Noise-Robust Range Alignment Method for Inverse Synthetic Aperture Radar Based on Aperture Segmentation and Average Range Profiles Correlation

Abstract

<jats:title>Abstract</jats:title> <jats:p>Range alignment is an essential procedure in translation motion compensation of inverse synthetic aperture radar imaging. Global optimization or maximum correlation-based algorithms have been used to realize range alignment. However, it is still challenging to achieve range alignment in low signal-to-noise ratio scenarios, which are common in inverse synthetic aperture radar imaging. In this paper, a novel anti-noise range alignment approach is proposed. In this new method, the target's motion is modelled as a uniformly accelerated motion during a short sub-aperture time. Minimum entropy optimization is implemented to estimate the motion parameters in each sub-aperture. These estimated parameters can be used to align the profiles of the current sub-aperture. Once each sub-aperture's range profiles are aligned, the noncoherent accumulation gain is obtained by averaging all profiles in each sub-aperture, which can be used as valuable information. The accumulation and correlation method is applied to align the average range profiles of each sub-aperture for the reason that the former step mainly focuses on alignment within the sub-apertures. Experimental results based on simulated and real measured data demonstrate the effectiveness of the proposed algorithm in low signal-to-noise ratio scenarios.</jats:p>