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Dynamic multipath mitigation applying unscented Kalman filters in local positioning systems

  • Thorsten Nowak (a1) and Andreas Eidloth (a1)
Abstract

Multipath propagation is still one of the major problems in local positioning systems today. Especially in indoor environments, the received signals are disturbed by blockages and reflections. This can lead to a large bias in the user's time-of-arrival (TOA) value. Thus multipath is the most dominant error source for positioning. In order to improve the positioning performance in multipath environments, recent multipath mitigation algorithms based upon the concept of sequential Bayesian estimation are used. The presented approach tries to overcome the multipath problem by estimating the channel dynamics, using unscented Kalman filters (UKF). Simulations on artificial and measured channels from indoor as well as outdoor environments show the profit of the proposed estimator model. Furthermore, the quality of channel estimation applying the UKF and the channel sounding capabilities of the estimator are shown.

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Corresponding author
Corresponding author: T. Nowak Email: thorsten.nowak@iis.fraunhofer.de
References
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International Journal of Microwave and Wireless Technologies
  • ISSN: 1759-0787
  • EISSN: 1759-0795
  • URL: /core/journals/international-journal-of-microwave-and-wireless-technologies
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