A number of imaging and target identification admit a generalized tomographic formulation where the object to be imaged (identified) is multi-dimensional and the sensor data are one-dimensional generalized projections of this object. The goal of the proposed research is to formulate, develop and test and evaluate efficient wavelet based algorithms for such applications directly at the level of the sensor data; i.e., algorithms that operate directly on the tomographic projections prior to any image formation or target detection or recognition procedures. The research is directed toward the class of applications, such as ultrasound tomography and radar imaging, where the object being imaged is highly localized in image space so that the generalized projections are transient like and therefore ideally suited to wavelet analysis. The proposed research includes the development of iterative and non-iterative wavelet based generalized tomographic imaging algorithms and test and evaluation of these algorithms in ultrasound tomography and the recently developed "chirp diversity radar". Other applications such as medical x-ray computed tomography and synthetic aperture radar (SAR) will also be addressed. Anticipated Benefits:
The research has direct application to a number of imaging and target identification applications including medical CT and MRI, radar imaging, geophysical imaging and medical ultrasound tomography.