Landmine Detection Based on Aspect Invariant Characteristics

The difficulty of landmine detection using vehicle-mounted ultra-wideband ground penetrating radar was the excessively high false alarm rate. It was hard to distinguish landmine and clutter in full aperture. To decrease the false alarm rate during landmine detection, a detection algorithm based on aspect invariant characteristics of sub-aperture images was proposed in this paper. With the split transmitting virtual aperture imaging model, the full image was decomposed into left and right sub-aperture images. And the double-hump model was established according to the one dimension range profile of sub-aperture images. Based on this model, some aspect invariant characteristics features could be extracted. Furthermore, the measurement of consistency of left and right sub- aperture images was obtained and sent to classifier as the final feature vector. It was proved by real data that the algorithm in this paper can effectively eliminate the clutter which can not be eliminated in the full aperture image. So this algorithm can decrease the false alarm rate in vehicle-mounted ultra-wideband ground penetrating radar.