基于注意循环神经网络模型的雷达高分辨率距离像目标识别

徐彬; 陈渤; 刘宏伟; 金林

doi:10.11999/JEIT161034

基于注意循环神经网络模型的雷达高分辨率距离像目标识别

doi: 10.11999/JEIT161034

基金项目:

国家杰出青年科学基金(61525105)，国家自然科学基金(61201292, 61322103, 61372132)，全国优秀博士学位论文作者专项资金(FANEDD-201156)

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- 被引次数: 0
出版历程
- 收稿日期: 2016-10-08
- 修回日期: 2016-11-25
- 刊出日期: 2016-12-19

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

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)

摘要

摘要: 针对雷达高分辨率距离像(HRRP)数据的识别问题，该文利用HRRP生成的时序特性，提出一种基于循环神经网络的注意模型。该模型利用具有记忆功能的循环神经网络对时域数据进行编码，并根据HRRP中不同距离单元所映射的隐层对目标识别的重要性，自适应地赋予隐层不同的权值系数，并根据隐层特征编码特征进行HRRP目标识别。该模型利用了隐藏在HRRP数据内部的目标结构信息，提高了特征的区分度。实测数据的实验结果表明，该方法可以有效地进行识别，在样本存在一定余度数据和样本偏移的情况下，都能准确地找出目标支撑区域。
- 雷达目标识别 /
- 高分辨距离像 /
- 循环神经网络 /
- 注意模型
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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参考文献(18)

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