Radar HRRP Target Recognition Based on Deep Multi-Scale 1D Convolutional Neural Network

Chen GUO; Tao JIAN; Congan XU; You HE; Shun SUN

doi:10.11999/JEIT180677

Volume 41 Issue 6

Jun. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(6): 1302-1309

Chen GUO, Tao JIAN, Congan XU, You HE, Shun SUN. Radar HRRP Target Recognition Based on Deep Multi-Scale 1D Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2019, 41(6): 1302-1309. doi: 10.11999/JEIT180677

Citation:

Chen GUO, Tao JIAN, Congan XU, You HE, Shun SUN. Radar HRRP Target Recognition Based on Deep Multi-Scale 1D Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2019, 41(6): 1302-1309. doi: 10.11999/JEIT180677

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Radar HRRP Target Recognition Based on Deep Multi-Scale 1D Convolutional Neural Network

doi: 10.11999/JEIT180677 cstr: 32379.14.JEIT180677

Naval Aviation University, Yantai 264001, China

Funds: The National Natural Science Foundation of China (61471379, 61790551, 61102166), The Taishan Scholar Project of Shandong Province

Received Date: 2018-07-06
Rev Recd Date: 2019-01-10

Available Online: 2019-01-22

Publish Date: 2019-06-01

Abstract

Abstract

In order to meet the demand for high real-time and high generalization performance of radar recognition, a radar High Resolution Range Profile (HRRP) recognition method based on deep multi-scale one dimension convolutional neural network is proposed. The multi-scale convolutional layer that can represent the complex features of HRRP is designed based on two features of the convolution kernels which are weight sharing and extraction of different fineness features from different scales, respectively. At last, the center loss function is used to improve the separability of features. Experimental results show that the model can greatly improve the accuracy of the target recognition under non-ideal conditions and solve the problem of the target aspect sensitivity, which also has good robustness and generalization performance.
- Radar target recognition,
- High Resolution Range Profile (HRRP),
- Multi-scale,
- Convolutional Neural Network (CNN),
- Center loss

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References(19)

References

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