Target classification based on radar track using bilstm
Target classification based on radar track using bilstm
- Author(s): H. Li 1 ; S. Luo 2 ; H. Wang 1 ; L. Zhang 1 ; W. Yanhua 1, 3 ; Y. Li 1, 3
- DOI: 10.1049/icp.2021.0793
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- Author(s): H. Li 1 ; S. Luo 2 ; H. Wang 1 ; L. Zhang 1 ; W. Yanhua 1, 3 ; Y. Li 1, 3
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View affiliations
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Affiliations:
1:
Radar Research Laboratory, School of Information and Electronics , Beijing Institute of Technology , Beijing , China ;
2: No. 203 Research Institute of China Ordnance Industries , Xi’an , China ;
3: Beijing Institute of Technology Chongqing Innovation Center , Chongqing , China
Source:
IET International Radar Conference (IET IRC 2020),
2021
p.
369 – 373
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Affiliations:
1:
Radar Research Laboratory, School of Information and Electronics , Beijing Institute of Technology , Beijing , China ;
- Conference: IET International Radar Conference (IET IRC 2020)
- DOI: 10.1049/icp.2021.0793
- ISBN: 978-1-83953-540-6
- Location: Online Conference
- Conference date: 04-06 November 2020
- Format: PDF
The track information is an important feature for radar target recognition. In this paper, the authors propose to classify targets based on radar track using bi-directional long short-term memory (BiLSTM) and self-attention mechanism. More specifically, the forward and backward operation in BiLSTM is able to capture temporal dependence of the track. The resulting deep resprentations of the track is refined by a self-attention mechanism to enhance the most discriminative parts. A fully connected layer followed by softmax operator is used to determine the type of the track. Experimental results show that the proposed method outperforms conventional algorithms.
Inspec keywords: radar target recognition; radar computing; recurrent neural nets; radar tracking
Subjects: Neural nets; Radar theory; Radar equipment, systems and applications; Communications computing