Sets with incomplete and missing data NN radar signal classification

Ivan Jordanov*, Nedyalko Petrov

*Corresponding author for this work

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

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We investigate further the problem of radar signal classification and source identification with neural networks. The available large dataset includes pulse train characteristics such as signal frequencies, type of modulation, pulse repetition intervals, scanning type, scan period, etc., represented as a mixture of continuous, discrete and categorical data. Typically, considerable part of the data samples is with missing values. In our previous work we used only part of the radar dataset, applying listwise deletion to get rid of the samples with missing values and processed relatively small subset of complete data. In this work we apply multiple imputation (MI) method, which is a model based approach of dealing with missing data, by producing confidence intervals for unbiased estimates without loss of statistical power (using both complete and incomplete cases). We employ MI to all data samples with up to 60% missingness, this way increasing more than twice the size of the initially used data subset. We apply feedforward backpropagation neural network (NN) supervised learning for solving the classification and identification problem and investigate and critically compare the same three case studies, researched in the previous paper and report improved, superior results, which is a consequence of the implemented MI and improved NN training.

Original languageEnglish
Title of host publicationProceedings of the 2014 International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)978-1479914845
Publication statusPublished - 4 Sept 2014
Event2014 International Joint Conference on Neural Networks - Beijing, China
Duration: 6 Jul 201411 Jul 2014


Conference2014 International Joint Conference on Neural Networks
Abbreviated titleIJCNN 2014


  • missing data
  • multiple imputation
  • neural networks
  • radar signal classification
  • supervised learning


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