With the development of technology, the intrapulse modulation technology of radar signal is becoming more and more complex. In this paper, a method based on multilayer convolutional neural network (MCNN) is proposed to identify the intrapulse modulation mode. Firstly, five signal modulation modes (conventional pulse signal, linear frequency modulation signal, sinusoidal frequency modulation signal, phase shift keying signal and frequency shift keying signal) are established. By changing the values of carrier frequency, pulse width and repetition frequency, five kinds of training data are generated with a certain signal-to-noise ratio (SNR). Then, five trained MCNNs are obtained by training five kinds of training data with a MCNN. Further, the recognition performance of different trained MCNNs is studied with the test data generated under different SNRs. Finally, the simulation shows that the mode with parameter variations has the best recognition performance.
Aiming at the high computational complexity of radon ambiguity transform, this paper proposes a sortie resolution method of radon ambiguity transform based on golden section iteration. Firstly, the high computational complexity of radon ambiguity transform is analyzed, and then the golden section iterative algorithm is used to improve radon ambiguity transform. This method greatly reduces the algorithm complexity. The Doppler frequency modulation slope of the formation target is estimated by an iterative algorithm, and then the echo signal is phase compensated. Finally, the formation target sorties can be distinguished in the frequency domain.
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