GNSS Chirp Interference Estimation and Mitigation
D.L.J. Kappelle (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Jan-Joris van Es – Mentor (Royal Netherlands Aerospace Centre NLR)
A.J. van der Veen – Graduation committee member (TU Delft - Signal Processing Systems)
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Abstract
GNSS receivers can suffer severely from radio frequency interference (RFI). RFI can introduce errors in the position and time calculations or if the interference is very severe, can lead to a total loss of GNSS reception. This vulnerability of GNSS can have large implications on critical infrastructure such as power plants, telephony, aviation or search and rescue operations. RFI is a real threat to GNSS as many interfering incidents are reported every day. A common type of RFI is chirp interference, which is a sweep over a wide range of frequencies that overlap with the frequencies used by GNSS. This is often emitted by cheap Personal Privacy Devices that can be bought online. The question in this thesis was how well such interference can be modelled and if modelling could help mitigation against it. This thesis consists of two main parts. In the first part a novel estimator is proposed that assumes a mathematical model of a chirp and estimates its parameters from recordings of chirps. The estimator has shown to work well in simulations for chirps with an SNR of −9 dB or more. On real recordings the estimates were accurate for 66.7 % of the signals. In the second part the estimator was used to derive a filter. The filter is based on the subtraction of a replica of the chirp interference from the received signal. It uses the proposed estimator to create the replica. In simulations, the filter is able to improve correlation strength by up to 7 dB. On real recordings the performance was worse as for only 46 % of the recordings the GNSS correlation was increased. Both the estimator and filter have many ways in which they could be improved. The estimator can be improved to allow for more complex chirps, which would in turn improve the filter. Both can also be made more computationally efficient. Furthermore, in order to get a better understanding of Personal Privacy Devices, one such device has been tested. It was found that the signal from the device was very unstable and changed much over time, it was also highly dependent on ambient temperature.