Unknown Radar Waveform Recognition Based on Transferred Deep Learning

Abstract

Radar signals are emerging constantly for urgent task because of its complex patterns and rich working modes. For some radar waveforms with known modulation methods, they can be identified by correlation between radar prior knowledge and the received signals by the reconnaissance receiver. As for the unknown radar signals, how to identify unknown radar waveforms under the condition of limited samples and low signal-to-noise ratio is a challenging problem. Aiming at the learning ability of the deep features of the image by the convolutional neural network (CNN), the reconstructed features of the timefrequency image (TFI) of the known and unknown radar waveform signals have been excavated. A decision fusion unknown radar signal identification model based on transfer deep learning and linear weight decision fusion is designed in this paper. Firstly, the CNN is trained using the known radar signals; Then, based on the transfer learning, the neurons obtained from the multiple underlying the CNN are used to represent the reconstruction feature; Finally, the performance of the single random forest classifier of the original TFI and short-time autocorrelation features images (SAFI)are fused, the identification decision of unknown signals is realized by setting linear weight to the two databases. The recognition rate of unknown new classes for small samples exceeds 80.31%, and the classification accuracy rate for known radar waveform reach more than 99.15%. INDEX TERMS Unknown radar waveform recognition, convolutional neural network, decision fusion, transfer learning, random forest.

Self-Supervised Representation Learning for Quasi-Simultaneous Arrival Signal Identification Based on Reconnaissance Drones

“…However, QSAS is not simply a combination of different modulations and contains a lot of noise. Therefore, it is crucial to explore how to train the network and perform transfer learning (TL) [35,36] to adapt to different signal-to-noise ratios (SNR) [37,38] and improve noise adaptation.…”

Radar Intra–Pulse Signal Modulation Classification with Contrastive Learning

“…One of the most popular FSL methods is transfer learning [22], which has been widely used in many fields, such as image classification [23] and text classification [24]. Some radar intra-pulse signal modulation classification algorithms based on transfer learning have also been proposed [25][26][27][28]. It has been found that the time-frequency image-based algorithms provide higher classification accuracy than the raw signal-based ones.…”

New Optimization Method Based on Neural Networks for Designing Radar Waveforms With Good Correlation Properties

“…A deep learning framework for synthetic aperture radar automatic target recognition has been presented [23]- [25]. Moreover, a method combining CNNs and signal time-frequency transformation has been proposed to complete the classification and recognition of various radar signals, which acquire high accuracy under low signal-to-noise ratio [26]- [28].…”