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Ground-penetrating radars (GPRs) show promising results for subsurface buried target detection. However, the online detection as the GPR scans a region is a difficult problem, and the best performance requires to know the characteristics of the clutter and noise which affect the used test statistics in detection. In statistical detection methods developed for GPR, mostly Gaussian clutter assumption is used mainly due to its simplicity. In this letter, a low-complexity goodness-of-fit test suitable for online GPR detection is applied to experimental GPR data sets to determine the best clutter distribution defining the data test statistic. The distributions of A-scan energies after background subtraction are determined from different experimental data taken over notarget regions. The obtained results show that the GPR clutter for the tested experimental data is mainly gamma distributed than Gaussian. The demonstrated procedure can be applied to any GPR data set for the determination of the background distribution, for target detection, and for selecting detection thresholds properly for GPR applications and more realistic GPR clutter generation simulations.