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Multi-Resolution Fusion Convolutional Neural Networks for Intrapulse Modulation LPI Radar Waveforms Recognition

Xue NI, Huali WANG, Ying ZHU, Fan MENG

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Summary :

Low Probability of Intercept (LPI) radar waveform has complex and diverse modulation schemes, which cannot be easily identified by the traditional methods. The research on intrapulse modulation LPI radar waveform recognition has received increasing attention. In this paper, we propose an automatic LPI radar waveform recognition algorithm that uses a multi-resolution fusion convolutional neural network. First, signals embedded within the noise are processed using Choi-William Distribution (CWD) to obtain time-frequency feature images. Then, the images are resized by interpolation and sent to the proposed network for training and identification. The network takes a dual-channel CNN structure to obtain features at different resolutions and makes features fusion by using the concatenation and Inception module. Extensive simulations are carried out on twelve types of LPI radar waveforms, including BPSK, Costas, Frank, LFM, P1~P4, and T1~T4, corrupted with additive white Gaussian noise of SNR from 10dB to -8dB. The results show that the overall recognition rate of the proposed algorithm reaches 95.1% when the SNR is -6dB. We also try various sample selection methods related to the recognition task of the system. The conclusion is that reducing the samples with SNR above 2dB or below -8dB can effectively improve the training speed of the network while maintaining recognition accuracy.

Publication
IEICE TRANSACTIONS on Communications Vol.E103-B No.12 pp.1470-1476
Publication Date
2020/12/01
Publicized
2020/06/15
Online ISSN
1745-1345
DOI
10.1587/transcom.2019EBP3262
Type of Manuscript
PAPER
Category
Sensing

Authors

Xue NI
  Army Engineering University of PLA
Huali WANG
  Army Engineering University of PLA
Ying ZHU
  Army Engineering University of PLA
Fan MENG
  Nanjing Marine Radar Institute

Keyword