Crowdsourced Jammer Localization Using APBMs: Performance Analysis Considering Observations Disruption

Abstract

Global navigation satellite systems (GNSSs) are a vital technology for many applications. The received signals, how-ever, are weak and easily vulnerable to intentional/unintentional interference. Jamming signals are becoming a serious threat for GNSS users and the localization of the jammer is an effective countermeasure to such attacks. Congested areas are particularly sensitive to these kinds of attacks, but they also present an opportunity to leverage crowdsourced data for threat monitoring purposes. In this context, we foresee a system where agents navigate an area with the ability to transmit the measured signal power, information that can be leveraged for jamming localization purposes. We propose a crowdsourced-based scheme for jammer localization, based on a signal propagation model, enhanced through the use of physics-based path loss modeling and an augmented, data-driven, component. This method can outperform the maximum likelihood estimator in a realistic scenario, despite the limited knowledge of the propagation model. The disruptive effect on agents' own position estimation affects the final jammer localization outcome, which is evaluated in this paper. In the work, we provide extensive experimentation to measure the effect of denied or degraded positioning on crowdsourced estimation as a function of relevant parameters such as agents' positioning error, observation density, and measurement noise.