Imaging Formation Algorithm of the Ground and Space-Borne Hybrid BiSAR Based on Parameters Estimation from Direct SignalReport as inadecuate

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Mathematical Problems in Engineering - Volume 2014 2014, Article ID 294306, 11 pages -

Research Article

China Academy of Space Technology CAST, Beijing 100094, China

Radar Research Lab, Beijing Institute of Technology, Beijing 100081, China

Received 30 October 2013; Accepted 6 January 2014; Published 20 February 2014

Academic Editor: Jui-Sheng Lin

Copyright © 2014 Qingjun Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper proposes a novel image formation algorithm for the bistatic synthetic aperture radar BiSAR with the configuration of a noncooperative transmitter and a stationary receiver in which the traditional imaging algorithm failed because the necessary imaging parameters cannot be estimated from the limited information from the noncooperative data provider. In the new algorithm, the essential parameters for imaging, such as squint angle, Doppler centroid, and Doppler chirp-rate, will be estimated by full exploration of the recorded direct signal direct signal is the echo from satellite to stationary receiver directly from the transmitter. The Doppler chirp-rate is retrieved by modeling the peak phase of direct signal as a quadratic polynomial. The Doppler centroid frequency and the squint angle can be derived from the image contrast optimization. Then the range focusing, the range cell migration correction RCMC, and the azimuth focusing are implemented by secondary range compression SRC and the range cell migration, respectively. At last, the proposed algorithm is validated by imaging of the BiSAR experiment configured with china YAOGAN 10 SAR as the transmitter and the receiver platform located on a building at a height of 109 m in Jiangsu province. The experiment image with geometric correction shows good accordance with local Google images.

Author: Qingjun Zhang, Cheng Hu, Lixin Wu, Tao Zeng, and Teng Long



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