Model-based principal component techniques for detection of buried landmines in multiframe synthetic aperture radar images

Here we consider the use of model-based methods for the detection of buried objects from a sequence of synthetic aperture images obtained by a radar sensor moving linearly down a track. The scattering physics of the underlying sensing modality cause the relevant target signatures to change in a complex yet predictable manner from one image to the next. To arrive at a tractable processing scheme that exploits these motion-induced changes, we develop a flexible parametric model capable of capturing the full variation of these signatures. A detection scheme based on a principal components analysis of estimated model vectors is then derived. Results are demonstrated using field data from a forward-looking sensor.