Over-the-Air Radar Emitter Signal Classification Based on SDR

At present, in the field of radar emitter classification, theoretical simulation is mostly used to carry out algorithm research. However, there are few schemes to study signal classification in real electromagnetic environment using actual hardware. Therefore, this paper proposes a radar emitter classification scheme based on HackRF Software Defined Radio (SDR) and deep learning to solve the problem of weak engineering practice. Firstly, the GNU Radio development environment is used to realize the integration design of real space signal transceiver and time-frequency analysis algorithm application on HackRF hardware platform. Then, a classification model with 11 layers network is constructed to automatically extract the deep features of intra-pulse signal time-frequency image. Finally, the classification performance of eight kinds of signals in real electromagnetic environment is tested. The total recognition accuracy of this scheme is more than 83% under 6dB low Signal-to-Noise Ratio (SNR), which proves the effectiveness of the scheme, and provides an important basis for practical engineering application in the future.