a listwise deletion is used to clean the data of samples containing missing values and then feed-forward neural networks are employed for the classification task. In the second one, a multiple imputation model-based method for dealing with missing data (by producing confidence intervals for unbiased estimates without loss of statistical power, i.e., by using all the available samples) is investigated. Afterwards, a feedforward backpropagation neural networks are trained to solve the signal classification problem. Each of the approaches is tested and validated on a number of case studies and the results are evaluated and critically compared.
Original languageEnglish
Title of host publicationRecent Advances in Computational Intelligence in Defence and Security
EditorsRami Abielmona, Rafael Falcon, Nur Zincir-Heywood, Hussein A. Abbass
Place of PublicationCham
ISBN (Print)978-3319264486
Publication statusPublished - 21 Dec 2015

Publication series

NameStudies in Compputational Intelligence
PublisherSpinger International Publishing
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503


  • radar signal recognition and classification, surveillance, neural networks, data analysis, multiple imputation, missing data
  • WNU


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