Regression-based Path Loss Model Correction to Construct Fingerprint Database for Indoor Localization

Abstract

The fingerprint-based indoor localization has been widely used due to its simple hardware setup and high positioning accuracy, especially using Received Signal Strength Indicator (RSSI). However, the fingerprint database has main drawbacks in database construction, requiring a lot of effort and time. This paper presents an approach for reducing the effort of manual fingerprint database construction for indoor localization using path loss model enhancement via simple regression, i.e., Linear and Polynomial Regression for RSSI-based fingerprint technique. We used the public dataset to evaluate our proposal, which was collected in a small room with low interference using three wireless technologies (Wi-Fi, ZigBee, and Bluetooth Low Energy). The K-nearest neighbors (KNN) is applied to locate the target. We compared the results from the original path loss model (O-PLM), the linear regression-path loss model (LR-PLM), and the polynomial regression path loss model (PR-PLM) with the actual RSSI values to validate our approach. The results showed that the Original Path Loss Model database and the Polynomial Regression Path Loss Model database improved the localization accuracy for Wi-Fi devices. The Linear Regression Path Loss Model can perform well in the ZigBee device case.