Analysis of wideband forward looking synthetic aperture radar for sensing land mines
Signal processing algorithms are considered for the analysis of wideband, forward looking synthetic aperture radar data and for sensing metal and plastic land mines, with principal application to unpaved roads. Simple prescreening algorithms are considered for reduction of the search space required for a subsequent classifier. The classifier employs features based on viewing the target at multiple ranges, with classification implemented via a support vector machine and a relevance vector machine (RVM). Concerning classifier training, we consider cases for which training is performed on both mine and nonmine (clutter) data. In addition, motivated by the fact that the clutter statistics may vary significantly between the training and testing data, we also consider RVM implementation when we only train on mine data.
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