Quasi-LFM radar waveform recognition based on fractional Fourier transform and time-frequency analysis
Xie Cunxiang, Zhang Limin, Zhong Zhaogen
Abstract
Recent advances in electronics have increased the complexity of radar signal modulation. The quasi-linear frequency modulation (quasi-LFM) radar waveforms (LFM, Frank code, P1-P4 code) have similar time-frequency distributions, and it is difficult to identify such signals using traditional time-frequency analysis methods. To solve this problem, this paper proposes an algorithm for automatic recognition of quasi-LFM radar waveforms based on fractional Fourier transform and time-frequency analysis. First of all, fractional Fourier transform and the Wigner-Ville distribution (WVD) are used to determine the number of main ridgelines and the tilt angle of the target component in WVD. Next, the standard deviation of the target component's width in the signal's WVD is calculated. Finally, an assembled classifier using neural network is built to recognize different waveforms by automatically combining the three features. Simulation results show that the overall recognition rate of the proposed algorithm reaches 94.17% under 0 dB. When the training data set and the test data set are mixed with noise, the recognition rate reaches 89.93%. The best recognition accuracy is achieved when the size of the training set is taken as 400. The algorithm complexity can meet the requirements of real-time recognition.