Convex Relaxation Approaches to Robust RSS-TOA Based Source Localization in NLOS Environments

Wenxin Xiong*, Sneha Mohanty, Christian Schindelhauer, Stefan Johann Rupitsch, Hing Cheung So

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

14 Citations (Scopus)

Abstract

For handling unreliable datasets contaminated by the non-line-of-sight (NLOS) bias errors, this correspondence statistically robustifies the traditional least squares type hybrid received signal strength and time-of-arrival location estimator using the ℓ1 and Huber loss functions. The two robust formulations are then tackled via different convex relaxation approaches. Numerical results are presented to make fair comparisons with the commonly adopted balancing parameter based scheme, demonstrating the positioning accuracy superiority of the proposed methods in various NLOS environments. © 2023 IEEE.
Original languageEnglish
Pages (from-to)11068-11073
JournalIEEE Transactions on Vehicular Technology
Volume72
Issue number8
Online published20 Mar 2023
DOIs
Publication statusPublished - Aug 2023

Research Keywords

  • ℓ1 loss
  • Computational modeling
  • convex relaxation
  • Huber loss
  • Linear programming
  • Location awareness
  • Minimization
  • Position measurement
  • Programming
  • Received signal strength
  • robust localization
  • Sensors
  • time-of-arrival

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