Research On The Method Of Landmine Detection Based On Physics Model

Generally, landmine detection method based on energy has a high false alarm rate because of the complex environment and small size of landmine itself. Based on geometric model of real radar system and physic characteristic of landmine, scattering model of landmine is analyzed and established, at the same time, efficient landmine features are extracted for discrimination.For the real system, Stepped-frequency Forward-looking Ground-penetrating Virtual Aperture Radar developed by the National University of Defense Technology, the equal single station model together with the characteristics of sub-aperture image and full aperture image are analyzed firstly. Following that, a model of landmine electromagnetic scattering is established, and then the mechanism of double-hump characteristics together with its aspect consistency are introduced detailedly.Filtering in advance is an important step in target detection and is essential to characteristic extraction, which can efficiently reduce operation size and help to get region of interest (ROI). In this paper, conventional flow of detection is used for extracting the target's ROI, which include coupling signal depression, noise depression, image gain proportion, constant false alarm rate (CFAR) detection, morphological filter and weighted clustering.In the process of landmine discrimination, four characteristics related to the system and landmine structure are adopted, including angle of view offset degree, distance between the double-hump, double-hump peaks value ratio and width of main peak in double-hump characteristic model. According to the characteristics which are decided by the physics model, target discriminators are adopted easy threshold and Hyper Sphere Support Vector Machine respectively.Features extracted from the physics-model can be used for reducing clutter efficiently and get higher detection rate, and that is proved to be valid by the real data. Because the image revolution is limited by the bandwidth of signal and length of aperture, which makes features extraction difficultly. So new features extraction from super-revolution images using APES and Congress Sensing are studied, which is a new try in landmine detection.