Low-cost signal jammers, which are illegal in many countries but continue to be traded in the black market, emit weak global navigation satellite system (GNSS) jamming signals. This study proposes a method to detect, identify, and localise these signals based on a cloud-data processing technique in which the jamming detection, identification, and localisation are performed sequentially after gathering received signals from low-cost GNSS receivers distributed in a monitoring network. This technique consists of two steps: two-dimensional image correlation in the time-frequency domain for detection and identification, and signal correlation in the time domain and relevant processing for localisation. For this, technical descriptions of the proposed cloud-data processing method are first presented with various jamming scenarios. Then, numerical simulations are performed to evaluate the proposed method by generating satellite and jamming signals. The authors are thus able to verify the feasibility of the technique and conclude that, in practical applications, it would allow efficient and extensive monitoring of low-cost signal jammers.

中文翻译：

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用于干扰检测，识别和定位的GNSS云数据处理技术

低成本信号干扰器在许多国家都是非法的，但仍在黑市上交易，它们会发出微弱的全球导航卫星系统（GNSS）干扰信号。这项研究提出了一种基于云数据处理技术的检测，识别和定位这些信号的方法，其中在从分布在监控网络中的低成本GNSS接收机收集到接收到的信号后，依次执行干扰检测，识别和定位。该技术包括两个步骤：在时频域中进行二维图像相关以进行检测和识别，在时域中进行信号相关并进行相关处理以进行定位。为此，首先以各种干扰场景介绍所提出的云数据处理方法的技术描述。然后，通过生成卫星和干扰信号进行了数值模拟，以评估所提出的方法。作者因此能够验证该技术的可行性，并得出结论，在实际应用中，它将允许对低成本信号干扰器进行有效且广泛的监控。