A Hybrid Global Minimization Scheme for Accurate Source Localization in Sensor Networks

TL;DR

This paper introduces a hybrid global optimization approach for accurate multi-source localization in sensor networks, effectively handling unknown attenuation models and multi-path effects.

Contribution

It proposes a novel hybrid algorithm combining differential evolution and Levenberg-Marquardt for robust localization with unknown environmental attenuation.

Findings

Enhanced localization accuracy in multi-path environments

Effective handling of unknown attenuation models

Robust performance under sensor synchronization issues

Abstract

We consider the localization problem of multiple wideband sources in a multi-path environment by coherently taking into account the attenuation characteristics and the time delays in the reception of the signal. Our proposed method leaves the space for unavailability of an accurate signal attenuation model in the environment by considering the model as an unknown function with reasonable prior assumptions about its functional space. Such approach is capable of enhancing the localization performance compared to only utilizing the signal attenuation information or the time delays. In this paper, the localization problem is modeled as a cost function in terms of the source locations, attenuation model parameters and the multi-path parameters. To globally perform the minimization, we propose a hybrid algorithm combining the differential evolution algorithm with the Levenberg-Marquardt algorithm. Besides the proposed combination of optimization schemes, supporting the technical details such as closed forms of cost function sensitivity matrices are provided. Finally, the validity of the proposed method is examined in several localization scenarios, taking into account the noise in the environment, the multi-path phenomenon and considering the sensors not being synchronized.