Attention-based Recurrent Neural Network Model for Radar High-resolution Range Profile Target Recognition

XU Bin; CHEN Bo; LIU Hongwei; JIN Lin

doi:10.11999/JEIT161034

Volume 38 Issue 12

Jan. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(12): 2988-2995

XU Bin, CHEN Bo, LIU Hongwei, JIN Lin. Attention-based Recurrent Neural Network Model for Radar High-resolution Range Profile Target Recognition[J]. Journal of Electronics & Information Technology, 2016, 38(12): 2988-2995. doi: 10.11999/JEIT161034

Citation:

XU Bin, CHEN Bo, LIU Hongwei, JIN Lin. Attention-based Recurrent Neural Network Model for Radar High-resolution Range Profile Target Recognition[J]. Journal of Electronics & Information Technology, 2016, 38(12): 2988-2995. doi: 10.11999/JEIT161034

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Attention-based Recurrent Neural Network Model for Radar High-resolution Range Profile Target Recognition

doi: 10.11999/JEIT161034

Funds:

The National Science Fund for Distinguished Young Scholars (61525105), The National Natural Science Foundation of China (61201292, 61322103, 61372132), The Program for New Century Excellent Talents in University (FANEDD-201156)

Received Date: 2016-10-08
Rev Recd Date: 2016-11-25
Publish Date: 2016-12-19

Abstract

Abstract

To improve the performance of radar High-Resolution Range Profile (HRRP) target recognition, a new attention-based model is proposed based on time domain feature. This architecture encodes the time domain feature which can reveal the correlation inside the target with Recurrent Neural Network (RNN). Then, this model gives a weight to each part and sums the hidden feature with each weight for the final recognition. Experiments based on measured data show that the attention-based model is effective for radar HRRP recognition. Furthermore, the proposed method can still find the support areas even with the removed test data.
- Radar Automatic Target Recognition (RATR),
- High-Resolution Range Profile (HRRP),
- Recurrent Neural Network (RNN),
- Attention-based model

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References(18)

References

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