Quadratic time–frequency analysis and sequential recognition for specific emitter identification

Abstract

Specific emitter identification (SEI) is a state-of-the-art in electronic warfare. The conventional methods for SEI hardly satisfy the modern electronic reconnaissance. In this study, first the authors study the quadratic time–frequency distributions and its slice features and noise analysis. Based on the time-frequency features, two sequential recognition methods based on probabilistic support vector machine (SVM) and iterative least-square estimation are studied, respectively. The proposed methods are able to reject the interference pulses and update the classifier or feature parameters, which accomplish the online recognition and online learning of specific radar emitter. Experiments on actual intercepted radar signals with the same type verify the correctness and validity of the proposed methods.