Processing Sliding Mosaic Mode Data with Modified Full-Aperture Imaging Algorithm Integrating Scalloping Correction
Zhao Tuan*①② Deng Yunkai① Wang Yu① Li Ning① Wang Xiangyu①②
① (Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)② (University of Chinese Academy of Sciences, Beijing 100039, China)
Abstract In this study, we present a modified full-aperture imaging algorithm that includes scalloping correction and spike suppression for sliding-Mosaic-mode Synthetic Aperture Radar (SAR). It is innovational to correct the azimuth beam-pattern weighting altered by radar antenna rotation in the azimuth during the de-ramping preprocessing operation. The main idea of spike suppression is to substitute zeros between bursts with linear-predicted data extrapolated from adjacent bursts to suppress spikes caused by multiburst processing. We also integrate scalloping correction for the sliding mode into this algorithm. Finally, experiments are performed using the C-band airborne SAR system with a maximum bandwidth of 200 MHz to validate the effectiveness of this approach.
Key words ： Synthetic Aperture Radar (SAR)
Sliding Mosaic
Scalloping correction
Full-aperture imaging algorithm
Linear-prediction-model
Received: 2016-01-21;
Published: 2016-07-08
Fund: The National Natural Science Foundation of China (61422113)
Cite this article:
Zhao Tuan,Deng Yunkai,Wang Yu et al. Processing Sliding Mosaic Mode Data with Modified Full-Aperture Imaging Algorithm Integrating Scalloping Correction[J]. JOURNAL OF RADARS, 2016, 5(5): 548-557.
[1]
Ding Baiyuan, Wen Gongjian, Yu Liansheng, Ma Conghui. Matching of Attributed Scattering Center and Its Application to Synthetic Aperture Radar Automatic Target Recognition
[2]
Zeng Lina, Zhou Deyun, Li Xiaoyang, Zhang Kun. Novel SAR Target Detection Algorithm Using Free Training
[3]
Hu Dingsheng, Qiu Xiaolan, Lei Bin, Xu Feng. Analysis of Crosstalk Impact on the Cloude-decomposition-based Scattering Characteristic
[4]
Xu Feng, Wang Haipeng, Jin Yaqiu. Deep Learning as Applied in SAR Target Recognition and Terrain Classification
[5]
Wang Siyu, Gao Xin, Sun Hao, Zheng Xinwei, Sun Xian. An Aircraft Detection Method Based on Convolutional Neural Networks in High-Resolution SAR Images
[6]
Zhao Junxiang, Liang Xingdong, Li Yanlei. Change Detection in SAR CCD Based on the Likelihood Change Statistics
[7]
Gu Wenkun, Wang Dangwei, Ma Xiaoyan. Distributed MIMO-ISAR Sub-image Fusion Method
[8]
Hu Jingqiu, Liu Falin, Zhou Chongbin, Li Bo, Wang Dongjin. CS-SAR Imaging Method Based on Inverse Omega-K Algorithm(in English)
[9]
Zhou Yejian, Zhang Lei, Wang Hongxian, Xing Mengdao. Performance Analysis on ISAR Imaging of Space Targets
[10]
Hong Wen. Hybrid-polarity Architecture Based Polarimetric SAR: Principles and Applications (in Chinese and in English)
[11]
Sun Xun, Huang Pingping, Tu Shangtan, Yang Xiangli. Polarimetric SAR Image Classification Using Multiple-feature Fusion and Ensemble Learning
[12]
Zhang Jingjing, Hong Wen, Yin Qiang. Robust Distributed-target-based Calibration Method for Polarimetric SAR Using Spherically Truncated Covariance Matrix
[13]
Zhao Xiaohui, Jiang Yicheng, Zhu Tongyu. Target Segmentation Method in SAR Images Based on Appearance Conversion Machine
[14]
Wang Yanfei, Liu Chang, Zhan Xueli, Han Song. Technology and Applications of UAV Synthetic Aperture Radar System
[15]
Chen Wenfeng, Li Shaodong, Yang Jun, Ma Xiaoyan. Multiple Measurement Vectors ISAR Imaging Algorithm Based on a Class of Linearized Bregman Iteration
2016, Vol. 5 
