The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
기존의 표적 인식 방법은 일반적으로 레이더 HRRP(고해상도 범위 프로파일) 인식에 적용할 때 정보 손실 및 표적 측면 감도 문제를 겪습니다. 따라서 강력하고 차별적인 특징 표현을 효과적으로 설정하면 실제 레이더 응용 프로그램의 성능이 크게 향상됩니다. 본 연구에서는 밀리미터파 레이더 HRRP 인식을 위해 수정된 협업 자동 인코더를 기반으로 하는 새로운 특징 추출 방법을 제시합니다. 잠재 프레임별 가중치 벡터는 프레임의 샘플에 대해 훈련되어 다양한 대상에 대한 로컬 정보를 유지하는 데 도움이 됩니다. 실험 결과는 제안된 알고리즘이 기존의 표적 인식 알고리즘보다 더 높은 표적 인식 정확도를 얻는다는 것을 보여주었다.
Yilu MA
Nanjing University of Science and Technology
Zhihui YE
Nanjing University of Science and Technology
Yuehua LI
Nanjing University of Science and Technology
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
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
부
@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},}
부
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 -