A forward-looking and -moving ground-penetrating radar (GPR) acquires data that can be used for buried target detection. As the platform moves forward the sensor can acquire and form a sequence of images for a common spatial region. Due to the near-field nature of relevant collection scenarios, the point-spread function (PSF) varies significantly as a function of the spatial position, both within the scene and relative to the sensor platform. This variability of the PSF causes computational difficulties for matched-filter and related processing of the full video sequence. One approach to circumventing this difficulty is to coherently or incoherently integrate the video frames, and then perform detection processing on the integrated image. Here, averaging over the space- and motion-variant nature of the PSFs for each frame causes the PSF for the integrated image to appear less space-variant. Another alternative—and the one we investigate in this paper—is to transform each image from the conventional (range, cross-range) coordinate system to a (range, sine-angle) coordinate system in which the PSF is approximated as spatially invariant. The advantage of the (range, sine-angle) coordinate space is that methods that require space-invariance can be directly applied. Here we develop a multi-anodization approach, which results in a significantly improved image. To evaluate the relative advantages of this procedure, we will empirically measure the integrated side-lobe ratio, which represents the reduction in the side-lobes before and after applying the algorithm.