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Target-Aware Recurrent Attentional Network for Radar HRRP Target Recognition.

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Title: Target-Aware Recurrent Attentional Network for Radar HRRP Target Recognition.
Authors: Xu, Bin1,2, Chen, Bo1,2 bchen@mail.xidian.edu.cn, Wan, Jinwei1,2, Liu, Hongwei1,2, Jin, Lin1,2
Source: Signal Processing. Feb2019, Vol. 155, p268-280. 13p.
Subjects: Radar in navigation, High resolution imaging, Time-domain analysis, Scattering (Physics), Spectrographs
Abstract: Highlights • RNN can learn discriminative HRRP features by considering the temporal correlation. • Spectrogram containing phase information is more suitable for HRRP ATR. • The attention mechanism can automatically focus on the discriminative target areas. • Combining attention mechanism and RNN alleviates the time-shift sensitivity of HRRP. Abstract In this paper, we develop a Target-Aware Recurrent Attentional Network (TARAN) for Radar Automatic Target Recognition (RATR) based on High-Resolution Range Profile (HRRP) to make use of the temporal dependence and find the informative areas in HRRP, since it reflects the distribution of scatterers in target along the range dimension. Specifically, we utilize RNN to explore the sequential relationship between the range cells within a HRRP sample and employ the attention mechanism to weight up each timestep in the hidden state so as to discover the target area, which is more discriminative and informative. Effectiveness and efficiency are evaluated on the measured data. Compared with traditional methods, besides the competitive recognition performance, TARAN is also more robust to time-shift sensitivity thanks to the memory function of RNN and attention mechanism. Furthermore, detailed analysis of TARAN model are provided based on time domain and spectrogram features. [ABSTRACT FROM AUTHOR]
Copyright of Signal Processing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Target-Aware Recurrent Attentional Network for Radar HRRP Target Recognition.
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  Data: <searchLink fieldCode="JN" term="%22Signal+Processing%22">Signal Processing</searchLink>. Feb2019, Vol. 155, p268-280. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Radar+in+navigation%22">Radar in navigation</searchLink><br /><searchLink fieldCode="DE" term="%22High+resolution+imaging%22">High resolution imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Time-domain+analysis%22">Time-domain analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Scattering+%28Physics%29%22">Scattering (Physics)</searchLink><br /><searchLink fieldCode="DE" term="%22Spectrographs%22">Spectrographs</searchLink>
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  Label: Abstract
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  Data: Highlights • RNN can learn discriminative HRRP features by considering the temporal correlation. • Spectrogram containing phase information is more suitable for HRRP ATR. • The attention mechanism can automatically focus on the discriminative target areas. • Combining attention mechanism and RNN alleviates the time-shift sensitivity of HRRP. Abstract In this paper, we develop a Target-Aware Recurrent Attentional Network (TARAN) for Radar Automatic Target Recognition (RATR) based on High-Resolution Range Profile (HRRP) to make use of the temporal dependence and find the informative areas in HRRP, since it reflects the distribution of scatterers in target along the range dimension. Specifically, we utilize RNN to explore the sequential relationship between the range cells within a HRRP sample and employ the attention mechanism to weight up each timestep in the hidden state so as to discover the target area, which is more discriminative and informative. Effectiveness and efficiency are evaluated on the measured data. Compared with traditional methods, besides the competitive recognition performance, TARAN is also more robust to time-shift sensitivity thanks to the memory function of RNN and attention mechanism. Furthermore, detailed analysis of TARAN model are provided based on time domain and spectrogram features. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Signal Processing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.sigpro.2018.09.041
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      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 268
    Subjects:
      – SubjectFull: Radar in navigation
        Type: general
      – SubjectFull: High resolution imaging
        Type: general
      – SubjectFull: Time-domain analysis
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      – SubjectFull: Scattering (Physics)
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      – SubjectFull: Spectrographs
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      – TitleFull: Target-Aware Recurrent Attentional Network for Radar HRRP Target Recognition.
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              Text: Feb2019
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              Y: 2019
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              Value: 155
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