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• W4229013439 abstract "• An adaptive WBI detection method based on TF kurtosis is proposed. Aiming to solving the poor robustness of the existing interference detection methods, this paper introduces TF kurtosis to measure the non-Gaussianity of the echo in the TF domain. It also achieves adaptive statistical detection of RFI with the Neiman Pearson criterion. • The proposed scheme addresses the conventional assumption of a complex Gaussian distribution for SAR echo signals by introducing the Laplace distribution to describe the SAR echo signal without interference, and thereby provide a L 1 –LRMF model, which can bring about a more satisfactory mitigation performance. • The hierarchical Bayesian posterior model promotes the robustness and generalization ability of the WBI mitigation algorithm with respect to unobserved SAR data. The L 1 norm LRMF method aims to describe the observed SAR data specifically, but it has the potential overfitting problem due to the deterministic model. The proposed algorithm utilizes the probability to introduce the uncertainty of interference mitigation model, which promotes the robustness on the unobserved SAR data. • The adoption of VBI for estimating these parameters in the present scheme addresses the lack of adaptability to changes in signal characteristics commonly encountered in existing interference mitigation methods. Moreover, estimating parameters using VBI also addresses the problems associated with parameter estimation based on the usual expectation-maximization algorithm, which involves a multiple-integration process that makes the estimation process extremely complicated and intractable for complex models. Wideband interference (WBI) seriously detracts from synthetic aperture radar (SAR) imaging quality and hinders the subsequent interpretation of images. Meanwhile, the large bandwidth and complex modulation of WBI necessitates the development of mitigation algorithms with strong adaptability and robustness that are lacking in existing algorithms based on filtering and model analysis. The present work addresses this issue by proposing a WBI mitigation algorithm based on variational Bayesian inference (VBI). Firstly, the WBI-contaminated echo signal is identified in the time–frequency (TF) domain through an adaptive statistical detection method. Then, a low-rank matrix factorization is formulated according to the low-rank characteristics of the WBI and the Laplace distribution assumption of the target echo signal in the TF domain. Finally, the WBI component is accurately reconstructed using a mean-field VBI method, and then eliminated from the original SAR echo signal to recover the target echo signal. The effectiveness and robustness of the proposed WBI mitigation algorithm are demonstrated based on its application to simulated and measured SAR data." @default.
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• W4229013439 date "2022-09-01" @default.
• W4229013439 modified "2023-09-22" @default.
• W4229013439 title "Wideband interference mitigation for synthetic aperture radar based on the variational Bayesian method" @default.
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• W4229013439 doi "https://doi.org/10.1016/j.sigpro.2022.108581" @default.
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