Clutter suppression in synthetic aperture radar targets using the DFRFT and subspace methods with rank reduction

SAR based vibration estimation using discrete Fractional Fourier transform (DFRFT) analysis methods has gained attention in recent work on vibrometry. In the presence of significant clutter however, this estimation becomes challenging due to the presence of clutter induced peaks in the vibration spectra. In this paper, we incorporate rank reduction and filtering into a subspace DFRFT approach that results in significant peak enhancement along with an accompanying reduction in the associated mean square estimation errors when applied to simulated SAR and synthetic chirp data. The approach is further applied to vibration data gathered from a real GA-Lynx system and shown to produce a corresponding peak enhancement and clutter suppression in the vibration spectrum.