TY - JOUR
T1 - Reweighted Nuclear Norm and Reweighted Frobenius Norm Minimizations for Narrowband RFI Suppression on SAR System
AU - Huang, Yan
AU - Liao, Guisheng
AU - Xiang, Yijian
AU - Zhang, Zhen
AU - Li, Jie
AU - Nehorai, Arye
N1 - Funding Information:
Manuscript received March 5, 2018; revised May 20, 2018, September 5, 2018, and January 8, 2019; accepted February 24, 2019. Date of publication April 4, 2019; date of current version July 22, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61621005, Grant 61601339, and Grant 61701106, and in part by the Natural Science Foundation of Jiangsu Province under Grant BK20170698. (Corresponding author: Yan Huang.) Y. Huang is with the State Key Lab of Millimeter Waves, School of Information Science and Engineering, Southeast University, Nanjing 210096, China (e-mail: [email protected]).
Publisher Copyright:
© 1980-2012 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - Synthetic aperture radar (SAR), as a wideband radar system, is subject to interference by radio frequency systems, such as radio, TV, and cellular networks. Since the narrowband radio frequency interference (RFI) has a stable frequency in a snapshot sequence, it has a low-rank property that can be used to substract RFI from the received signal. The nuclear norm is a common convex relaxation to constrain the rank, but it is optimized by the singular value thresholding (SVT) algorithm, which uses a single threshold to treat all singular values and greatly over-punishes large singular values. Hence, in this paper, we propose two methods, the reweighted nuclear norm (RNN) algorithm and the reweighted Frobenius norm (RFN) algorithm, to separate the RFI and the useful signal. The RNN and RFN minimization problems are the approximations of the real rank function, which can protect large singular values and restrict the rank. As a result, the RFI is accurately extracted and the useful signal is successfully protected. Also, we strictly derive the closed-form solutions of the RNN and RFN minimization problems for complex radar signals, and we also employ downsampling to extract the mainband of the signal spectrum to speed up the convergence. Real SAR data is applied to demonstrate the effectiveness of the proposed methods for RFI suppression.
AB - Synthetic aperture radar (SAR), as a wideband radar system, is subject to interference by radio frequency systems, such as radio, TV, and cellular networks. Since the narrowband radio frequency interference (RFI) has a stable frequency in a snapshot sequence, it has a low-rank property that can be used to substract RFI from the received signal. The nuclear norm is a common convex relaxation to constrain the rank, but it is optimized by the singular value thresholding (SVT) algorithm, which uses a single threshold to treat all singular values and greatly over-punishes large singular values. Hence, in this paper, we propose two methods, the reweighted nuclear norm (RNN) algorithm and the reweighted Frobenius norm (RFN) algorithm, to separate the RFI and the useful signal. The RNN and RFN minimization problems are the approximations of the real rank function, which can protect large singular values and restrict the rank. As a result, the RFI is accurately extracted and the useful signal is successfully protected. Also, we strictly derive the closed-form solutions of the RNN and RFN minimization problems for complex radar signals, and we also employ downsampling to extract the mainband of the signal spectrum to speed up the convergence. Real SAR data is applied to demonstrate the effectiveness of the proposed methods for RFI suppression.
KW - Narrowband radio frequency interference (RFI) suppression
KW - reweighted Frobenius norm (RFN)
KW - reweighted nuclear norm (RNN)
KW - synthetic aperture radar (SAR) system
UR - http://www.scopus.com/inward/record.url?scp=85069753770&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2019.2903579
DO - 10.1109/TGRS.2019.2903579
M3 - Article
AN - SCOPUS:85069753770
SN - 0196-2892
VL - 57
SP - 5949
EP - 5962
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 8
M1 - 8681711
ER -