Highway gantries identify jammers

Recent years have seen an increase in drivers turning to cheap GNSS jamming devices in order to move around undetected or to thwart built-in anti-theft systems or road tolling systems. These jammers not only knock out their own GNSS receiver, they also block GNSS signal reception in a radius of several hundred of meters.There is a growing demand for automatic detection of these illegal jammers to help catching the offending driver.

Septentrio GNSS antenna placement on highway gantry. (Photo: Septentrio)

An ION GNSS+ 2018 presentation by Wim de Wilde and Jean-Marie Sleewaegen presentation showed how a multi-antenna GNSS receiver with built-in RF spectrum monitor and adequate processing tool can efficiently detect and classify jamming events and identify the offending car or truck. They conducted a five-day test with two Septentrio AsteRx-U dual-antenna receivers installed on an overhead structure above a busy highway.

In parallel to the GNSS tracking and built-in anti-jam functionality, the AsteRx-U can simultaneously sample the RF signal from its two antennas. One of the objectives of the test was to evaluate the possibility to perform lane detection by cross-correlating the jamming signal received by the two antennas. In addition, the antennas were mounted with a significant inclination angle to create an asymmetrical reception pattern.

The goal was to assess the feasibility of detecting the driving direction from the time series of the received jammer power. Such lane or direction detection would greatly help identifying the offending driver in heavy traffic conditions when more than one vehicle crosses the overhead structure at the time of the jamming.

Over the five days of the experiment, 45 jamming events were recorded and analyzed, most of them intentional: continuous wave, chirp or even less-known pulse jammers.

Chirp jammer example. (Charts: Septentrio)

The researchers explained how the jamming events are automatically detected and classified by the processing tool, using pattern recognition to distinguish between intentional harmful events and unintentional interferences. They presented selected cases illustrating the RF signature of the most prevailing types of jammer.

They then addressed the direction and lane sensing algorithm and discussed the effect of multipath propagation of the jammer signal. All algorithms are illustrated with real-life examples.

For further information on this case study, see www.ion.org/publications/browse.cfm.