Jammer Localization: From Crowdsourcing to Synthetic Detection

Urban area GNSS in‐car‐jammer localization based on pattern recognition

“…The performance of locating a jamming source is analyzed by the following metrics: the correct localization rate, the average localization error, and the judgment accuracy to the motion state of the jammer. The performance is also compared with the C/N 0 variation model method proposed by Borio et al The C/N 0 variation model method is also a network‐based jamming localization method that uses C/N 0 as the monitoring metric. It utilizes a classical log‐normal shadowing loss model as a general model to estimate the signal transmitting loss in different environments.…”

“…This parameter is widely used as a metric for interference detection but seldom used for interference localization. Borio et al proposed a method that estimates the localization of interference based on the model of the effective C/N 0 variation with the distance between the interference source and the monitoring node . The model is established by combining the C/N 0 versus interference power model and the log‐normal shadowing model.…”

“…Borio et al proposed a method that estimates the localization of interference based on the model of the effective C/N 0 variation with the distance between the interference source and the monitoring node. 15 The model is established by combining the C/N 0 versus interference power model and the log-normal shadowing model. The localization accuracy of this method is affected by the consistency of the model and the real environment, so it is difficult to locate the interference precisely in multiple environments.…”

Situational Awareness: Mapping Interference Sources in Real-Time Using a Smartphone App

“…By using the PVT results and the C/N 0 from the crowd-sourced, the possibility of detecting and localizing the source of the jammer was demonstrated in [17,51]. In those studies, the information (i.e., C/N 0 ) is extracted from the GNSS receiver embedded in smartphone.…”

“…We can see that all the PRNs which have C/N 0 in the same range can use the same reference distribution without significant effect on the algorithm. For example, signals in the range [48,49,50,51,52,53,54,55,56,57,58,59] dBHz (e.g., PRN 1, PRN 5, PRN 6 and PRN 10) could use the reference distribution of PRN 5 with limited false alarm rate. However, from the table, we can also realize that the lower the C/N 0 value, the higher the false alarm rate.…”