Emitter Signal Waveform Classification Based on Autocorrelation and Time-Frequency Analysis

Ma, Zhiyuan; Huang, Zhi Gao; Lin, Anni

doi:10.3390/electronics8121419

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…Directly performing CWD time-frequency analysis to the received unstable pulse-modulated signal ( ) y t can obtain the original TFI, but the original TFI characterizes the feature of the noise signal, too. Now, seeks a feature representation that can highlight the received useful signal [36] and signal recovery [37].…”

Section: ) Short-time Autocorrelation Feature Image Databasementioning

confidence: 99%

“…It should be further explained that the SAFI of radar signal used in this section is obtained by autocorrelation sequence with CWD time-frequency analysis. some related knowledge the signal autocorrelation value such as the deviation analysis the sample point parameters and the clipping rules are all introduced in 3.1.4 of the literature [36] completely and in detail.…”

Section: ) Short-time Autocorrelation Feature Image Databasementioning

confidence: 99%

“…And for an image-classifier of radar emitter, it reflects signal characteristics through extracting the shape feature of images [32]. The short-time autocorrelation characteristic image database can through the study of the autocorrelation of signal processing of deterministic signal intercept the signal feature, eliminate noise interference [36][37] [38] [42]. Fusion of different datasets is a potential solution to improve signal classification accuracy and anti-noise performance.…”

Section: Introductionmentioning

confidence: 99%

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Unknown Radar Waveform Recognition Based on Transferred Deep Learning

Lin

Huang

et al. 2020

IEEE Access

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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.

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Section: ) Short-time Autocorrelation Feature Image Databasementioning

confidence: 99%

Section: ) Short-time Autocorrelation Feature Image Databasementioning

confidence: 99%