Abstract
Global Navigation Satellite Systems (GNSS) have been the primary positioning solution for unmanned aerial vehicles (UAV) due to their advantages of high accuracy, low cost, lightweight receiver, and global coverage. However, reliance on GNSS raises a safety concern considering the vulnerability to radio frequency interference (RFI), especially malicious attacks such as jamming.
In this work, we develop a Kalman Filter-based jamming detection algorithm utilising carrier-to-noise ratio (C/N0) measurements reported by a GNSS receiver as jamming indicators. The algorithm is integrated with previously developed aided inertial navigation systems (INS) and tested using the data collected in the open jamming event to assess its performance.
This work suggests novel methodologies for early detection and identification of jammed frequency bands to enable a safe handover from jamming-affected GNSS to either jamming-unaffected GNSS bands or the Phased Array Radio System (PARS)-based positioning.
In this work, we develop a Kalman Filter-based jamming detection algorithm utilising carrier-to-noise ratio (C/N0) measurements reported by a GNSS receiver as jamming indicators. The algorithm is integrated with previously developed aided inertial navigation systems (INS) and tested using the data collected in the open jamming event to assess its performance.
This work suggests novel methodologies for early detection and identification of jammed frequency bands to enable a safe handover from jamming-affected GNSS to either jamming-unaffected GNSS bands or the Phased Array Radio System (PARS)-based positioning.