IEICE TRANSACTIONS on Information
Millimeter-Wave Radar Target Recognition Algorithm Based on Collaborative Auto-Encoder
Summary :
Conventional target recognition methods usually suffer from information-loss and target-aspect sensitivity when applied to radar high resolution range profile (HRRP) recognition. Thus, Effective establishment of robust and discriminatory feature representation has a significant performance improvement of practical radar applications. In this work, we present a novel feature extraction method, based on modified collaborative auto-encoder, for millimeter-wave radar HRRP recognition. The latent frame-specific weight vector is trained for samples in a frame, which contributes to retaining local information for different targets. Experimental results demonstrate that the proposed algorithm obtains higher target recognition accuracy than conventional target recognition algorithms.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.1 pp.202-205
- Publication Date
- 2019/01/01
- Publicized
- 2018/10/03
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDL8142
- Type of Manuscript
- LETTER
- Category
- Pattern Recognition
Authors
Yilu MA
Nanjing University of Science and Technology
Zhihui YE
Nanjing University of Science and Technology
Yuehua LI
Nanjing University of Science and Technology
Keyword
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Yilu MA, Zhihui YE, Yuehua LI, "Millimeter-Wave Radar Target Recognition Algorithm Based on Collaborative Auto-Encoder" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 1, pp. 202-205, January 2019, doi: 10.1587/transinf.2018EDL8142.
Abstract: Conventional target recognition methods usually suffer from information-loss and target-aspect sensitivity when applied to radar high resolution range profile (HRRP) recognition. Thus, Effective establishment of robust and discriminatory feature representation has a significant performance improvement of practical radar applications. In this work, we present a novel feature extraction method, based on modified collaborative auto-encoder, for millimeter-wave radar HRRP recognition. The latent frame-specific weight vector is trained for samples in a frame, which contributes to retaining local information for different targets. Experimental results demonstrate that the proposed algorithm obtains higher target recognition accuracy than conventional target recognition algorithms.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8142/_p
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@ARTICLE{e102-d_1_202,
author={Yilu MA, Zhihui YE, Yuehua LI, },
journal={IEICE TRANSACTIONS on Information},
title={Millimeter-Wave Radar Target Recognition Algorithm Based on Collaborative Auto-Encoder},
year={2019},
volume={E102-D},
number={1},
pages={202-205},
abstract={Conventional target recognition methods usually suffer from information-loss and target-aspect sensitivity when applied to radar high resolution range profile (HRRP) recognition. Thus, Effective establishment of robust and discriminatory feature representation has a significant performance improvement of practical radar applications. In this work, we present a novel feature extraction method, based on modified collaborative auto-encoder, for millimeter-wave radar HRRP recognition. The latent frame-specific weight vector is trained for samples in a frame, which contributes to retaining local information for different targets. Experimental results demonstrate that the proposed algorithm obtains higher target recognition accuracy than conventional target recognition algorithms.},
keywords={},
doi={10.1587/transinf.2018EDL8142},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - Millimeter-Wave Radar Target Recognition Algorithm Based on Collaborative Auto-Encoder
T2 - IEICE TRANSACTIONS on Information
SP - 202
EP - 205
AU - Yilu MA
AU - Zhihui YE
AU - Yuehua LI
PY - 2019
DO - 10.1587/transinf.2018EDL8142
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2019
AB - Conventional target recognition methods usually suffer from information-loss and target-aspect sensitivity when applied to radar high resolution range profile (HRRP) recognition. Thus, Effective establishment of robust and discriminatory feature representation has a significant performance improvement of practical radar applications. In this work, we present a novel feature extraction method, based on modified collaborative auto-encoder, for millimeter-wave radar HRRP recognition. The latent frame-specific weight vector is trained for samples in a frame, which contributes to retaining local information for different targets. Experimental results demonstrate that the proposed algorithm obtains higher target recognition accuracy than conventional target recognition algorithms.
ER -
English
