A Scheme for Extracting Weak RFI Parameters Based on TJLS Matrix Decomposition

This letter proposes a scheme for automatically and accurately extracting the parameters of weak radio frequency interference (RFI) present in the signal within the time–frequency domain. This scheme utilizes short-time Fourier transform (STFT) to transform each frame of radar echo containing interference into the time–frequency domain. In the time domain, a joint low-rank sparse robust principal component analysis based on truncated nuclear norms (TJLS-RPCA) is employed to divide the time–frequency representation into two joint low-rank sparse matrices, from which the interference parameters are extracted. Subsequently, $K$ -means clustering is applied to determine the number of RFIs, and Gaussian fitting is applied to eliminate high-error items. This scheme can extract parameters such as the central frequency, bandwidth, and duration of the interference. The proposed scheme improves the accuracy of parameter extraction and processing speed, demonstrating notable effectiveness in handling weak interference. The Monte Carlo experimental results demonstrate that the scheme provides highly accurate parameter estimation when the signal-to-interference ratio (SIR) is less than 13 dB, with errors below 0.5%. Finally, the feasibility of the proposed scheme is validated using Sentinel-1A data, and the extracted results are consistent with the simulation results.