RSSI 데이터 기반 머신러닝 실내 측위 연구

Abstract

With the recent development of science and technology, localization technology has made it possible to provide customized mobile services based on real-time location information to users through smartphones and Internet of Things devices. In particular, GPS-based real-time localization is commonly used. However, unlike outdoors, not only does the GPS reception rate decrease significantly indoors, but it is also difficult to accurately measure the user's indoor location using only GPS latitude and longitude information. In this study, we propose to measure the user's indoor location using RSSI (received signal strength indicator). For this purpose, an indoor localization study was conducted by applying machine learning classifiers, SVM, decision tree, ExtraTrees, random forest, and KNN to RSSI data. Among the machine learning classifiers, random forest exhibited the best performance. Therefore, we applied random forest-based RFE to extract features from RSSI data. We confirmed that even with a smaller amount of data, it was possible to achieve more accurate indoor positioning. In addition, it was confirmed through regression analysis that latitude and longitude can be estimated from RSSI data, so that indoor as well as outdoor locations can be estimated.