Beacon

Dead reckoning

Algorithm

Physics

Inertial navigation system

Artificial intelligence

Kalman filter

Inertial frame of reference

Computer vision

Computer science

Global Positioning System

Quantum mechanics

Telecommunications

Real-time computing

IEEE Internet of Things Journal

Indoor wireless localization using Bluetooth low energy (BLE) beacons has attracted considerable attention due to its extensive distribution and low cost properties. This article proposes a novel 3-D indoor localization algorithm which uses the combination of BLE and multiple sensors (3D-LBMS). The inertial navigation system (INS) and pedestrian dead reckoning (PDR) mechanizations are combined for accurate heading and speed estimation, which contains a multilevel constraints-based quasistatic magnetic field (QSMF) detection algorithm. In addition, dynamic-time-warping (DTW)-based BLE landmark detection algorithm is proposed to provide absolute 3-D location reference to multiple sensors-based positioning method, and the detected BLE landmark points are also used to calibrate the parameter of step-length calculation. Finally, the adaptive unscented Kalman filter (AUKF) is applied to fuse the results of INS/PDR mechanizations, QSMF and locations of detected BLE landmarks to achieve accurate and concrete multisource-based 3-D indoor localization performance. The experimental results show that the proposed 3-D-LBMS is proved to achieve meterlevel 2-D positioning accuracy and submeter level 3-D altitude estimation accuracy in typical indoor environments.

A Novel 3-D Indoor Localization Algorithm Based on BLE and Multiple Sensors