TY - GEN
T1 - Interference Mitigation for Synthetic Aperture Radar Data using Tensor Representation and Low-Rank Approximation
AU - Tao, Mingliang
AU - Li, Jieshuang
AU - Su, Jia
AU - Fan, Yifei
AU - Wang, Ling
AU - Zhang, Zijing
N1 - Publisher Copyright:
© 2020 URSI.
PY - 2020/8
Y1 - 2020/8
N2 - Radio frequency interference (RFI) is a critical issue to synthetic aperture radar (SAR), which would cause great distortions to amplitude and phase information of the received echoes. Most of the existing literatures deal with the interference separation problem in time domain, frequency domain, or time-frequency domain using the matrix representation and matrix optimization tools, without further exploiting the correlation among multiple dimensional measurements. This paper proposes an interference separation for SAR data using tensor representation by formulating a novel time-frequency azimuth tensor. Then, the low-rank property of the interference is utilized and the interference contribution is estimated using low rank tensor approximation. Experimental results demonstrate that the interference components is effectively extracted, and well imaging results could be recovered.
AB - Radio frequency interference (RFI) is a critical issue to synthetic aperture radar (SAR), which would cause great distortions to amplitude and phase information of the received echoes. Most of the existing literatures deal with the interference separation problem in time domain, frequency domain, or time-frequency domain using the matrix representation and matrix optimization tools, without further exploiting the correlation among multiple dimensional measurements. This paper proposes an interference separation for SAR data using tensor representation by formulating a novel time-frequency azimuth tensor. Then, the low-rank property of the interference is utilized and the interference contribution is estimated using low rank tensor approximation. Experimental results demonstrate that the interference components is effectively extracted, and well imaging results could be recovered.
UR - https://www.scopus.com/pages/publications/85096843252
U2 - 10.23919/URSIGASS49373.2020.9232211
DO - 10.23919/URSIGASS49373.2020.9232211
M3 - 会议稿件
AN - SCOPUS:85096843252
T3 - 2020 33rd General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2020
BT - 2020 33rd General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 33rd General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2020
Y2 - 29 August 2020 through 5 September 2020
ER -