An Innovative Indoor Location Algorithm Based on Supervised Learning and WIFI Fingerprint Classification

By studying the characteristics of WIFI fingerprint signals and combining supervised learning methods in machine learning, an innovative indoor location algorithm based on Naive Bayes and WIFI fingerprinting is presented. In the experiment, the router is selected as the generator of WIFI signal, and the RSSI fingerprint of the signal is collected to form the fingerprint library. The Naive Bayes models are used to train the data, and the server is used to calculate the position in order to realize the fast positioning of the intelligent terminal. Experiment is designed with an indoor environment including 6 positioning points, scanning interval is set to 5 s, and the learning time is set to 10 min. The experiment result shows that the system and algorithm perform well and the accuracy of positioning is higher than 80%.