Global navigation satellite system interference tracking and mitigation based on an adaptive fading Kalman filter

A time-domain signal tracking and mitigation algorithm is proposed to estimate the frequency of interference in a global navigation satellite system (GNSS) so as to classify different types of interference and to mitigate interference. The frequency of GNSS interference can be obtained using the properties of the trigonometric functions of received signal samples, but these values contain numerous errors caused by measurement noise and frequency changes associated with the interference. To reduce these errors, an adaptive fading Kalman filter is applied in the proposed algorithm. Furthermore, a low-pass differentiator and a pattern enhancement algorithm are used to estimate the sweep period of chirp-type interference, which is used to reset the filter parameter for estimating the frequency of the interference accurately. By estimating the sweep period, the interference identification logic is designed to select the proper system model of the Kalman filter. Finally, in order to mitigate the interference, the denoised frequency from the filter is used to design a notch filter which eliminates the interference in the received signal. The frequency tracking performance of the proposed algorithm is verified to compare with conventional algorithms and the mitigation performance of the proposed algorithm is evaluated by means of Monte Carlo simulations.