Time-Varying RFI Suppression of Dechirping Radar Based on <italic>l</italic>₁ Norm Filtering and RAM-Based Frequency Estimation

• Main Authors: Pengcheng Wan, Ningning Tong, Guimei Zheng
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Sparse recovery; reweighted atomic norm minimization (RAM); radio frequency interference (RFI); dechirping
• Online Access: https://ieeexplore.ieee.org/document/9129735/

This paper presents a novel framework for suppressing time-varying radio frequency interference (TRFI) in wideband dechirping radar. The TRFI challenges the wideband radar because of 1) changing in wide spectra range; 2) dynamic nature and unpredictability; 3) significant noise power in limited band. In this paper, an essential phenomenon of real measured data about TRFI in dechirping process is analyzed, where it is smashed into several segments because of the narrowband filtering. Based on that, a 2-step framework combining the interference filtering and frequency estimation is proposed. First, since the different sparsity of signal of interference, a simple yet very effective regional filtering method based on l₁ norm of STFT is applied, which transforms original problem into a sparse signal recovery problem. Second, for 1-D signal, the reweighted atomic norm minimization (RAM) algorithm is applied to estimate the signal from defect filtered data; for 2-D signal block, a weighting strategy is proposed to promote the accuracy of original 2-D atomic norm minimization method. This method can be implemented as an interference suppression stage for wideband radar with dechirping processing. Experiment of real measured signal in TRFI environment and simulation of defect signal block illustrate its effectiveness.