Performance Analysis Of Out-of-the-shelf Regressors for Accurate Indoor Positioning

Bismark C Teixeira, Julia B Silva, Diego Aguiar Sousa, Daniel Araújo

DOI: 10.14209/sbrt.2023.1570923594

Evento: XLI Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2023)

Keywords:

Abstract

This paper presents a novel indoor positioning system utilizing machine learning for user localization based on path loss estimates from strategically placed access points. We compare the performance of different out-of-the-shelf regressors and leverage the Extra Trees Regressor algorithm's randomness, avoiding overfitting and outperforming traditional methods like K-Nearest Neighbors. Our system, simulated under the 3GPP 28 GHz indoor channel model, focuses on improving Root Mean Square Error and R-squared metrics. Findings affirm the system's robustness and machine learning's potential in enhancing indoor positioning accuracy.

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