Radar intra–pulse signal modulation classification with contrastive learning

The existing research on deep learning for radar signal intra–pulse modulation classification is mainly based on supervised leaning techniques, which performance mainly relies on a large number of labeled samples. To overcome this limitation, a self–supervised leaning framework, contrastive learning (CL), combined with the convolutional neural network (CNN) and focal loss function is proposed, called CL––CNN. A two–stage training strategy is adopted by CL–CNN. In the first stage, the model is pretrained using abundant unlabeled time–frequency images, and data augmentation is used to introduce positive–pair and negative–pair samples for self–supervised learning. In the second stage, the pretrained model is fine–tuned for classification, which only uses a small number of labeled time–frequency images. The simulation results demonstrate that CL–CNN outperforms the other deep models and traditional methods in scenarios with Gaussian noise and impulsive noise–affected signals, respectively. In addition, the proposed CL–CNN also shows good generalization ability, i.e., the model pretrained with Gaussian noise–affected samples also performs well on impulsive noise–affected samples

LPI Radar Waveform Recognition Based on CNN and TPOT

The electronic reconnaissance system is the operational guarantee and premise of electronic warfare. It is an important tool for intercepting radar signals and providing intelligence support for sensing the battlefield situation. In this paper, a radar waveform automatic identification system for detecting, tracking and locating low probability interception (LPI) radar is studied. The recognition system can recognize 12 different radar waveform: binary phase shift keying (Barker codes modulation), linear frequency modulation (LFM), Costas codes, polytime codes (T1, T2, T3, and T4), and polyphase codes (comprising Frank, P1, P2, P3 and P4). First, the system performs time−frequency transform on the LPI radar signal to obtain a two-dimensional time−frequency image. Then, the time−frequency image is preprocessed (binarization and size conversion). The preprocessed time−frequency image is then sent to the convolutional neural network (CNN) for training. After the training is completed, the features of the fully connected layer are extracted. Finally, the feature is sent to the tree structure-based machine learning process optimization (TPOT) classifier to realize offline training and online recognition. The experimental results show that the overall recognition rate of the system reaches 94.42% when the signal-to-noise ratio (SNR) is −4 dB

LPI Radar Waveform Recognition Based on CNN and TPOT

The electronic reconnaissance system is the operational guarantee and premise of electronic warfare. It is an important tool for intercepting radar signals and providing intelligence support for sensing the battlefield situation. In this paper, a radar waveform automatic identification system for detecting, tracking and locating low probability interception (LPI) radar is studied. The recognition system can recognize 12 different radar waveform: binary phase shift keying (Barker codes modulation), linear frequency modulation (LFM), Costas codes, polytime codes (T1, T2, T3, and T4), and polyphase codes (comprising Frank, P1, P2, P3 and P4). First, the system performs time–frequency transform on the LPI radar signal to obtain a two-dimensional time–frequency image. Then, the time–frequency image is preprocessed (binarization and size conversion). The preprocessed time–frequency image is then sent to the convolutional neural network (CNN) for training. After the training is completed, the features of the fully connected layer are extracted. Finally, the feature is sent to the tree structure-based machine learning process optimization (TPOT) classifier to realize offline training and online recognition. The experimental results show that the overall recognition rate of the system reaches 94.42% when the signal-to-noise ratio (SNR) is −4 dB