A new algorithm for the restoration of extended images, the Regularized Pseudoinverse Deconvolution (RAPID) algorithm, is proposed. The algorithm consists of expanding the regularized pseudoinverse of the imaging operator into a sequence of terms which can be easily implemented using Fourier processing techniques. The first term of the expansion is closely related to generalized Wiener filtering if the point spread function is shift-invariant. The other terms in the expansion are correction terms which are small when the point spread function is shift-invariant, as is the case with many imaging systems. Even when the point spread function of the imaging system is space-variant, such as with a partially obscured imaging system or a system with severe aberrations, the correction terms are both few in number and easily implemented.
Inverse synthetic aperture radar (ISAR) is an imaging technique that shows real promise in classifying airborne targets in real time under all weather conditions. Over the past few years a large body of ISAR data has been collected and considerable effort has been expended to develop algorithms to form high-resolution images from this data. One important goal of workers in this field is to develop software that will do the best job of imaging under the widest range of conditions. The success of classifying targets using ISAR is predicated upon forming highly focused radar images of these targets. Efforts to develop highly focused imaging computer software have been challenging, mainly because the imaging depends on and is affected by the motion of the target, which in general is not precisely known. Specifically, the target generally has both rotational motion about some axis and translational motion as a whole with respect to the radar. The slant-range translational motion kinematic quantities must be first accurately estimated from the data and compensated before the image can be focused. Following slant-range motion compensation, the image is further focused by determining and correcting for target rotation. The use of the burst derivative measure is proposed as a means to improve the computational efficiency of currently used ISAR algorithms. The use of this measure in motion compensation ISAR algorithms for estimating the slant-range translational motion kinematic quantities of an uncooperative target is described. Preliminary tests have been performed on simulated as well as actual ISAR data using both a Sun 4 workstation and a parallel processing transputer array. Results indicate that the burst derivative measure gives significant improvement in processing speed over the traditional entropy measure now employed.
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