Multiple Frequency Tracking Method Based on the Cardinalised Probability Hypothesis Density Filter with Cardinality Compensation

Sun Young Kim

Peer Reviewed

Abstract:	In this paper, a multiple frequency tracking method based on a cardinalised probability hypothesis density (CPHD) filter with cardinality compensation and fuzzy-c mean (FCM) clustering is proposed to precisely estimate multiple interference frequencies in the received global navigation satellite system (GNSS) signal. The cardinality refers to the number of elements in the data set (the number of the target) and estimation accuracy on the cardinality has an effect on the tracking performance of the CPHD filter. For that reason, cardinality compensation process is added to the filter, which is based on information fusion step using estimated cardinality obtained from the CPHD filter and measured cardinality obtained by finding the peak in the power spectrum density using the projection analysis image of a reassigned smoothed pseudo Wigner-Ville distribution (RSPWVD). In the general CPHD filter, k-means clustering is used to calculate the cardinality. However, track loss occurs when there are severe noise and clutters in the measurement. Thus, FCM clustering, which is robust to noise or measurement disturbance, is also applied to the CPHD filter to reduce the track loss. The proposed tracking method is evaluated by theoretical analysis including simple simulations. It is confirmed that the proposed algorithm has better tracking performance of multiple frequencies compared to the conventional CPHD filter based method.
Published in:	Proceedings of the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2018) September 24 - 28, 2018 Hyatt Regency Miami Miami, Florida
Pages:	3504 - 3517
Cite this article:	Kim, Sun Young, "Multiple Frequency Tracking Method Based on the Cardinalised Probability Hypothesis Density Filter with Cardinality Compensation," Proceedings of the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2018), Miami, Florida, September 2018, pp. 3504-3517. https://doi.org/10.33012/2018.16110
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