Enhancing indoor positioning based on filter partitioning cascade machine learning models

Abstract

This paper proposes a method, called the filter partitioning machine learning classification (FPMLC). It can enhance the accuracy of indoor positioning based on fingerprinting using machine learning algorithms and prominent access points (APs). FPMLC selects limited information from groups of signal strengths and combines a clustering task and a classification task. There are three processes in FPMLC, i.e., feature selection to choose prominent APs, clustering to determine approximated positions, and classification to determine fine positions. This work demonstrates the procedure for FPMLC creation. The results of FPMLC are compared with those of a primitive method using measured data. FPMLC is compared with well-known machine learning classifiers, i.e., Decision Tree, Naive Bayes, and Artificial Neural Networks. The performance comparison is done in terms of accuracy and error distance between classified positions and actual positions. The appropriate number of selected prominent APs and the number of clusters are assigned in the clustering process.  The result of this study shows that FPMLC can increase performance for indoor positioning of all classifiers. Additionally, FPMLC is the most optimized model using a Decision Tree as its classifier.