Signal subspace fusion of uncalibrated sensors with application in SAR and diagnostic medicine

Addresses the problem of fusing the information content of two uncalibrated sensors. This problem arises in registering images of a scene when it is viewed via two different sensory systems, or detecting change in a scene when it is viewed at two different time points by a sensory system, or via two different sensory systems or observation channels. We are concerned with sensory systems which have not only a relative shift, scaling and rotational calibration error, but also an unknown point spread function (that is time varying for a single sensor, or different for two sensors). By modeling one image in terms of an unknown linear combination of the other image, its powers and their spatially transformed (shift, rotation and scaling) versions, a signal subspace processing is developed for fusing uncalibrated sensors. The proposed method is shown to be applicable in moving target detection (MTD) using monopulse synthetic aperture radar (SAR) with uncalibrated radars. Results are shown for video, magnetic resonance images of a human brain, moving target detector monopulse SAR, and registration of SAR images of a target obtained via two different radars or at different coordinates by the same radar for automatic target recognition (ATR)