Abstract
Range alignment is an essential procedure in the 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 motion is modeled 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 the range profiles of each sub-aperture are aligned, the non-coherent 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 because the former step focuses mainly 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.
Highlights
Inverse synthetic aperture radar (ISAR), which is used to obtain images of non-cooperative and moving targets, has been widely applied in many civil and military domains in the last few decades [1,2,3,4,5]
A novel range alignment method was proposed in this article
The minimum entropy principle was applied as a metric, and the coordinate descent algorithm (CDA) with a proximal point updating scheme was implemented as the solver to estimate the motion parameters in each sub-aperture
Summary
Inverse synthetic aperture radar (ISAR), which is used to obtain images of non-cooperative and moving targets, has been widely applied in many civil and military domains in the last few decades [1,2,3,4,5]. ISAR achieves high resolutions in both the range and azimuth directions by exploiting the wideband characteristics and angular diversity during the coherent processing interval. During the process of imaging, a target’s movement can be divided into two parts: translational and rotational motion. It is well known that only the rotational motion contributes to imaging, while translational motion can cause blurring of the ISAR images and must be compensated for. The traditional range alignment methods can be sorted into three categories. To improve the performance of these methods in low SNR scenarios, [10] proposed that the average of aligned profiles can be used as a reference.
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