This work investigates radar signal classification and source identification using three classification models: Neural Networks (NN), Support Vector Machines (SVM) and Random Forests (RF). The available large dataset consists of 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 contains missing values. In our previous work we used only part of the radar dataset, applying listwise deletion to clean the samples with missing values and processed relatively small subset of complete data. In this work we apply three different imputation techniques to deal with the missing data: multiple imputation (MI), K-Nearest Neighbour Imputation (KNNI) and Bagged Tree Imputation (BTI). We employ the imputation methods to all data samples with up to 60% missingness, this way increasing more than twice the size of the initially used data subset. Subsequently the three classifiers (NN, SVM, and RF) are employed and the results are analysed and critically compared based on their accuracy to assess the model with the best performance.