期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 57, 期 11, 页码 9396-9409出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2019.2926440
关键词
Narrowband; Synthetic aperture radar; Sparse matrices; Radiofrequency interference; Time-frequency analysis; Azimuth; Radio-frequency interference (RFI) mitigation; reweighted tensor Frobenius norm (RTFN); reweighted tensor nuclear norm (RTNN); synthetic aperture radar (SAR)
类别
资金
- National Natural Science Foundation of China [61901112, 61771372, 61801297, 61701106]
- Natural Science Foundation of Jiangsu Province [BK20170698]
- Shenzhen University [2019119]
Radio-frequency systems, such as TV and cellular networks, severely interfere with synthetic aperture radar (SAR) systems. Narrowband radio-frequency interference (RFI) has a special low-rank property in the received signal matrix, because it performs like a sinusoid with nearly invariant frequency as the slow time proceeds. Exploiting this special property, in this paper, we divide the received signal matrix into several small matrices, in each of which the RFI is also low rank. Without losing the connection between these small matrices, we stack them into a three-mode tensor to separate the low-rank RFI tensor and recover the informative signal tensor. Previous studies employed the nuclear norm to regularize the low-rank RFI, which is not a good choice. Hence, we propose two reweighted algorithms, the reweighted tensor nuclear norm (RTNN) and the reweighted tensor Frobenius norm (RTFN) algorithms, to approximate the rank function in a tensor and accurately extract the low-rank RFI tensor from the received signal tensor. As a result, the introduction of the tensor structure dramatically decreases the computational cost. Furthermore, the reweighted scheme helps suppressing the RFI and recovering the useful signal with excellent performance. Finally, real SAR data with measured RFI is employed to demonstrate the effectiveness of the proposed methods for RFI mitigation.
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