Low complexity radar signal classification based on spectrum shape - Liang YIN; Rui LIN; Xiaolei WANG; Yuliang YAO; Lin ZHOU; Yuan HE (Dianxin kexue)
In order to solve the problems of high computational complexity, low recognition accuracy of low signal to noise ratio (SNR) environment and low fidelity of simulation data in radar signal modulation recognition, a low complexity radar signal classification algorithm based on spectrum shape was proposed.Signal spectrum was normalized, feature parameters were extracted by spectrum sampling method, and then machine learning classification model was trained.The test results of the data generated by the radar signal source show that the classification accuracy of Barker code, Frank code, LFM code, BPSK, QPSK modulation and conventional radar signals is more than 90% (SNR≥3 dB).The algorithm has low computational complexity, can adapt to the change of signal parameters, and has good generalization.