Spectral-factorisation Root-MUSIC algorithm for super-resolution ISAR imaging

Abstract

Inverse synthetic aperture radar (ISAR) is widely used in both military and civil fields recently. The fast Fourier transformation is the conventional method to obtain high-resolution ISAR images, but it is Rayleigh limited and has high sidelobes. Root-MUSIC served as a super-resolution method can break the Rayleigh limit. Whereas Root-MUSIC has computational redundancy as its roots are presented as conjugated pairs. Here, a spectral factorisation Root-MUSIC (SF-Root-MUSIC) is proposed for super-resolution ISAR imaging. SF-Root-MUSIC efficiently reduces the order of rooting-polynomial of Root-MUSIC into half and has a similar resolution ability as Root-MUSIC. The computational complexity of SF-Root-MUSIC is significantly reduced compared to Root-MUSIC. Simulated signal, MIG-25 data, and real measured Yak-42 data are employed to validate the validity of the proposal.

Keywords

• synthetic aperture radar
• motion compensation
• image resolution
• polynomials
• fast fourier transforms
• radar imaging
• real measured yak-42 data
• mig-25 data
• simulated signal
• fast fourier transform
• rooting-polynomial
• sf-root-music
• spectral factorisation root-music
• super-resolution method
• high-resolution isar images
• super-resolution isar imaging
• factorisation root-music algorithm