An Improved Pre-processing Algorithm For Resolution Optimization In Galileo Based Bistatic SAR

Zhangfan Zeng, Zhiming Shi, Yanling Zhou, Yuzhang Chen, Yongcai Pan
2019 IEEE Access  
Galileo based bistatic synthetic aperture radar (Galileo-BiSAR) is a subclass of passive radar, where the Galileo satellite is used as the transmitter and the receiver can be mounted on a moving vehicle or fixed on the ground. Such scheme is cost-effective and safe, and therefore can be widely used in military areas such as battle monitoring or civil scenarios such as ground deformation monitoring, change detection, etc. However, the drawback of Galileo-BiSAR system lies on the fact that, same
more » ... he fact that, same with other passive radar, the transmitter and receiver are separately located, which introduces the necessity of pre-processing before further processing such as image formation. The existing pre-processing algorithm for Galileo-BiSAR system was established on the idea that only single band E5b signal is used. Due to its bandwidth of 20MHz, however, the range resolution of resulting image is merely up to 15m, which is not acceptable for most applications. Aimed at improving range resolution, this article proposes a new pre-processing algorithm for Galileo-BiSAR. Rather than employing the single band signal as well as duplicate of transmitting signal as the local reference signal in tradition way, a novel technique of dual matched filtering is proposed for pre-processing. Full simulations of the proposed pre-processing and further image formation were both performed. The results demonstrated the full functionality of the proposed pre-processing algorithm and verified its capability of higher resolution imaging against traditional algorithm. In addition, the paper also highlights its good computational efficiency and performance limitation against noise. Bistatic SAR, pre-processing, resolution. 122972 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ VOLUME 7, 2019 INDEX TERMS
doi:10.1109/access.2019.2938238 fatcat:64s4qzew3rfi3ahl6mjbac377e