ISAR imaging of targets with rotating parts based on robust principal component analysis

Abstract

In inverse synthetic aperture radar (ISAR) imaging, the radar echoes from the rotating parts on a target may contaminate the ISAR image of the main body. The performance of the autofocus algorithms may also be degraded when the interference is too strong, for the commonly used rigid-body motion assumption is not satisfied. In this study, a novel method based on the robust principal component analysis is proposed for ISAR imaging of targets with rotating parts. The low rank property of the main body returns and the sparse property of the rotating components in a certain range cell are utilised to separate them from each other. The separated echoes are then refocused to produce a high-resolution target image and estimate the rotating parameters. Experimental results with both simulated and measured data have demonstrated the effectiveness of the proposed method.