Leaky LMS algorithm and fractional Brownian motion model for GNSS receiver position estimation

Jean Philippe Montillet*, Kegen Yu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

5 Citations (Scopus)

Abstract

This paper presents a new approach for smoothing long time series of position estimates of ground GNSS (global navigation satellite system) receivers. The fractional Brownian motion (fBm) model is employed to describe the position coordinate estimates that have long-range dependencies. A new and low-complexity method is proposed to estimate the Hurst parameter and the simulation results show that the new method achieves good accuracy and low complexity. A modified leaky least mean squares (ML-LMS) estimator is proposed to filter the long time series of the position coordinate estimates, which uses the Hurst parameter estimates to update the filter tap weights. Simulation results demonstrate that this ML-LMS estimator outperforms the classic LMS estimator considerably in terms of both accuracy and convergence.

Original languageEnglish
Title of host publicationVTC Fall 2011
Subtitle of host publication2011 IEEE Vehicular Technology Conference Fall
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)9781424483273, 9781424483266
ISBN (Print)9781424483280
DOIs
Publication statusPublished - 2011
Externally publishedYes
EventIEEE 74th Vehicular Technology Conference, VTC Fall 2011 - San Francisco, CA, United States
Duration: 5 Sep 20118 Sep 2011

Other

OtherIEEE 74th Vehicular Technology Conference, VTC Fall 2011
CountryUnited States
CitySan Francisco, CA
Period5/09/118/09/11

Keywords

  • GNSS positioning
  • fractional Browinian motion model
  • Hurst parameter estimation
  • modified leaky LMS estimator

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