TDOA-based localization with NLOS mitigation via robust model transformation and neurodynamic optimization

Wenxin Xiong, Christian Schindelhauer, Hing Cheung So, Joan Bordoy, Andrea Gabbrielli, Junli Liang

Research output: Contribution to journalArticlepeer-review

46 Scopus citations

Abstract

This paper revisits the problem of locating a signal-emitting source from time-difference-of-arrival (TDOA) measurements under non-line-of-sight (NLOS) propagation. Many currently fashionable methods for NLOS mitigation in TDOA-based localization tend to solve their optimization problems by means of convex relaxation and, thus, are computationally inefficient. Besides, previous studies show that manipulating directly on the TDOA metric usually gives rise to intricate estimators. Aiming at bypassing these challenges, we turn to retrieve the underlying time-of-arrival framework by treating the unknown source onset time as an optimization variable and imposing certain inequality constraints on it, mitigate the NLOS errors through the ℓ1-norm robustification, and finally apply a hardware realizable neurodynamic model based on the redefined augmented Lagrangian and projection theorem to solve the resultant nonconvex optimization problem with inequality constraints. It is validated through extensive simulations that the proposed scheme can strike a nice balance between localization accuracy, computational complexity, and prior knowledge requirement.

Original languageEnglish
Article number107774
JournalSignal Processing
Volume178
DOIs
StatePublished - Jan 2021

Keywords

  • Neural network
  • Non-line-of-sight
  • Nonconvex optimization
  • Robust model transformation
  • Source localization
  • Time-difference-of-arrival

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