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| | Abstract | | We propose a landmine detection technique using model based compressive sensing GPR system. Conventional CS algorithm enables the reconstruction of sparse subsurface image using much reduced measurement by exploiting its sparsity. However, for landmine detection purpose, CS faces some challenges because the landmine is not exactly a point target and also faces high level clutter from the propagation in the medium. By exploiting the physical characteristic of the landmine using model based CS, the probability of landmine detection can be increased. Using a small pixel size, the landmine reflection in the image is represented by several pixels grouping together in a three dimensional plane. This block structure can be used in the model based CS processing for imaging the buried landmine. It is observed in experimental result, that with the same amount of measurement samples, the model based CS gives better reconstruction of landmine image than the conventional CS. |
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