Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Degen Wang"'
Publikováno v:
IET Radar, Sonar & Navigation, Vol 17, Iss 9, Pp 1380-1390 (2023)
Abstract Non‐sidelooking airborne radar encounters significant non‐stationary and heterogeneous clutter environments, resulting in a severe shortage of samples. Sparse recovery‐based space‐time adaptive processing (SR‐STAP) methods can achi
Externí odkaz:
https://doaj.org/article/8a186a6581b04978ac8918c94a9ac439
Publikováno v:
IET Radar, Sonar & Navigation, Vol 17, Iss 5, Pp 772-784 (2023)
Abstract Space‐time adaptive processing (STAP) struggles to effectively suppress clutter in the heterogeneous clutter environment due to the lack of training samples. In order to enhance clutter suppression performance of STAP, a subspace‐weighte
Externí odkaz:
https://doaj.org/article/8060ded772214403a379f3cfd991a698
Publikováno v:
IET Radar, Sonar & Navigation, Vol 16, Iss 12, Pp 1936-1948 (2022)
Abstract Space‐time adaptive processing with finite samples is supposed to be a crucial technique for airborne radar systems. Inspired by the application of Gaussian prior in sparse Bayesian learning algorithm and the adaptive least absolute shrink
Externí odkaz:
https://doaj.org/article/0d4cf777e56d4fd09656d439115a6507
Publikováno v:
Remote Sensing, Vol 16, Iss 2, p 307 (2024)
The space–time adaptive processing (STAP) technique can effectively suppress the ground clutter faced by the airborne radar during its downward-looking operation and thus can significantly improve the detection performance of moving targets. Howeve
Externí odkaz:
https://doaj.org/article/13707beeec1a4009964f9a0881ed793f
Publikováno v:
Remote Sensing, Vol 15, Iss 17, p 4334 (2023)
Space-time adaptive processing (STAP) approaches based on sparse Bayesian learning (SBL) have attracted much attention for the benefit of reducing the training samples requirement and accurately recovering sparse signals. However, it has the problem
Externí odkaz:
https://doaj.org/article/ac6465570f5d4ee78bcd04ec56c2ea1d
Publikováno v:
Remote Sensing, Vol 15, Iss 1, p 130 (2022)
In recent years, sparse recovery-based space-time adaptive processing (SR-STAP) technique has exhibited excellent performance with insufficient samples. Sparse Bayesian learning algorithms have received considerable attention for their remarkable and
Externí odkaz:
https://doaj.org/article/895bc0dce25246afb72515dbae868ec5
Publikováno v:
Remote Sensing, Vol 14, Iss 18, p 4463 (2022)
Detecting a moving target is an attractive topic in many fields, such as remote sensing. Space-time adaptive processing (STAP) plays a key role in detecting moving targets in strong clutter backgrounds for airborne early warning radar systems. Howeve
Externí odkaz:
https://doaj.org/article/63fc48819e8244f99d7950c7132e4119
Publikováno v:
Remote Sensing, Vol 14, Iss 15, p 3520 (2022)
Space-time adaptive processing (STAP) encounters severe performance degradation with insufficient training samples in inhomogeneous environments. Sparse Bayesian learning (SBL) algorithms have attracted extensive attention because of their robust and
Externí odkaz:
https://doaj.org/article/a9a7f7ef2245498497220fb1d3a4c767
Publikováno v:
Sensors, Vol 22, Iss 15, p 5479 (2022)
Space-time adaptive processing (STAP) is an effective technology in clutter suppression and moving target detection for airborne radar. Because airborne radar moves at a constant acceleration, and there is a lack of independent and identically distri
Externí odkaz:
https://doaj.org/article/614743f01a034776b89c910b57398e47
Publikováno v:
IEEE Sensors Journal. 23:10900-10911
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ac1db139cce7eeb0b790c34c5efb989f
https://doi.org/10.1109/jsen.2023.3263919
https://doi.org/10.1109/jsen.2023.3263919