Zobrazeno 1 - 10
of 114
pro vyhledávání: '"Xiongjun Fu"'
Publikováno v:
IET Radar, Sonar & Navigation, Vol 18, Iss 8, Pp 1291-1306 (2024)
Abstract Countermeasures for chaff jamming have drawn great attention in the field of radar target detection and tracking. Current approaches for chaff jamming recognition and suppression exhibit limitations in practical effect, generalisation abilit
Externí odkaz:
https://doaj.org/article/fb0aa6ed9b1f49a298f3dd466ed8093e
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 19260-19273 (2024)
Ship detection is a critical component of marine resource management and environmental monitoring. Methods based on deep learning have been widely used in target detection. However, there are two obstacles in ship detection: First, due to the mechani
Externí odkaz:
https://doaj.org/article/a190b2e9abd04c6ba1aa84ab1276d504
Publikováno v:
Sensors, Vol 24, Iss 13, p 4290 (2024)
Synthetic Aperture Radar (SAR) ship detection is applicable to various scenarios, such as maritime monitoring and navigational aids. However, the detection process is often prone to errors due to interferences from complex environmental factors like
Externí odkaz:
https://doaj.org/article/e13a0aba14ac4ff7b6bb81dff6b98388
Publikováno v:
Sensors, Vol 24, Iss 11, p 3445 (2024)
Frequency agility refers to the rapid variation of the carrier frequency of adjacent pulses, which is an effective radar active antijamming method against frequency spot jamming. Variation patterns of traditional pseudo-random frequency hopping metho
Externí odkaz:
https://doaj.org/article/0cfecb5d4bf74bbd9474af59faec9025
Autor:
Hao Chang, Xiongjun Fu, Jihua Lu, Kunyi Guo, Jian Dong, Congxia Zhao, Cheng Feng, Ziying Li, Yue Zhang
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 8363-8378 (2023)
Synthetic aperture radar (SAR) images of ships have complex background interference, multi-scale targets, and irregular distribution characteristics. However, existing mainstream SAR ship detection algorithms rely on manually designed hyperparameters
Externí odkaz:
https://doaj.org/article/ebc91eaa37c34c9bb8b6a3651b33ffc9
Publikováno v:
Remote Sensing, Vol 15, Iss 20, p 4921 (2023)
Coherent frequency-agile radar (FAR) has a low probability of intercept (LPI) and excellent performance of electronic counter-countermeasures (ECCM) and electromagnetic compatibility, which can improve radar cooperation and survivability in complex e
Externí odkaz:
https://doaj.org/article/d02c62c2a04c43d395ad9212d1f54c39
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 6615-6630 (2022)
Deep-learning-based synthetic aperture radar automatic target recognition (SAR-ATR) plays a significant role in the military and civilian fields. However, data limitation and large computational cost are still severe challenges in the actual applicat
Externí odkaz:
https://doaj.org/article/e45edcd22d3a4b2dbac3e57b9c4ac19e
Publikováno v:
IEEE Access, Vol 10, Pp 97429-97438 (2022)
The confrontation between radar and jammer is increasingly competitive in electromagnetic spectrum warfare. The current radar anti-jamming methods are constrained to some extent in complex electromagnetic environment. It is necessary to study not onl
Externí odkaz:
https://doaj.org/article/38e1ec41a5b94fde922cc3f2a5134dd3
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 4599-4611 (2022)
Intelligent ship detection based on high-precision synthetic aperture radar (SAR) images plays a vital role in ocean monitoring and maritime management. Denoising is an effective preprocessing step for target detection. Morphological network-based de
Externí odkaz:
https://doaj.org/article/83fd80c22ecd466b8a3b34b3ea356de9
Publikováno v:
ETRI Journal, Vol 43, Iss 6, Pp 991-1003 (2021)
AbstractAutomatic modulation classification is essential in radar emitter identification. We propose a cascade classifier by combining a support vector machine (SVM) and convolutional neural network (CNN), considering that noise might be taken as rad
Externí odkaz:
https://doaj.org/article/ca9397887ec34b4d8ab90f331254a6e0