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

Abstract

A GNSS in-car-jammer poses a great threat to the security of navigation satellite signals for civilian applications. Traditional interference localization methods such as received signal strength (RSS), signal arrival angle (AOA), and signal arrival time difference (TDOA) have poor performance in urban environments due to line-of-sight (LoS) path obstruction and reflection. In this paper, a new interference localization method, which works on monitoring networks and follows the pattern recognition principle is proposed. The algorithm locates the jammer by learning the carrier-to-noise density ratio features that are extracted from the data of all monitoring nodes of the network with the means of binary tree support vector machine (SVM) classification technology. Its performance is tested by two simulated interference scenarios: the urban canyon and the urban residential area. Results show that the algorithm provides better localization reliability and smaller position errors compared with traditional localization methods.