Enhanced Passive GNSS-Based Radar Imaging Based on Coherent Integrated Multi-Satellite Signals
Enhanced Passive GNSS-Based Radar Imaging Based on Coherent Integrated Multi-Satellite Signals
复制标题
基于相干集成多卫星信号的增强型无源 GNSS 雷达成像
DOI：
10.3390/s20030842
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发表时间：
2020-02
期刊：
影响因子：
3.9
通讯作者：
Zhou Feng
中科院分区：
文献类型：
--
作者：
Zheng Yu;Zhang Zhuxian;Feng Lu;Zhu Peidong;Zhou Feng
反射信号微弱是近年来发展起来的一种遥感工具——无源GNSS雷达（GNSS雷达）的主要问题之一。针对这一问题，提出了一种基于多星相干集成的增强GNSS雷达成像方案。在该方案中，为了避免监视天线的直接信号干扰，作为机会传输的卫星采用后向散射几何模型。为了在选定的卫星照射的目标感测区域内相干累积场景中心回波信号幅度，在进行平行距离压缩后，对各自的距离域进行坐标对齐算子，在该距离域内对齐伪随机噪声（PRN）码相。然后，将选定卫星的坐标对准距离压缩信号沿方位角域进行相干积分，提高成像增益，实现单态处理。理论分析和现场概念验证实验结果表明，与传统双基地成像方案和当前最先进的多图像融合方案相比，该方案具有更高的成像增益；与现有的多图像融合方案相比，该方案具有计算复杂度低、算法速度快的优点。
Weak reflected signal is one of the main problems in a recent developing remote sensing tool-passive GNSS-based radar (GNSS radar). To address this issue, an enhanced GNSS radar imaging scheme on the basis of coherently integrating multiple satellites is proposed. In the proposed scheme, to avoid direct signal interference at surveillance antenna, the satellites that used as transmission of opportunity are in backscattering geometry model. To coherently accumulate echo signal magnitudes of the scene center in the targeted sensing region illuminated by the selected satellites, after performing the paralleled range compressions, a coordinates alignment operator is performed to the respective range domains, in which, pseudorandom noise (PRN) code phases are aligned. Thereafter, the coordinates aligned range compressed signals of the selected satellites are coherently integrated along azimuth domain so that imaging gain is improved and azimuth processing can be accomplished in only one state operation. The theoretical analysis and field proof-of-concept experimental results indicate that compared to both conventional bistatic imaging scheme and the state-of-the-art multi-image fusion scheme, the proposed scheme can provide a higher imaging gain; compared to the state-of-the-art multi-image fusion scheme, the proposed scheme has a less computational complexity and faster algorithm speed.
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DOI：
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