Neurodynamic TDOA localization with NLOS mitigation via maximum correntropy criterion

TL;DR

This paper introduces a neurodynamic approach using maximum correntropy criterion to improve TDOA localization accuracy under NLOS conditions, outperforming existing methods through robust joint estimation.

Contribution

It presents a novel neurodynamic optimization framework applying MCC for robust TDOA localization in NLOS environments, with stability analysis and superior simulation results.

Findings

Outperforms existing TDOA localization schemes in NLOS scenarios

Demonstrates robustness of the neurodynamic MCC approach

Provides stability analysis of the neural network model

Abstract

In this paper, we exploit the maximum correntropy criterion (MCC) to robustify the traditional time-difference-of-arrival (TDOA) location estimator in the presence of non-line-of-sight (NLOS) propagation conditions. For the sake of statistical efficiency, the correntropy-based robust loss is imposed on the underlying time-of-arrival composition via joint estimation of the source position and onset time, instead of the TDOA counterpart generated in the postprocessing of sensor-collected timestamps. We then employ a neurodynamic optimization approach to tackle the highly nonconvex MCC formulation. Furthermore, we examine the local stability of equilibrium for the corresponding projection-type neural network model. Simulation investigations in representative NLOS propagation scenarios demonstrate that our neurodynamic robust TDOA localization solution is capable of outperforming several existing schemes in terms of positioning accuracy.