Feature Evaluation and Comparison in Radar Emitter Recognition Based on SAHP

In the field of radar emitter recognition, with the wide application of modern radar, the traditional recognition method based on typical five feature parameters cannot achieve satisfactory recognition results in a complex electromagnetic environment. Currently, many new feature extraction methods are presented, but few approaches have been applied for feature evaluation or performance comparison. To deal with this problem, a feature evaluation and selection method was proposed based on set pair analysis (SPA) theory and analytic hierarchy process (AHP). The main idea of this method is to use SPA theory to solve problems regarding the construction of the decision matrix based on AHP, as it relies heavily on expert's subjective experience. The aim was to improve the objectivity of the evaluation. To check the effectiveness of the proposed method, six feature parameters were selected for a comprehensive performance evaluation. Then, the convolutional neural network (CNN) was introduced to validate the recognition capability based on the evaluation results. Simulation results demonstrated that the proposed method could achieve the feature analysis and evaluation more reasonably and objectively.