Focusing High-Maneuverability Bistatic Forward-Looking SAR Using Extended Azimuth Nonlinear Chirp Scaling Algorithm

被引:9
|
作者
Song, Xuan [1 ]
Li, Yachao [1 ]
Zhang, Tinghao [1 ]
Li, Lianghai [2 ]
Gu, Tong [1 ]
机构
[1] Xidian Univ, Natl Lab Radar Signal Proc, Xian 710071, Peoples R China
[2] Beijing Res Inst Telemetry, Beijing 100000, Peoples R China
关键词
Azimuth; Synthetic aperture radar; Imaging; Radar imaging; Doppler effect; Chirp; Transmitters; Acceleration; extended azimuth nonlinear chirp scaling (EANLCS); fast reference point selection (FRPS) method; high-maneuverability bistatic forward-looking synthetic aperture radar (HMBF-SAR); RESOLUTION; MODEL;
D O I
10.1109/TGRS.2022.3228803
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
In high-maneuverability bistatic forward-looking synthetic aperture radar (HMBF-SAR) imaging, the acceleration leads to an increased residual range curve and a deepened 2-D spatial variance of Doppler parameters, which cannot be processed by the traditional algorithms. To address these problems, this article establishes a more accurate digital representation for the HMBF-SAR model and investigates an extended azimuth nonlinear chirp scaling (EANLCS) imaging method. In the flowchart of this article, we first propose a more precise slant range model with improved expansion coefficients and define the range and azimuth direction of HMBF-SAR imaging. Then, a novel fast reference point (i.e., azimuth and range reference point) selection method is proposed to analyze the 2-D spatial variance of signal characteristics, which is used to construct a high-order model of residual range cell migration and Doppler parameters. Based on the above analysis, we put forward an advanced imaging algorithm of combining the second-order keystone and EANLCS to compensate for the increased residual range curve and 2-D spatial variance of Doppler parameters. Finally, the effectiveness of the proposed HBMF-SAR method is verified by several numerical simulations and comparative studies based on both the simulated and raw data.
引用
收藏
页数:14
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