GPR data reconstruction method based on compressive sensing and K-SVD
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 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.