基于GPS反射信号的远程成像系统

M. Usman, D. Armitage
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引用次数: 6

摘要

本文描述了一种利用反射全球定位系统(GPS)信号在感兴趣区域内形成目标图像的方法。该原理是基于一种双静态合成孔径雷达(SAR),其中采用匹配滤波技术进行图像重建。这种方法依赖于这样一个事实,即来自单个目标的反射产生的接收信号的每个分量都受到唯一的啁啾。需要解决的一个主要挑战是与接收到的反射GPS信号相关的骇人听闻的信噪比。另一个困难是直接信号的自相关函数尾部的功率对反射信号的掩盖,不能完全抑制。此外,重构方法会产生不理想的点扩展函数(PSF),严重地涂抹重构图像。我们模拟整个GPS信号的产生和图像重建过程,尽可能忠实地在有限的计算能力。我们能够证明,只要允许足够的相干相关器积分时间,并且可以充分抑制直接信号,理论上可以从实际观测距离获得LI波长(19 cm)数量级的空间分辨率。对于模拟中相当简化的目标组织,我们能够证明,通过基于维纳滤波器的反卷积方法可以清除被PSF涂抹的图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Remote Imaging System Based on Reflected GPS Signals
This paper describes a method for utilizing reflected Global Positioning System (GPS) signals to form an image of targets within a region of interest. The principle is based upon a type of bi-static synthetic aperture radar (SAR) in which a matched filter technique is employed to perform the image reconstruction. This method relies upon the fact that each component of the received signal resulting from a reflection from an individual target is subjected to a unique chirp. A major challenge to be tackled is the appalling signal to noise ratio associated with the received reflected GPS signals. Another difficulty is the masking of the reflected signals by power in the tails of the autocorrelation function of the direct signals which cannot be totally suppressed. Moreover, the reconstruction method results in an undesirable point spread function (PSF) which seriously smears the reconstructed image. We simulate the entire GPS signal generation and image reconstruction process as faithfully as possible within the limitations of the available computational effort. We are able to demonstrate that a spatial resolution of the order of the LI wavelength (19 cm) is theoretically possible from realistic observation distances provided that sufficient coherent correlator integration time is allowed and that the direct signals can be sufficiently suppressed. For the rather simplified organization of targets within the simulation we are able to show that the image smeared by the PSF is able to be cleaned by means of a Wiener filter based deconvolution method.
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