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.

范围对准是逆合成孔径雷达成像平移运动补偿中的必要步骤。全局优化或基于最大相关性的算法已用于实现范围对齐。然而，在低信噪比情况下实现距离对准仍然是挑战，这在逆合成孔径雷达成像中很常见。在本文中，提出了一种新的抗噪声范围对准方法。在这种新方法中，目标运动被建模为短子孔径时间内的均匀加速运动。实施最小熵优化以估计每个子孔径中的运动参数。这些估计的参数可用于对齐当前子孔径的轮廓。对准每个子光圈的范围轮廓后，非相干累积增益是通过平均每个子孔径中的所有轮廓而获得的，可以用作有价值的信息。由于前一步主要关注子孔径内的对准，因此采用累积和相关方法来对准每个子孔径的平均范围轮廓。基于模拟和实际测量数据的实验结果证明了该算法在低信噪比情况下的有效性。