Method of Removing the Cross-correlation Noise for Dual-input and Dual-output SAR
Huang Ping-ping
(College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China)
Guide null
Abstract According to analysis of separating the mixed echo by suppressing the cross-correlation noise in dual-input and dual-output SAR system, a new method based on threshold filter and inverse filter was proposed. The method can eliminate the most energy of cross-correlation noise by threshold filter, which can suppress the cross-correlation noise well. The principle and implementation steps are presented in detail. The computer simulation and account for the integrated sidelobe ratio showed the effectiveness of the proposed method.
Key words : SAR
Threshold filter
Inverse filter method
Cross-correlation noise
Received: 2012-01-12;
Published: 2012-04-06
Fund: null
Cite this article:
Huang Ping-ping. Method of Removing the Cross-correlation Noise for Dual-input and Dual-output SAR[J]. JOURNAL OF RADARS, 2012, 1(1): 91-95.
[1]
Ding Baiyuan, Wen Gongjian, Yu Liansheng, Ma Conghui. Matching of Attributed Scattering Center and Its Application to Synthetic Aperture Radar Automatic Target Recognition [J]. JOURNAL OF RADARS, 2017, 6(2): 157-166.
[2]
Zeng Lina, Zhou Deyun, Li Xiaoyang, Zhang Kun. Novel SAR Target Detection Algorithm Using Free Training [J]. JOURNAL OF RADARS, 2017, 6(2): 177-185.
[3]
Hu Dingsheng, Qiu Xiaolan, Lei Bin, Xu Feng. Analysis of Crosstalk Impact on the Cloude-decomposition-based Scattering Characteristic [J]. JOURNAL OF RADARS, 2017, 6(2): 221-228.
[4]
Xu Feng, Wang Haipeng, Jin Yaqiu. Deep Learning as Applied in SAR Target Recognition and Terrain Classification [J]. JOURNAL OF RADARS, 2017, 6(2): 136-148.
[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 [J]. JOURNAL OF RADARS, 2017, 6(2): 195-203.
[6]
Zhao Junxiang, Liang Xingdong, Li Yanlei. Change Detection in SAR CCD Based on the Likelihood Change Statistics [J]. JOURNAL OF RADARS, 2017, 6(2): 186-194.
[7]
Gu Wenkun, Wang Dangwei, Ma Xiaoyan. Distributed MIMO-ISAR Sub-image Fusion Method [J]. JOURNAL OF RADARS, 2017, 6(1): 90-97.
[8]
Hu Jingqiu, Liu Falin, Zhou Chongbin, Li Bo, Wang Dongjin. CS-SAR Imaging Method Based on Inverse Omega-K Algorithm(in English) [J]. JOURNAL OF RADARS, 2017, 6(1): 25-33.
[9]
Zhou Yejian, Zhang Lei, Wang Hongxian, Xing Mengdao. Performance Analysis on ISAR Imaging of Space Targets [J]. JOURNAL OF RADARS, 2017, 6(1): 17-24.
[10]
Hong Wen. Hybrid-polarity Architecture Based Polarimetric SAR: Principles and Applications (in Chinese and in English) [J]. JOURNAL OF RADARS, 2016, 5(6): 559-595.
[11]
Sun Xun, Huang Pingping, Tu Shangtan, Yang Xiangli. Polarimetric SAR Image Classification Using Multiple-feature Fusion and Ensemble Learning [J]. JOURNAL OF RADARS, 2016, 5(6): 692-700.
[12]
Zhang Jingjing, Hong Wen, Yin Qiang. Robust Distributed-target-based Calibration Method for Polarimetric SAR Using Spherically Truncated Covariance Matrix [J]. JOURNAL OF RADARS, 2016, 5(6): 701-710.
[13]
Zhao Tuan, Deng Yunkai, Wang Yu, Li Ning, Wang Xiangyu. Processing Sliding Mosaic Mode Data with Modified Full-Aperture Imaging Algorithm Integrating Scalloping Correction [J]. JOURNAL OF RADARS, 2016, 5(5): 548-557.
[14]
Chen Wenfeng, Li Shaodong, Yang Jun, Ma Xiaoyan. Multiple Measurement Vectors ISAR Imaging Algorithm Based on a Class of Linearized Bregman Iteration [J]. JOURNAL OF RADARS, 2016, 5(4): 389-401.
[15]
Du Kangning, Deng Yunkai, Wang Yu, Li Ning . Medium Resolution SAR Image Time-series Built-up Area Extraction Based on Multilayer Neural Network [J]. JOURNAL OF RADARS, 2016, 5(4): 410-418.