The clutter suppression is an active research area for ground-penetrating radar (GPR) and the current trends use low rank and sparse decomposition (LRSD) based methods which provide an overwhelming advantage over classical low-rank methods. However, there are not many studies about tensor decomposition methods which actually provide powerful tools such as tensor robust principal component analysis (TRPCA) method for decomposing a matrix into its low rank and sparse components, namely, clutter and target parts. In addition to the success of TRPCA and motivated by tensor decomposition results in video background subtraction compared to LRSD, we propose a new GPR clutter suppression method based on online stochastic tensor decomposition (OSTD) by adding a simple pre-transformation step. The proposed method is tested on both simulated and real datasets. Obtained results prove its superiority over the state-of-the-art clutter suppression methods.
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