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The phase gradient autofocus (PGA) technique for phase error correction of spotlight m d e synthetic aperture radar (SAR) imagery is examined carefully in the context of four fundamental signal processing steps that constitute the algorithm We demnstrate that excellent results over a wide variety of scene content, and phase error function structure are obtained if and only if all of these steps are included in the processing. Finally, we show that the computational demands of the full PGA algorithm do not represent a large fraction of the total image formation problem, when mid to large size images are involved.

APR 0 8 1998The detection and refocus of moving targets in SAR imagery is of interest in a number of applications. In this paper we address the problem of refocusing a blurred signature that has by some means been identified as a moving target. We assume that the target vehicle velocity is constant, i.e., the motion is in a straight line with constant speed. The refocus is accomplished by application of a twedimensional phase function to the phase history data obtained via Fourier transformation of an image chip that contains the blurred moving target data. By considering separately the phase effects of the range and cross-range components of the target velocity vector, we show how the appropriate phase correction term can be derived as a two-parameter function. We then show a procedure for estimating the two parameters, so that the blurred signature can be automatically refocused. The algorithm utilizes optimization of an image domain contrast metric. We present results of refocusing moving targets in real SAR imagery by this method.

The phase-gradient algorithm represents a powerful new signal-processing technique with applications to aperture-synthesis imaging. These include, for example, synthetic-aperture-radar phase correction and stellar-image reconstruction. The algorithm combines redundant information present in the data to arrive at an estimate of the phase derivative. We show that the estimator is in fact a linear, minimum-variance estimator of the phase derivative.

Uncompensated phase errors present in synthetic-aperture-radar data can have a disastrous effect on reconstructed image quality. We present a new iterative algorithm that holds promise of being a robust estimator and corrector for arbitrary phase errors. Our algorithm is similar in many respects to speckle processing methods currently used in optical astronomy. We demonstrate its ability to focus scenes containing large amounts of phase error regardless of the phase-error structure or its source. The algorithm works extremely well in both high and low signal-to-clutter conditions without human intervention.