Numerous data-driven Bluetooth-based location methods have demonstrated favorable location outcomes in recent years. However, the robustness of the location model has not been thoroughly investigated. This study addresses this gap by conducting simulation tests that involve altering environment and presents a crowdsourcing-based model updating approach. We first introduced a Convolutional Neural Network (CNN) as a location basic model and constructed a spatial covariance matrix image as the network's input. We formulate the localization task as a regression problem and train the CNN to predict beacon position in multipath indoor environments. Based on this model, we introduced a Reconstruction-based Regression Network (RBRN) that updates the feature extractor parameters in the CNN localization basic model by extracting the distributional features of crowdsourced data to adapt to the dynamic fluctuations of the environment. Experimental results demonstrate a notable improvement in the positioning performance of the CNN location basic network compared to state-of-the-art Bluetooth positioning methods. Additionally, in the face of dynamic environmental changes, the crowdsourcing-based RBRN model updating method reduces the average positioning error by 15% ~ 18%, confirming the effectiveness of the proposed model updating method.
Crowdsourcing-based high-precision Bluetooth indoor location method for adapting to environmental dynamics
10.10.2023
2141742 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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