# Fast and Fault-Tolerant Passive Hyperbolic Localization Using Sensor Consensus

## TL;DR

This paper introduces a fast and reliable method for accurate location estimation in challenging environments using sensor consensus.

## Contribution

A novel branch-and-bound computational method with proven global convergence for optimizing consensus functions in localization.

## Key findings

- The proposed algorithm achieves global maximum of consensus functions efficiently.
- Simulation and real measurements confirm the method's robustness and accuracy in NLOS conditions.

## Abstract

The accuracy of passive hyperbolic localization applications using Time Difference of Arrival (TDOA) measurements can be severely compromised in non-line-of-sight (NLOS) situations. Consensus functions have been successfully used to provide robust and accurate location estimates in such challenging situations. In this paper, a fast branch-and-bound computational method for finding the global maximum of consensus functions is proposed and the global convergence property of the algorithm is mathematically proven. The performance of the method is illustrated by simulation experiments and real measurements.