Tackling the GNSS jamming problem using a particle filter algorithm

GNSS Jammers are devices that generate RF noise in the carrier frequency of the GNSS (e.g.,1.57GHz for L1 GPS). Jamming behavior can be characterized by a sharp degradation in the SNR of the GNSS satellites. In this paper we suggest a Bayesian particle filter approach for jamming detection and localization using crowd-sourcing of Smart-Phones GNSS data. The presented algorithm computes in real-time a probabilistic map of the jammers´ positions. Since a 2D histogram distribution space is generated, multiple jammers can be detected simultaneously. The presented algorithm can cope with various types of jammers - no pre-assumptions are made regarding the propagation pattern of the jammer. Experimental results showed that in less than two minutes and using only 3 smart-phones, our algorithm correctly detected a GNSS jammer within a 2 meters range in a Region-of-Interest (ROI) of 5000 m².