Low-cost indoor positioning system using BLE (bluetooth low energy) based sensor fusion with constrained extended Kalman Filter

摘要

In this paper, a BLE (Bluetooth Low Energy) based new sensor fusion algorithm is proposed. We can estimate distance by using the signal strength from the BLE beacons. Other sensors support the estimation process which uses the proposed “constrained extended Kalman Filter”. BLE is more efficient than the existing wireless communication, and is easy to install. The proposed algorithm effectively reduces offline tasks and accurately estimates indoor position. It is verified with practical experiments.