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Airborne spotlight synthetic aperture radar (Spotlight SAR) is a high resolution imaging mode of SAR. The basic principle is to achieve decimeter-level high-resolution imaging by continuous observation of fixed areas. This high-resolution imaging has extremely important applications in military and civilian fields, especially in target interpretation and recognition. However, with the improvement of resolution, in the long-term observation process, the moving target in the observation area will produce longer and more complex defocus and offset signals. When these signals cover important objects such as urban buildings, the effect of image interpretation and target recognition will be significantly reduced. In view of the above problems, two methods of moving target interference suppression are proposed in this paper. The first method is to combine the relative velocity with the range Doppler algorithm (RDA), and adjust the relative velocity parameters iteratively so that the defocused moving target can be refocused. In each iteration, the refocusing effect of the moving target is tested and judged by the maximum amplitude index. After the refocusing is completed, the refocused moving target is separated by the mask, and the original moving target echo signal is recovered by the inverse echo operation. Finally, by subtracting the recovered moving target echo from the original echo signal, the echo signal of the static scene can be obtained, and then the range Doppler algorithm is used to realize the imaging of the static scene. The second method is a moving target interference suppression method based on image sequence. The method obtains an image sequence by segmenting the sub-aperture, detects and generates a mask in the sub-aperture image sequence to remove the defocusing moving target signal, and then accumulates the processed image sequence complex data to generate a high-resolution static scene image. In this paper, semi-physical synthetic data are used for experiments, and evaluated by correlation and coherence operations. The experimental results show that the first method can effectively separate the moving target from the static scene echo and the effect is better than the second method.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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