A Novel Cluster Matching Algorithm Based on Support Vector Machine Indoor Localization System

To build a real-environment indoor localization system, we use the received signal strength (RSS) of Wi-Fi being the features and the fingerprinting method to construct the system. There are two stages of fingerprinting method: offline stage and online stage. The offline stage is so-called training stage which means that we construct the database from all reference points (RPs) and extract the features of RSS to train the model. The online stage is so-called testing stage which means that we collect the new data and use the trained model to predict the location. Due to the high computation complexity of the location predicting, we use the clustering algorithms to divide all the RPs into different groups. When we collect the new data and try to estimate the location, we can use cluster matching algorithm to decide which cluster it belongs to first. And then we can use the members of the cluster to do localization by the kernel-based weighting sum method. Because of the time-variant and uncertainty property of received signal strength, the traditional cluster matching algorithm using machine learning model to fit the training data and doing prediction is useless in the real environment. I propose a novel cluster matching algorithm called Margin with Centroids Cluster Matching (MCCM) to build a real-environment indoor localization system. The idea of MCCM is using the similarity which is obtained by kernel SVM margins between the cluster centroids and the test data and choosing the most similar centroid to be the cluster which the test data belongs to. Experiment results demonstrate that the proposed indoor localization system achieves 2.60 meters as mean error in the real environment. As compared to the kernel SVM, the proposed method reduces the mean localization error by 80.711%.