BLE-Based AoA Estimation Using a Sliding Window Median Filter to Remove Outliers

Erick Adrian Iglesias Rodriguez, Marcos Eduardo Pivaro Monteiro, Glauber Brante, Guilherme Luiz Moritz, Richard Demo Souza

DOI: 10.14209/sbrt.2025.1571144705

Evento: XLIII Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2025)

Keywords:

Abstract

We propose a method to enhance Bluetooth Low Energy (BLE) angle of arrival (AoA) estimation using the multiple signal classification (MUSIC) algorithm as a base estimator. In order to identify and remove outliers that jeopardize the accuracy performance, the proposed algorithm employs a sliding window median filter, which is compared to an adjustable threshold. Experiments with a 4x4 uniform rectangular array receiving signals from three BLE tags show that the proposed method reduces the root mean squared error by 54.9% compared to traditional MUSIC. Furthermore, the probability of achieving measurement errors below 5 degrees increases from 77% to 83% with the proposed scheme, improving the AoA accuracy.

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