Trusted K Nearest Bayesian Estimation for Indoor Positioning System

Abstract

Indoor positioning systems have received increasing attention because of their wide range of indoor applications. However, the positioning system generally suffers from a large error in localization and has low solidity. The main approaches widely used for indoor localization are based on the inertial measurement unit (IMU), Bluetooth, Wi-Fi, and ultra-wideband. The major problem with Bluetooth-based fingerprinting is the inconsistency of the radio signal strength, and the IMU-based localization has a drift error that increases with time. To compensate for these drawbacks, in the present study, a novel positioning system with IMU sensors and Bluetooth low energy (BLE) beacon for a smartphone are introduced. The proposed trusted K nearest Bayesian estimation (TKBE) integrates BLE beacon and pedestrian dead reckoning positionings. The BLE-based positioning, using both the K-nearest neighbor (KNN) and Bayesian estimation, increases the accuracy by 25% compared with the existing KNN-based positioning, and the proposed fuzzy logic-based Kalman filter increases the accuracy by an additional 15%. The overall performance of TKBE has an error of <;1 m in our experimental environments.

Keywords

• Bayesian estimation
• Bluetooth low energy (BLE)
• fingerprints
• fuzzy-logic system
• indoor positioning
• K-nearest neighbor (KNN)