基于手机的GNSS干扰机参与式检测与定位

G. Olsson, Sara Nilsson, E. Axell, E. Larsson, Panos Papadimitratos
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引用次数: 0

摘要

众所周知,GNSS接收机容易受到干扰和欺骗攻击,在过去十年中,世界各地报道了许多此类事件。参与式感知或群体感知的概念是由大量自愿贡献者提供测量,而不是依赖专门的感知基础设施。在这项工作中考虑的参与式传感网络是基于嵌入在移动电话中的GNSS接收器。所提供的测量是指接收机报告的载波噪声密度比($C / N_{0}$)估计值或自动增益控制(AGC)值。在这项工作中,我们利用$C / N_{0}$测量值以众包方式使用多个接收器来定位GNSS干扰器。我们扩展了以前的干扰器位置估计器,只包括在传感器检测到干扰的传感期间接收的数据。此外,我们执行硬件测试,以验证和评估所提出的和比较的最先进的算法。利用三星S20+手机作为参与传感器和思博伦GSS9000 GNSS模拟器进行评估,产生GNSS和干扰信号。当使用$C / N_{0}$测量值时,所提出的算法显示出良好的效果,并且在评估的场景中优于其他算法,当路径损失指数为2时,产生的中位数误差为50米。路径损耗指数越高,误差越大。手机的AGC输出噪声太大,需要进一步处理才能用于位置估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Mobile Phones for Participatory Detection and Localization of a GNSS Jammer
It is well known that GNSS receivers are vulnerable to jamming and spoofing attacks, and numerous such incidents have been reported in the last decade all over the world. The notion of participatory sensing, or crowds ensing, is that a large ensemble of voluntary contributors provides measurements, rather than relying on a dedicated sensing infrastructure. The participatory sensing network under consideration in this work is based on GNSS receivers embedded in, for example, mobile phones. The provided measurements refer to the receiver-reported carrier-to-noise-density ratio ($C / N_{0}$) estimates or automatic gain control (AGC) values. In this work, we exploit $C / N_{0}$ measurements to locate a GNSS jammer, using multiple receivers in a crowdsourcing manner. We extend a previous jammer position estimator by only including data that is received during parts of the sensing period where jamming is detected by the sensor. In addition, we perform hardware testing for verification and evaluation of the proposed and compared state-of-the-art algorithms. Evaluations are performed using a Samsung S20+ mobile phone as participatory sensor and a Spirent GSS9000 GNSS simulator to generate GNSS and jamming signals. The proposed algorithm is shown to work well when using $C / N_{0}$ measurements and outperform the alternative algorithms in the evaluated scenarios, producing a median error of 50 meters when the pathloss exponent is 2. With higher pathloss exponents the error gets higher. The AGC output from the phone was too noisy and needs further processing to be useful for position estimation.
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