An Adaptive Kalman Filtering Approach to Fourier Analysis for Estimating Various Chirp-Type GNSS Interference Frequencies

Chang Ho Kang, Sun Young Kim, Chan Gook Park

Peer Reviewed

Abstract: An adaptive Kalman filter-based frequency estimation algorithm is proposed to track the frequency of a chirp-type global navigation satellite system (GNSS) interference. The unknown interference frequency of GNSS in the received signal can be estimated by using received samples in the time domain, but the thus-obtained value can include a large amount of error caused by measurement noise and by the frequency change rate of the interference. In order to reduce the error, an adaptive fading Kalman filter that is based on the Fourier series is applied in the proposed algorithm. Furthermore, an optimal order-reduction algorithm is proposed to select the appropriate order of the Fourier series for the type of received interference. The performance of the tracking algorithm is verified by making comparisons with conventional algorithms. The proposed algorithm has better tracking performance than conventional algorithms when the jammer-to-signal ratio (J/S) is more than 30 dB, which would seriously affect the accuracy of the GNSS.
Published in: NAVIGATION: Journal of the Institute of Navigation, Volume 65, Number 1
Pages: 3 - 13
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