Abstract
This paper deals with the SAR autofocus problem, with the aim to implement the proposed autofocus algorithm in real-time. The crux of the SAR autofocus problem is to estimate the phase error e (·) based on the uncompensated SAR raw signal, and subsequently eliminate the phase error from the SAR data. The existing autofocus algorithms are mostly performed off-line as part of the post-processing step. This is due to the fact that the conventional autofocus methods require the entire synthetic aperture length for phase error estimation and hence, they are time-consuming and not practical for real-time implementation. The proposed algorithm, called the Multiple Phase Difference (MPD) Autofocus, utilises the maximum likelihood (ML) principle to solve the phase estimation problem. It is shown that the ML solution can essentially achieve better performance with minimum computational burden by employing the sub-aperture approach.
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