Maximum correntropy criterion for robust TOA-based localization in NLOS environments

TL;DR

This paper proposes a robust TOA-based localization method using maximum correntropy criterion to effectively handle NLOS conditions, outperforming existing approaches in accuracy with minimal prior information.

Contribution

It introduces a novel robust localization formulation based on maximum correntropy, solved via an efficient alternating maximization approach, requiring only basic measurements and prior info.

Findings

Outperforms state-of-the-art methods in accuracy.

Effective in scenarios with moderate NLOS path percentages.

Requires only TOA measurements and sensor positions.

Abstract

We investigate the problem of time-of-arrival (TOA) based localization under possible non-line-of-sight (NLOS) propagation conditions. To robustify the squared-range-based location estimator, we follow the maximum correntropy criterion, essentially the Welsch $M$-estimator with a redescending influence function which behaves like $\ell_0$-minimization towards the grossly biased measurements, to derive the formulation. The half-quadratic technique is then applied to settle the resulting optimization problem in an alternating maximization (AM) manner. By construction, the major computational challenge at each AM iteration boils down to handling an easily solvable generalized trust region subproblem. It is worth noting that the implementation of our localization method requires nothing but merely the TOA-based range measurements and sensor positions as prior information. Simulation and experimental results demonstrate the competence of the presented scheme in outperforming several state-of-the-art approaches in terms of positioning accuracy, especially in scenarios where the percentage of NLOS paths is not large enough.