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Minimum Entropy SAR Autofocus

Ali F. Yegulalp
MIT Lincoln Laboratory
244 Wood Street
Lexington, MA 02420
tel: (781) 981-0886
email: yegulalp@ll.mit.edu

Abstract A common problem for synthetic aperture radar (SAR) is focus degradation due to unknown or uncompensated platform motion. Algorithms that adaptively correct such focusing errors are generically referred to as autofocus algorithms. In this talk, we present a new autofocus algorithm based on the minimum-entropy principle. 

Unlike other autofocus algorithms, the minimum-entropy approach does not rely on using point-scatterers in the image to estimate phase errors. Instead, it searches a parameterized space of phase corrections using entropy as a measure of focus quality. Consequently, very little image contrast is required to focus images. In fact, substantial phase errors can be reliably corrected even when the image consists of pure noise (provided the noise is not exactly Gaussian). With reasonable contrast levels in the image, extremely severe phase errors can be routinely corrected. 

We will show examples of minimum-entropy autofocus applied to both real and synthetic SAR images. We also compare minimum-entropy to phase-gradient autofocus (PGA), currently a popular choice for SAR autofocus. PGA relies on finding point scattering centers in the image, so it performs poorly with severely defocused or low contrast images. PGA is also incapable of correcting two-dimensional focusing errors, which is easily done using minimum-entropy. On the other hand, PGA requires less computation than minimum-entropy.

Presentation (pdf format)



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