Abstract:

To solve the problem of jamming signals classification and recognition in transform domain communication system (TDCS), a jamming classification and recognition algorithm based on the signal feature space and support vector machine (SF-SVM) is proposed. Firstly, the signal feature space is built by the jamming signals feature extraction based on the jamming signals models and signal space theory. In order to solve the problems for binary classification and multi-class classification, the classification and recognition SF-SVM algorithm is proposed. Simulation results demonstrate that SF-SVM is superior to traditional classification algorithms in both classification accuracy and training speed, and they indicate the superiority for the new designed classifier and the improvement for TDCS performance when the signal to noise ratio (SNR) is above 8 dB.