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Ground moving target indication (GMTI) is one of the important applications of synthetic aperture radar (SAR). Due to the finite pulse sampling rate in azimuth, the azimuth ambiguity appears in spaceborne SAR systems, which not only reduces the image quality but also affects the accuracy of moving target detection. The existing ambiguity suppression methods cannot retain moving targets which are overlapped with ambiguity, or cannot suppress the ambiguity in SAR-GMTI. To overcome the shortcomings of existing methods, this letter proposes an effective azimuth ambiguity suppression method based on local azimuth ambiguity-to-signal ratio (AASR) estimation and ambiguity focusing for SAR-GMTI. This method can effectively suppress the ambiguity and is applicable to the situations where the moving target is overlapped with the ambiguity, large scenes, and higher order ambiguity. Finally, the effectiveness of the proposed method is verified and demonstrated by the experimental results on GaoFen-3 SAR. [ABSTRACT FROM AUTHOR]

Azimuth ambiguity greatly affects the image quality and application of synthetic aperture radar (SAR). Several azimuth ambiguity suppression methods have been proposed; however, these methods cannot remove the ambiguity completely and only the first-order ambiguity has been considered. Hence, in this letter, a novel method based on Wiener filtering and local azimuth ambiguity-to-signal ratio (AASR) estimation for $N$ -order azimuth ambiguity suppression is proposed. The Wiener filtering is used to attenuate the ambiguity energy. Then the original image and the filtered image are combined to estimate local AASR, which is used to identify the ambiguity pixel. Finally, the ambiguity can be removed via interpolation. Through this method, the $N$ -order ambiguity energy can also be suppressed to a lower level, and simultaneously, the consistency, resolution, and signal-to-noise ratio of an SAR image are maintained. Furthermore, in order to verify the practicability of the proposed method, it has been tested on the GaoFen-3 image and TerraSAR-X image. [ABSTRACT FROM AUTHOR]