A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization

Sensors

Pablo Hernández-Morera¹  José Ma. Quinteiro¹  David Sánchez-Rodríguez²  Itziar Alonso-González² 

[1] IUMA Information and Communications Systems, Edificio Polivalente I, Parque Científico y Tecnológico, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, Spain;Institute for Technological Development and Innovation in Communications, Edificio Polivalente II, 2aplanta, Parque Científico y Tecnológico, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, Spain;

关键词: WLAN indoor localization;    weighted decision trees;    received signal strength;    orientation;    sensor fusion;   

DOI  :  10.3390/s150614809

来源: DOAJ

【 摘 要 】

Indoor position estimation has become an attractive research topic due to growing interest in location-aware services. Nevertheless, satisfying solutions have not been found with the considerations of both accuracy and system complexity. From the perspective of lightweight mobile devices, they are extremely important characteristics, because both the processor power and energy availability are limited. Hence, an indoor localization system with high computational complexity can cause complete battery drain within a few hours. In our research, we use a data mining technique named boosting to develop a localization system based on multiple weighted decision trees to predict the device location, since it has high accuracy and low computational complexity. The localization system is built using a dataset from sensor fusion, which combines the strength of radio signals from different wireless local area network access points and device orientation information from a digital compass built-in mobile device, so that extra sensors are unnecessary. Experimental results indicate that the proposed system leads to substantial improvements on computational complexity over the widely-used traditional fingerprinting methods, and it has a better accuracy than they have.

【 授权许可】

Unknown