Dynamic Curve Fitting and BP Neural Network With Feature Extraction for Mobile Specific Emitter Identification

Abstract

As a critical technology in both civil and military fields, specific emitter identification (SEI) can identify signal sources according to their various features. Existing methods on SEI are mostly based on the prior knowledge of emitters, which are powerless in the non-cooperative scenario. In order to realize the unsupervised identification, the mobile SEI method based on fingerprint set construction and feedback classification algorithm is proposed in this paper. The proposed method first divides signal fingerprints into static features and dynamic features, where the former describe the inherent features of emitters, and the latter represent the moving state features of emitters. Then, the feedback classification algorithm composed of dynamic curve fitting and back propagation (BP) neural network is applied in the classification of signals. The dynamic curve accomplishes the first classification and the results with high credibility are used to train the BP neural network which accomplishes the final classification. Simulation results demonstrate that the proposed method can complete the identification of mobile specific emitter sources in the unsupervised state with more than 95% identification rate.