条带式合成孔径声呐相位梯度自聚焦算法

Phase gradient autofocus algorithm applied to stripmap SAS

相位梯度自聚焦(PGA)算法是一种稳健的高分辨SAR相位校正方法,在SAR领域得到广泛应用;但PGA是针对聚束式合成孔径雷达提出的,不能直接用于条带式合成孔径声呐,为补偿小运动误差对合成孔径声纳图像造成的影响,文中考察了条带式合成孔径声呐运动误差模型和聚束合成孔径声呐运动误差模型之间的内在关系,推导了条带式合成孔径声呐的误差模型,提出了把相位梯度自聚焦应用到条带式合成孔径声呐的相应方式.首先,将条带式合成孔径声呐的成像沿方位向分割为有重叠的小的条带,然后,用聚束式PGA算法分别对各分块后的图像进行自聚焦处理,最后将各聚焦后的分块图像以适当的方式拼接在一起形成新的图像,从而实现条带模式合成孔径声呐的PGA处理.水池数据的处理证实了本文方法的可行性和有效性.

The phase gradient autofocus （PGA） algorithm used widely in SAR community is proven to be robust and is a superior phase error correction method for fine resolution SAR. The PGA technique for spotlight mode imaging, however, cannot be directly applied to image formed in the stripmap model. To compensate the sonar platform movement errors that will distort the SAS image, based on the inherent relations between spotlight SAS （ Synthetic Aperture Sonar） and stripmap SAS, a movement error model for stripmap SAS is provided and PGA can be applied to stripmap SAS in following manners. Firstly, the stripmap SAS images are divided in along-track into many overlapping strips. Next, each strip is focused by spotlight PGA. Finally the focused strips are pieced together to form the new image and the PGA processing for stripmap SAS is complished. The processing results of pool data show the feasibility and validity of the proposed method.