Abstract
Recently, there has been growing interest in improving the efficiency and accuracy of the Indoor Positioning System (IPS). The Received Signal Strength- (RSS-) based fingerprinting technique is essential for indoor localization. However, it is challenging to estimate the indoor position based on RSS’s measurement under the complex indoor environment. This paper evaluates three machine learning approaches and Gaussian Process (GP) regression with three different kernels to get the best indoor positioning model. The hyperparameter tuning technique is used to select the optimum parameter set for each model. Experiments are carried out with RSS data from seven access points (AP). Results show that GP with a rational quadratic kernel and eXtreme gradient tree boosting model has the best positioning accuracy compared to other models. In contrast, the eXtreme gradient tree boosting model could achieve higher positioning accuracy with smaller training size and fewer access points.
Highlights
Wireless indoor positioning is attracting considerable critical attention due to the increasing demands on indoor location-based services
Machine learning approaches can avoid the complexity of determining an appropriate propagation model with traditional geometric approaches and adapt well to local variations of indoor environment [6]. us, we use machine learning approaches to construct an empirical model that models the distribution of Received Signal Strength (RSS) in an indoor environment. e model can determine the indoor position based on the RSS information in that position
We evaluate different machine learning approaches for indoor positioning with RSS data. e models include Support Vector Regression (SVR), Random Forest (RF), XGBoost, and Gaussian Process Regression (GPR) with three different kernels
Summary
Wireless indoor positioning is attracting considerable critical attention due to the increasing demands on indoor location-based services. Machine learning approaches can avoid the complexity of determining an appropriate propagation model with traditional geometric approaches and adapt well to local variations of indoor environment [6]. Us, we use machine learning approaches to construct an empirical model that models the distribution of Received Signal Strength (RSS) in an indoor environment. The client’s position is determined by the signal strength and the trained model. The XGBoost model can achieve high positioning accuracy with smaller training size and fewer APs. We design experiment and use results to show the optimal number of access points and the size of RSS data for the optimal model.
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