Improved robust toa-based localization via nlos balancing parameter estimation

Haotian Chen, Gang Wang, Nirwan Ansari

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

In this paper, the time-of-arrival-based localization problem under mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions is addressed. Previous studies show that existing robust methods perform well in dense NLOS environments, but generally perform badly in sparse NLOS environments. To alleviate this problem, we introduce a 'balancing parameter' related to the NLOS errors and formulate a new robust weighted least squares (RWLS) problem with the source position and the NLOS balancing parameter as the estimation variables. The proposed method does not require the statistics of NLOS errors and the path status. By leveraging the S-Lemma, the RWLS problem is transformed into a non-convex optimization problem, which is then relaxed into a convex semidefinite program. Simulation results show that the proposed method works well for both the sparse and dense NLOS environments.

Original languageEnglish (US)
Article number8691482
Pages (from-to)6177-6181
Number of pages5
JournalIEEE Transactions on Vehicular Technology
Volume68
Issue number6
DOIs
StatePublished - Jun 2019

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

Keywords

  • Time-of-arrival
  • line-of-sight/non-line-of-sight (LOS/ NLOS)
  • robust localization
  • semidefinite relaxation

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