Ambiguity Function Based Radar Waveform Classification and Unsupervised Adaptation Using Deep CNN Models

We present a robust generalized approach to phase and frequency modulated LPI Radar waveform classification and adaptation, inspired by deep convolutional neural architectures. We use a complex Ambiguity Function matrix as a pre-processing step, following which, a waveform classification, or adaptation to unlabeled reference target domains, is performed. We test our method on a wide range of tasks, datasets, and different signal distributions. Our method surpasses the state-of-the-art performance on classification problems on multi-encoding, multi-feature datasets, in diverse and challenging conditions. Our novel approach to an unlabeled Radar waveform adaptation reveals impressive classification improvements to domain shifted unlabeled signals.

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

[2]  David A. Shnidman.  MATLAB Code for Plotting Ambiguity Functions , 2002 .

[3]  Issam H. Laradji,et al.  M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning , 2018, Domain Adaptation for Visual Understanding.

[4]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[5]  Brian D. Rigling,et al.  ACF-based classification of phase modulated waveforms , 2010, 2010 IEEE Radar Conference.

[6]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[8]  Trevor Darrell,et al.  Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Ming Zhang,et al.  Convolutional Neural Networks for Automatic Cognitive Radio Waveform Recognition , 2017, IEEE Access.

[10]  Aly El Gamal,et al.  Deep neural network architectures for modulation classification , 2017, 2017 51st Asilomar Conference on Signals, Systems, and Computers.

[11]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[12]  Pedro H. O. Pinheiro,et al.  Unsupervised Domain Adaptation with Similarity Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[13]  Victor S. Lempitsky,et al.  Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.

[14]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[15]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[16]  Brian D. Rigling,et al.  Phase modulated radar waveform classification using quantile one-class SVMs , 2015, 2015 IEEE Radar Conference (RadarCon).

[17]  Chao Wang,et al.  Automatic radar waveform recognition based on time-frequency analysis and convolutional neural network , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[18]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[19]  Ming Zhang,et al.  Neural Networks for Radar Waveform Recognition , 2017, Symmetry.

[20]  Manohar Paluri,et al.  Metric Learning with Adaptive Density Discrimination , 2015, ICLR.

[21]  Brian Rigling,et al.  Ambiguity-based classification of phase modulated radar waveforms , 2016, 2016 IEEE Radar Conference (RadarConf).

[22]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[23]  Ming Zhang,et al.  LPI Radar Waveform Recognition Based on Time-Frequency Distribution , 2016, Sensors.

[24]  Orhan Arikan,et al.  Classification of Intra-Pulse Modulation of Radar Signals by Feature Fusion Based Convolutional Neural Networks , 2018, 2018 26th European Signal Processing Conference (EUSIPCO).

[25]  Simon Haykin,et al.  GradientBased Learning Applied to Document Recognition , 2001 .