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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dans le contexte d'un fouillis non homogène et dynamique variable dans le temps, la capacité de traitement de l'algorithme traditionnel de détection du taux de fausses alarmes constant (CFAR) est considérablement réduite, ainsi que les performances de détection. Cet article propose un algorithme de détection CFAR basé sur la connaissance du fouillis (CK-CFAR), en tant que nouveau CFAR, pour améliorer l'adaptabilité des performances de détection du radar dans un fond de fouillis complexe. Grâce aux connaissances préalables acquises sur le fouillis, l'algorithme peut sélectionner dynamiquement des paramètres en fonction du changement du fouillis d'arrière-plan et calculer le seuil. Comparés aux algorithmes de détection tels que CA-CFAR, GO-CFAR, SO-CFAR et OS-CFAR, les résultats de simulation montrent que CK-CFAR présente d'excellentes performances de détection en arrière-plan d'un fouillis homogène et d'un fouillis de bord. Cet algorithme peut aider le radar à s'adapter au fouillis avec différentes caractéristiques de distribution, améliorant ainsi efficacement la détection radar dans un environnement complexe. Cela correspond davantage à l’orientation de développement du radar cognitif.
Kaixuan LIU
University of Electronic Science and Technology of China
Yue LI
University of Electronic Science and Technology of China
Peng WANG
University of Electronic Science and Technology of China
Xiaoyan PENG
University of Electronic Science and Technology of China
Hongshu LIAO
University of Electronic Science and Technology of China
Wanchun LI
University of Electronic Science and Technology of China
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Kaixuan LIU, Yue LI, Peng WANG, Xiaoyan PENG, Hongshu LIAO, Wanchun LI, "A CFAR Detection Algorithm Based on Clutter Knowledge for Cognitive Radar" in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 3, pp. 590-599, March 2023, doi: 10.1587/transfun.2022EAP1064.
Abstract: Under the background of non-homogenous and dynamic time-varying clutter, the processing ability of the traditional constant false alarm rate (CFAR) detection algorithm is significantly reduced, as well as the detection performance. This paper proposes a CFAR detection algorithm based on clutter knowledge (CK-CFAR), as a new CFAR, to improve the detection performance adaptability of the radar in complex clutter background. With the acquired clutter prior knowledge, the algorithm can dynamically select parameters according to the change of background clutter and calculate the threshold. Compared with the detection algorithms such as CA-CFAR, GO-CFAR, SO-CFAR, and OS-CFAR, the simulation results show that CK-CFAR has excellent detection performance in the background of homogenous clutter and edge clutter. This algorithm can help radar adapt to the clutter with different distribution characteristics, effectively enhance radar detection in a complex environment. It is more in line with the development direction of the cognitive radar.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2022EAP1064/_p
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@ARTICLE{e106-a_3_590,
author={Kaixuan LIU, Yue LI, Peng WANG, Xiaoyan PENG, Hongshu LIAO, Wanchun LI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A CFAR Detection Algorithm Based on Clutter Knowledge for Cognitive Radar},
year={2023},
volume={E106-A},
number={3},
pages={590-599},
abstract={Under the background of non-homogenous and dynamic time-varying clutter, the processing ability of the traditional constant false alarm rate (CFAR) detection algorithm is significantly reduced, as well as the detection performance. This paper proposes a CFAR detection algorithm based on clutter knowledge (CK-CFAR), as a new CFAR, to improve the detection performance adaptability of the radar in complex clutter background. With the acquired clutter prior knowledge, the algorithm can dynamically select parameters according to the change of background clutter and calculate the threshold. Compared with the detection algorithms such as CA-CFAR, GO-CFAR, SO-CFAR, and OS-CFAR, the simulation results show that CK-CFAR has excellent detection performance in the background of homogenous clutter and edge clutter. This algorithm can help radar adapt to the clutter with different distribution characteristics, effectively enhance radar detection in a complex environment. It is more in line with the development direction of the cognitive radar.},
keywords={},
doi={10.1587/transfun.2022EAP1064},
ISSN={1745-1337},
month={March},}
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TY - JOUR
TI - A CFAR Detection Algorithm Based on Clutter Knowledge for Cognitive Radar
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 590
EP - 599
AU - Kaixuan LIU
AU - Yue LI
AU - Peng WANG
AU - Xiaoyan PENG
AU - Hongshu LIAO
AU - Wanchun LI
PY - 2023
DO - 10.1587/transfun.2022EAP1064
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2023
AB - Under the background of non-homogenous and dynamic time-varying clutter, the processing ability of the traditional constant false alarm rate (CFAR) detection algorithm is significantly reduced, as well as the detection performance. This paper proposes a CFAR detection algorithm based on clutter knowledge (CK-CFAR), as a new CFAR, to improve the detection performance adaptability of the radar in complex clutter background. With the acquired clutter prior knowledge, the algorithm can dynamically select parameters according to the change of background clutter and calculate the threshold. Compared with the detection algorithms such as CA-CFAR, GO-CFAR, SO-CFAR, and OS-CFAR, the simulation results show that CK-CFAR has excellent detection performance in the background of homogenous clutter and edge clutter. This algorithm can help radar adapt to the clutter with different distribution characteristics, effectively enhance radar detection in a complex environment. It is more in line with the development direction of the cognitive radar.
ER -