Modulation recognition algorithm based on image deep learning

Aiming at the low Detection efficiency of modulation recognition algorithms based on deep learning, an efficient modulation recognition algorithm RadioFSDet(Radio Frequency Spectrum Detection) was proposed. RadioFSDet algorithm uses target detection algorithm YOLOv4 to detect modulated signals on the spectrum map according to the characteristic differences of signals on the spectrum map. Compared with the mainstream modulation recognition algorithm based on deep learning, RadioFSDet algorithm can not only detect the modulation categories of multiple signals in a single forward inference, but also roughly determine the center frequency of each signal. The experimental results show that the RadioFSDet algorithm achieves good detection of AM, FM, GSM and QPSK signals in VHF data set collected in the real scene, with an average detection accuracy of 71%. At the same time, in the experiment of RadioML2016 public data set, RadioFSDet algorithm achieves 87% average detection accuracy for AM, FM and QPSK signals with SNR of 0~18dB. In addition, in order to further accelerate the detection speed of RadioFSDet algorithm, combined with the latest research results in the field of computer vision, this paper proposes an efficient lightweight detection network RadioFSNet, the number of parameters of the network not only from the original 64 million to 2.2 million, and the detection accuracy of the model will not decrease. Experimental results show that the detection speed of RadioFSNet can reach 77FPS in the VHF data set, and 231 signals can be detected per second on average, greatly improving the detection efficiency of the model.