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Title: An intelligent joint filter for vector tracking loop considering noise interference
Authors: Dou, J
Xu, B 
Dou, L
Issue Date: Oct-2020
Source: Optik, Oct. 2020, v. 219, 164984
Abstract: In this paper, we propose an intelligent joint filter (JF) for enhancing the performance of vector tracking loop (VTL) in the Global Navigation Satellite System (GNSS). The JF combines the advantages of extended Kalman filter (EKF) and unbiased finite-impulse response (UFIR) filter. To this end, a supervised machine learning algorithm, named Gaussian mixture model (GMM) clustering, was used for providing excellent joint strategy. Those three types of filter-based vector tracking loop were first implemented and then processed with a set of raw satellite signals based on the software-defined receiver (SDR). Finally, comparative analyses and results of the tracking performance of EKF/UFIR/JF were carried out. Results show that the EKF-VTL has optimal tracking performance but sensitive to the noise statistics, which means it's not robust. The UFIR-VTL is suboptimal but more robust compare to EKF-VTL. The proposed JF-VTL is both optimal and robust.
Keywords: Extended Kalman filter (EKF)
Gaussian mixture model (GMM) clustering
Global Navigation Satellite System (GNSS)
Joint filter (JF)
Unbiased finite-impulse response (UFIR) filter
Vector tracking loop (VTL)
Publisher: Urban & Fischer
Journal: Optik 
ISSN: 0030-4026
EISSN: 1618-1336
DOI: 10.1016/j.ijleo.2020.164984
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Embargo End Date 2022-10-31
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