SAR imaging in the presence of spectrum notches via fast missing data IAA

A synthetic aperture radar system operating in congested frequency bands suffers from radio frequency inter- ference (RFI) from narrowband sources. When RFI interference is suppressed by frequency notching, gaps are introduced into the fast time phase history. This results in a missing data spectral estimation problem, where the missing data increases sidelobe energy and degrades image quality. The adaptive spectral estimation method Iterative Adaptive Approach (IAA) has been shown to provide higher resolution and lower sidelobes than comparable methods, but at the cost of higher computationally complexity. Current fast IAA algorithms reduce the computational complexity using Toeplitz/Vandermonde structures, but are not applicable for missing data cases because these structures are lost. When the number of missing data samples is small, which often is the case in SAR with RFI, we use a low rank completion to restore the Toeplitz/Vandermonde structures. We show that the computational complexity of the proposed algorithm is considerably lower than the state-of-the-art and demonstrate the utility on a simulated frequency notched SAR imaging problem.
QC 20131111