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Synthetic aperture radar (SAR) systems produce high resolution, two dimensional imaging of areas of environmental interest. SAR interferometry and tomography enables these techniques to extend to three dimensional imaging by exploiting multiple SAR images with diversity in space and time. These techniques require accurate phase information over multiple images as the data is extremely sensitive to deviations from the reference track, therefore to enable interferometry and tomography an accurate autofocus solution is required. This paper investigates phase errors resulting from navigational uncertainties in multipass spotlight SAR imaging and uses techniques from the field of compressive sensing to achieve an autofocus solution. The proposed algorithm builds on previous autofocus work by expanding it to the multipass case and jointly recovers phase errors for all images simultaneously, making it extremely useful for interferometry and tomography techniques. The algorithm described uses pixels that are stable in all SAR images to gain an autofocus solution as these are the pixels that are the focus for analysis using tomography. This is unlike conventional autofocus, which just works on an image-by-image basis. The tools of compressive sensing can be used to concurrently select pixels for bright image elements that are stable and coherent over all images, as these pixels are sparse in the image domain, and calculate the phase errors present in each pass. Using the multipass data after autofocus, height distributions for scatterers in single pixels are determined for simulated forest scenes at X-band. The performance of the autofocus algorithm is examined through numerical simulations and is also applied to real data collected from Selex ES’s airborne, X-band, experimental SAR system. The experimental results demonstrate that the algorithm effectively achieves an autofocus solution. By finding the vertical distribution of two scatterers in a single pixel over simulated forestry images we can determine if compressive sensing can be utilized to gain information on scatterers below the canopy at X-band in future studies.
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