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• W2769881504 abstract "ABSTRACT Missing and irregular ground‐penetrating radar trace data resulting from sampling conditions are important issues in engineering. This study adopted compressive sensing theory to reconstruct missing ground‐penetrating radar trace data. A ground‐penetrating radar data reconstruction method was established based on compressive sensing theory and K‐singular value decomposition. The method used the sampling matrix of the missing data as the measurement matrix and the K‐singular value decomposition algorithm to obtain a complete dictionary of sparse coefficients. A traditional dictionary cannot be adaptively adjusted according to the data features; the proposed method resolved this problem. The iteratively reweighted least‐squares method was used to reconstruct the missing trace data. Two experiments on the recovery of missing ground‐penetrating radar data through a simulation and a field example were conducted to test the feasibility and effectiveness of the proposed method." @default.
• W2769881504 created "2017-12-04" @default.
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• W2769881504 date "2017-05-01" @default.
• W2769881504 modified "2023-10-11" @default.
• W2769881504 title "GPR data reconstruction method based on compressive sensing and K‐SVD" @default.
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• W2769881504 doi "https://doi.org/10.3997/1873-0604.2017030" @default.
• W2769881504 hasPublicationYear "2017" @default.
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