An intelligent joint filter for vector tracking loop considering noise interference

Jie Dou, Bing Xu, Lei Dou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Article number164984
Publication statusPublished - Oct 2020


  • 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)

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering

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