Sets with incomplete and missing data NN radar signal classification

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.