Radar High-resolution Range Profile Target Recognition Based on Attention Mechanism and Bidirectional Gated Recurrent

LIU Jiaqi; CHEN Bo; JIE Xi

doi:10.12000/JR19014

Volume 8 Issue 5

Oct. 2019

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Article Navigation > Journal of Radars > 2019 > 8(5): 589-597

LIU Jiaqi, CHEN Bo, and JIE Xi. Radar high-resolution range profile target recognition based on attention mechanism and bidirectional gated recurrent[J]. Journal of Radars, 2019, 8(5): 589–597. doi: 10.12000/JR19014

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LIU Jiaqi, CHEN Bo, and JIE Xi. Radar high-resolution range profile target recognition based on attention mechanism and bidirectional gated recurrent[J]. Journal of Radars, 2019, 8(5): 589–597. doi: 10.12000/JR19014

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Radar High-resolution Range Profile Target Recognition Based on Attention Mechanism and Bidirectional Gated Recurrent

DOI: 10.12000/JR19014

LIU Jiaqi^1
,,
CHEN Bo^{1
,
,},
JIE Xi^2
,

1.
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
2.
School of Computer Science and Technology, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (61771361, 61701379), The National Science Fund for Distinguished Young Scholars (61525105)

More Information

Corresponding author: CHEN Bo, bchen@mail.xidian.edu.cn
Received Date: 2019-01-29
Rev Recd Date: 2019-04-07

Available Online: 2019-05-24

Publish Date: 2019-10-01

Abstract

Abstract

To address the problem of radar High-Resolution Range Profile (HRRP) target recognition, traditional methods only consider the envelope information of the sample and ignore the temporal correlation between the range cells. In this study, we propose a bidirectional self-recurrent neural network model based on an attention mechanism. The model divides the HRRP data in the time domain into two sequences, i.e., forward and backward using a sliding window, then extracts the features through two independent GRU networks, and splices the extracted features simultaneously, thus utilizing the bidirectional temporal information of HRRP. Considering that sequences at different moments have different degrees of importance to the target classification, different attention weights are assigned to the hidden layer features at each moment. Finally, the model uses the hidden features weighted summation to obtain target recognition and classification result. Experimental results show that the proposed method can effectively solve the target recognition problem of HRRP, and that the target area can still be accurately identified when the time shift occurs.
- Radar Automatic Target Recognition (RATR),
- High-Resolution Range Profile (HRRP),
- Gated Recurrent Unit (GRU),
- Attention mechanism,
- Time-shift sensitivity

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References

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