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Self-calibration for the time difference of arrival positioning

  • Juri Sidorenko
  • , Volker Schatz
  • , Dimitri Bulatov
  • , Norbert Scherer-Negenborn
  • , Michael Arens
  • , Urs Hugentobler
  • Fraunhofer Center for Machine Learning
  • Technical University of Munich
  • Object Recognition (OBJ)

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

The time-difference-of-arrival (TDOA) self-calibration is an important topic for many applications, such as indoor navigation. One of the most common methods is to perform nonlinear optimization. Unfortunately, optimization often gets stuck in a local minimum. Here, we propose a method of dimension lifting by adding an additional variable into the l2 norm of the objective function. Next to the usual numerical optimization, a partially-analytical method is suggested, which overdetermines the system of equations proportionally to the number of measurements. The effect of dimension lifting on the TDOA self-calibration is verified by experiments with synthetic and real measurements. In both cases, self-calibration is performed for two very common and often combined localization systems, the DecaWave Ultra-Wideband (UWB) and the Abatec Local Position Measurement (LPM) system. The results show that our approach significantly reduces the risk of becoming trapped in a local minimum.

Original languageEnglish
Article number2079
JournalSensors (Switzerland)
Volume20
Issue number7
DOIs
StatePublished - 1 Apr 2020

Keywords

  • Dimension lifting
  • Self-calibration
  • Time-difference-of-arrival (TDOA)

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