Automatic Modulation Recognition for Radar Signals via Multi-Branch ACSE Networks

Automatic modulation recognition (AMR) for radar signals plays a significant role in electronic warfare. Conventional recognition methods may suffer from the recognition accuracy and the computation complexity under low signal-to-noise ratio (SNR) conditions. In this paper, a novel multi-branch Asymmetric Convolution Squeeze-and-Excitation (ACSE) networks using multi-domain features and fusion strategy based on a support vector machine is proposed to recognize eight kinds of radar signals. First, features of radar signals in the frequency domain, the autocorrelation domain, and the time-frequency domain are extracted. Then the obtained multi-domain features are converted as the input of the proposed networks which owns the representational power and learning ability. Finally, the outputs of multi-branch ACSE networks are fused via the fusion strategy to obtain the final results. Via simulations, the robustness and effectiveness of the fusion strategy are verified. The results on the simulation dataset prove that the proposed method can achieve more than 93% accuracy at -10dB for all modulations. Compared with four newly proposed networks, the multi-branch ACSE networks achieves better performance under low SNR conditions. And the results on measured signals show that the proposed method outperforms other comparison methods, especially for binary frequency-shift keying (BFSK) signals.

[1]  Xing Wang,et al.  A novel radar signal recognition method based on a deep restricted Boltzmann machine , 2017 .

[2]  Hongyi Yu,et al.  Modulation Classification Based on Spectral Correlation and SVM , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[3]  Kemal Polat,et al.  Deep long short-term memory networks-based automatic recognition of six different digital modulation types under varying noise conditions , 2019, Neural Computing and Applications.

[4]  Adam J. Elbirt.  Information warfare: are you at risk? , 2003, IEEE Technol. Soc. Mag..

[5]  Quoc V. Le,et al.  EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.

[6]  Y. Yao,et al.  On Early Stopping in Gradient Descent Learning , 2007 .

[7]  Richard G. Wiley.  Electronic Intelligence: The Interception of Radar Signals , 1985 .

[8]  Yang Liu,et al.  An Ensemble Convolutional Neural Networks for Bearing Fault Diagnosis Using Multi-Sensor Data , 2019, Sensors.

[9]  Yong Xiaoju.  Research on recognizing the radar signal using the bispectrum cascade feature , 2012 .

[10]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Dong-Seong Kim,et al.  MCNet: An Efficient CNN Architecture for Robust Automatic Modulation Classification , 2020, IEEE Communications Letters.

[12]  Qilong Wang,et al.  ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  P. Misans,et al.  CW doppler radar based land vehicle speed measurement algorithm using zero crossing and least squares method , 2012, 2012 13th Biennial Baltic Electronics Conference.

[14]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Hao Wu,et al.  Convolutional neural network and multi‐feature fusion for automatic modulation classification , 2019, Electronics Letters.

[16]  Zunwen He,et al.  A Novel Attention Cooperative Framework for Automatic Modulation Recognition , 2020, IEEE Access.

[17]  Hong Sun,et al.  Automatic modulation classification using stacked sparse auto-encoders , 2016, 2016 IEEE 13th International Conference on Signal Processing (ICSP).

[18]  H. Robbins.  A Stochastic Approximation Method , 1951 .

[19]  Yiyang Pei,et al.  Deep Neural Network for Robust Modulation Classification Under Uncertain Noise Conditions , 2020, IEEE Transactions on Vehicular Technology.

[20]  Lifen Wang,et al.  Weighted Kalman filter phase unwrapping algorithm based on inSAR image , 2013 .

[21]  Jungong Han,et al.  ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[22]  Jian Yang,et al.  Automatic modulation recognition of compound signals using a deep multi-label classifier: A case study with radar jamming signals , 2020, Signal Process..

[23]  Yangyu Fan,et al.  Unsupervised feature learning and automatic modulation classification using deep learning model , 2017, Phys. Commun..