From Event: SPIE Commercial + Scientific Sensing and Imaging, 2017
Direct image formation in synthetic aperture radar (SAR) involves processing of data modeled as Fourier coefficients along a polar grid. Often in such data acquisition processes, imperfections in the data cannot simply be modeled as additive or even multiplicative noise errors. In the case of SAR, errors in the data can exist due to imprecise estimation of the round trip wave propagation time, which manifests as phase errors in the Fourier domain. To correct for these errors, we propose a phase correction scheme that relies on both the on smoothness characteristics of the image and the phase corrections associated with neighboring pulses, which are possibly highly correlated due to the nature of the data off setting. Our model takes advantage of these correlations and smoothness characteristics simultaneously for a new autofocusing approach, and our algorithm for the proposed model alternates between approximate image feature and phase correction minimizers to the model.
Toby Sanders and Theresa Scarnati, "Combination of correlated phase error correction and sparsity models for SAR," Proc. SPIE 10222, Computational Imaging II, 102220E (Presented at SPIE Commercial + Scientific Sensing and Imaging: April 09, 2017; Published: 1 May 2017); https://doi.org/10.1117/12.2262861.
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