A Method for Recognizing Complex Communication Navigation Signals Based on Neural Network

Electromagnetic spectrum resources have great economic, society and military value and are one of the important strategic resources of the country. With the rapid development of wireless technologies such as communication and Internet of Things, and the gradual deepening informatization, spectrum space is becoming more and more crowded. How to monitor and manage the electromagnetic spectrum intelligently and efficiently has become a major challenge in the development of radio technology. Existing signal recognition methods has a series of problems, such as small application range, poor generalization ability and few recognition types, which make it difficult to meet the needs of application scenarios. To address the above problems, this paper proposes a signal feature extraction recognition algorithm based on Dropout-DNN fused with wavelet and higher-order cumulant (WH-D-DNN), which firstly extracts the spectral features of the signal by using the short-time Fourier transform, and then introduces the wavelet transform and higher-order cumulant algorithms to extract the smooth and higher-order information of the signal, and finally adopts the Dropout strategy to train the DNN neural network for signal classification and recognition. Experimental results show that the algorithm can recognize 40 communication navigation signals such as BDS, GPS, 4G, WIFI, FM and radar, with 99.4% correct rate when the SNR is higher than 10dB.