Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Yiduo Guo"'
Publikováno v:
Remote Sensing, Vol 14, Iss 21, p 5294 (2022)
Target three-dimensional (3D) high-resolution imaging via multiple-input multiple-output (MIMO) radar may suffer from a heavy sampling burden and complicated radio frequency interferences. Considering a collocated two-dimensional wideband MIMO radar
Externí odkaz:
https://doaj.org/article/81abec67fe9c41689d6846bf7328c170
Publikováno v:
IEEE Access, Vol 7, Pp 6094-6108 (2019)
Sparse-recovery-based space–time adaptive processing (STAP) methods can exhibit superior clutter suppression performance with limited training data. However, the clutter suppression performance seriously degrades when the mutual coupling is present
Externí odkaz:
https://doaj.org/article/6ab6517ef059455baf1a8cd7ecfe22d3
Publikováno v:
IEEE Access, Vol 7, Pp 48109-48118 (2019)
The presence of mutual coupling usually causes space-time steering vector distortion for space-time adaptive processing (STAP) in airborne radar, thereby causing significant performance degradation. In this paper, a robust STAP approach was proposed
Externí odkaz:
https://doaj.org/article/352e58bc82b641a38e2e3d9bbcc86bfd
Publikováno v:
Remote Sensing, Vol 14, Iss 13, p 3064 (2022)
Most subspace-based algorithms need exact array manifold for direction of arrival (DOA) estimation, while, in practical applications, the gain-phases of different array elements are usually inconsistent, degrading their estimation performance. In thi
Externí odkaz:
https://doaj.org/article/2eae24550ee0445c9a192a30be41e906
Publikováno v:
IEEE Access, Vol 6, Pp 26605-26616 (2018)
To solve the problem of large training samples requirement of space time adaptive processing (STAP), a jointly sparse matrices recovery-based method is proposed for clutter plus noise covariance matrix estimation by exploiting the transmitting wavefo
Externí odkaz:
https://doaj.org/article/8e7c4ec20a064157b5ae727e73f6b3f1
Publikováno v:
Remote Sensing, Vol 13, Iss 18, p 3554 (2021)
Even though deep learning (DL) has achieved excellent results on some public data sets for synthetic aperture radar (SAR) automatic target recognition(ATR), several problems exist at present. One is the lack of transparency and interpretability for m
Externí odkaz:
https://doaj.org/article/ad072c7693c24c1a9b579575f817d6c3
Publikováno v:
IEEE Access, Vol 5, Pp 5896-5903 (2017)
An efficient and training-sample-reducing space-time adaptive processing (STAP) algorithm based on sparse representation for ground clutter suppression in airborne radar is proposed in this paper. First of all, the principle and problems of sample ma
Externí odkaz:
https://doaj.org/article/928b06c514564424b9a37f6ee86387f2
Publikováno v:
International Journal of Antennas and Propagation, Vol 2018 (2018)
Degree of freedom (DOF) of clutter in the reduced-dimension (RD) domain, which is called local DOF (LDOF), is of great importance for RD MIMO-STAP (space-time adaptive processing for multiple-input multiple-output radar) algorithms. In this paper, th
Externí odkaz:
https://doaj.org/article/616f3211525445d1b3630ad03cfc2690
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 20:1-5
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8ec7b71a208e34733431ae8af1a8becf
https://doi.org/10.1109/lgrs.2022.3230243
https://doi.org/10.1109/lgrs.2022.3230243
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 36:6783-6791
Catastrophic forgetting is a key obstacle to continual learning. One of the state-of-the-art approaches is orthogonal projection. The idea of this approach is to learn each task by updating the network parameters or weights only in the direction orth
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::61d81ded501844791a85ab4922279f3f
https://doi.org/10.1609/aaai.v36i6.20634
https://doi.org/10.1609/aaai.v36i6.20634