Robust TDOA Source Localization Based on Lagrange Programming Neural Network

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

3 Scopus Citations
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Author(s)

  • Wenxin Xiong
  • Christian Schindelhauer
  • Hing Cheung So
  • Dominik Jan Schott
  • Stefan Johann Rupitsch

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)1090-1094
Journal / PublicationIEEE Signal Processing Letters
Volume28
Online published19 May 2021
Publication statusPublished - 2021

Abstract

We revisit herein the problem of time-difference-of-arrival (TDOA) based localization under the mixed line-of-sight/non-line-of-sight propagation conditions. Adopting the strategy of statistically robustifying the non-outlier-resistant ℓ2 loss, we formulate it as the minimization of a possibly non-differentiable generalized robust cost function, which is rooted in the analog locally competitive algorithm (LCA) for sparse approximation. We then present a Lagrange programming neural network to address the optimization formulation, with the non-differentiability issues being handled by grafting thereon the LCA concept of internal state dynamics. Compared with the existing algorithms, our approach is computationally less expensive, less reliant on the use of a priori error information, and observed to be capable of producing higher localization accuracy.

Research Area(s)

  • Complexity theory, Lagrange programming neural network, locally competitive algorithm, Location awareness, Neural networks, Neurodynamics, Neurons, Sensors, Signal processing algorithms, Time-difference-of-arrival

Citation Format(s)

Robust TDOA Source Localization Based on Lagrange Programming Neural Network. / Xiong, Wenxin; Schindelhauer, Christian; So, Hing Cheung; Schott, Dominik Jan; Rupitsch, Stefan Johann.

In: IEEE Signal Processing Letters, Vol. 28, 2021, p. 1090-1094.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review