Radio ground-to-air interference signals recognition based on support vector machine

In this paper, we use support vector machine (SVM) to recognize the acoustic frequency of radio signals, in which, the SVM with the polynomial kernel function is optimized by gravitational search algorithm and selected as a classifier to recognize the acoustic frequency of radio signals, radio signals are the civil aviation radio ground-to-air interference signals, its the acoustic frequency are recognized by the optimized SVM classifier. Experiments show that our method is more accuracy and robustness than genetic algorithm to recognize radio ground-to-air interference signals.