IEEE Access (Jan 2021)

LPI Radar Waveform Recognition Based on Multi-Resolution Deep Feature Fusion

  • Xue Ni,
  • Huali Wang,
  • Fan Meng,
  • Jing Hu,
  • Changkai Tong

DOI
https://doi.org/10.1109/ACCESS.2021.3058305
Journal volume & issue
Vol. 9
pp. 26138 – 26146

Abstract

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Deep neural networks are used as effective methods for the Low Probability of Intercept (LPI) radar waveform recognition. However, existing models' performance degrades seriously at low Signal-to-Noise Ratios (SNRs) because the effective features extracted by the networks are insufficient under noise jamming. In this paper, we propose a multi-resolution deep feature fusion method for LPI radar waveform recognition. First, we apply the enhanced Fourier-based Synchrosqueezing Transform (FSST), which shows good performance at low SNRs, to convert radar signals into time-frequency images. Then, we construct a multi-resolution deep convolutional network to extract more deep features from each resolution channel. Next, we explore an interactive feature fusion strategy for deep feature fusion. By some down-sampling or up-sampling blocks, different resolution features are fused to generate new features. Finally, we apply a fusion algorithm to the fully connected layer to achieve classification fusion for better performance. Simulation experiments on twelve kinds of LPI radar waveforms show that the overall recognition accuracy of our method can reach 95.2% at the SNR of -8 dB. It is proved that our approach does indeed improve the recognition accuracy effectively at low SNRs.

Keywords