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An Efficient Calibration Algorithm for Large Aperture Array Position Errors in a GEO SAR

delete2018-09-01
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PRE
AI
L
Lihuan Huo *
廖
廖桂生 (Guisheng Liao)
Z
Zhiwei Yang
Q
Qingjun Zhang
DOI:10.1109/LGRS.2018.2843536delete
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摘要

摘要

En 中文
Geosynchronous orbit synthetic aperture radar (GEO SAR) plays an important role in wide-area surveillance and the continuous coverage of areas containing targets of interest. However, the relative position errors of large aperture arrays will distort the antenna pattern, which significantly degrades the target detection performance in a GEO SAR system. To address this issue, most of the conventional calibration methods are focused on the independent errors, without consideration of the parametric error model, which may increase the position estimation errors. To solve this problem, an efficient calibration algorithm for position errors in a GEO SAR is proposed in this letter. For the large antenna arrays, the parametric error model is first established. Then, the calibration method is performed to estimate the parameters of the position error model. Based on this, the accurate position errors can be obtained by the least-squares algorithm. Compared with the conventional methods, the target detection performance of a GEO SAR system can be significantly improved after the precise array position error compensation by the proposed algorithm. Moreover, the proposed algorithm transforms the estimation from 3-D positions to the finite parameters of the error model, which can considerably decrease the computational complexity and obtain a more accurate estimation of the position errors simultaneously. Several simulated results are presented to validate the proposed algorithm for the position error correction in a GEO SAR system.
Keyword:
Array-error calibration
array signal processing
synthetic aperture radar (SAR) data
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期刊

IEEE Geoscience and Remote Sensing Magazine 封面图
IEEE Geoscience and Remote Sensing Magazine
IF:
16.4
论文数:
1.0W
被引数:
5.1K

机构

X
Xidian University
学者数:
2.4W
论文数: 1.9W
被引数: 9.7K
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