![]() ![]() Yibing Li, Yanhuan Wang, Yun Lin, "Recognition of Radar Signals Modulation Based on Short Time Fourier Transform and Reduced Fractional Fourier Transform," Journal of Information &Computational Science, vol.Glauber Ribeiro Pereiraa,Liliam Fernandes de Oliveirab, "Reducing cross terms effects in the Choi-Williams transform of mioelectric signals," Elsevier North-Holland, vol.Chang Xu, Cong Wang, Wei Liu, "Nonstationary Vibration Signal Analysis Using Wavelet-Based Time-Frequency Filter and Wigner-Ville Distribution," Journal of Vibration &Acoustics,vol.138, Jun.2016.Lütfiye Durak, Orhan Arıkan, "Short-time Fourier transform: Two fundamental properties and an optimal implementation," IEEE Transactions on Signal Processing, vol.Xiao LeQun, Zhang YuLing, Zhao YongJun, "Radar signal feature extraction based on wavelet ridge and high order spectral analysis," IET International Radar Conference(IRC2009), Apr.2009.Wang Xing, Zhou Yipeng, Zhou Dongqing, Chen Zhonghui, Tian Yuanrong, "Research on Low Probability of Intercept Radar Signal Recognition Using Deep Belief Network and Bispectra Diagonal Slice," Journal of Electronics & Information Technology.Terauds, "CW Doppler Radar Based Land Vehicle Speed Measurement Algorithm Using Zero Crossing and Least Squares Method," Electronics Conference (BEC2012), October. Zhang Guo-zhu, Huang Ke-sheng, Jiang, Wen-li, "Emitter Feature Extract Method Based on Signal Envelope," Journal of Systems Engineering and Electronics, vol.The simulation results show that the overall correct recognition rate(CRR) of radar signals is 92.5% at -6dB that higher than existing methods. Last but not least, The model will extract feature and classify 10 type of radar signals include that CW, LFM, NLFM, BPSK, Costas, Frank, T1, T2, T3 and T4. After that, employing TensorFlow frame and GPU to built improved-AlexNet and that accelerate the training of model. Next, to fit for input of the model size selected later and weaken the influence of noise, time-frequency images must be denoised and clipped processing by wavelet threshold filtering and bi-cubic interpolation. First of all, the time-frequency images of radar signals are accessed by time-frequency analysis of SPWVD. In order to solve the problem that low recognition rate and less signal type of low probability of intercept (LPI) radar signal at -6dB of low signal-to-noise ratio (SNR), the paper presents a method based on Smooth Pseudo Wigner-Ville distribution (SPWVD) for signal time-frequency analyze and an improved-AlexNet deep convolutional neural network (DCNN) model for low probability of intercept radar signal to classification. ![]()
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