Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device

Sensors

Xiang He²  Daniel N. Aloi²  Jia Li¹ 

[1] Department of Electrical and Computer Engineering, Oakland University, 2200 N Squirrel Road, Rochester, MI 48309, USA

关键词: indoor positioning;    HMM framework;    graph structure;    multimodal particle filter;    sensor fusion;    iOS platform;   

DOI  :  10.3390/s151229867

来源: mdpi

【 摘 要 】

Nowadays, smart mobile devices include more and more sensors on board, such as motion sensors (accelerometer, gyroscope, magnetometer), wireless signal strength indicators (WiFi, Bluetooth, Zigbee), and visual sensors (LiDAR, camera). People have developed various indoor positioning techniques based on these sensors. In this paper, the probabilistic fusion of multiple sensors is investigated in a hidden Markov model (HMM) framework for mobile-device user-positioning. We propose a graph structure to store the model constructed by multiple sensors during the offline training phase, and a multimodal particle filter to seamlessly fuse the information during the online tracking phase. Based on our algorithm, we develop an indoor positioning system on the iOS platform. The experiments carried out in a typical indoor environment have shown promising results for our proposed algorithm and system design.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.