Majorization-Minimization based Hybrid Localization Method for High Precision Localization in Wireless Sensor Networks

TL;DR

This paper introduces a hybrid localization method for wireless sensor networks that combines multiple radio measurements using a majorization-minimization approach, achieving high accuracy and guaranteed convergence.

Contribution

It presents a unified iterative framework for hybrid localization using TOA, TDOA, RSS, and AOA measurements, with proven convergence and improved accuracy over existing methods.

Findings

Hybrid model outperforms single-measurement approaches in accuracy.

The method is robust to NLOS errors in various network scenarios.

Extensive simulations validate the effectiveness of the proposed algorithm.

Abstract

This paper investigates the hybrid source localization problem using the four radio measurements - time of arrival (TOA), time difference of arrival (TDOA), received signal strength (RSS), and angle of arrival (AOA). First, after invoking tractable approximations in the RSS and AOA models, the maximum likelihood estimation (MLE) problem for the hybrid TOA-TDOA-RSS-AOA data model is derived. Then a weighted least-squares problem is formulated from the MLE, which is solved using the principle of the majorization-minimization (MM), resulting in an iterative algorithm with guaranteed convergence. The key feature of the proposed method is that it provides a unified framework where localization using any possible merger out of these four measurements can be implemented as per the requirement/application. Extensive numerical simulations are conducted to study the performance of the proposed method. The obtained results indicate that the hybrid localization model improves the localization accuracy compared to the heterogeneous measurements under different network scenarios, which also includes the presence of non-line of sight (NLOS) errors.