Title :
Towards self-learning radio-based localization systems
Author_Institution :
Inst. of Inf. & Appl. Math., Univ. of Bern, Bern, Switzerland
Abstract :
Location-awareness indoors will be an inseparable feature of mobile services/applications in future wireless networks. Its current ubiquitous availability is still obstructed by technological challenges and privacy issues. We propose an innovative approach towards the concept of indoor positioning with main goal to develop a system that is self-learning and able to adapt to various radio propagation environments. The approach combines estimation of propagation conditions, subsequent appropriate channel modelling and optimisation feedback to the used positioning algorithm. Main advantages of the proposal are decreased system set-up effort, automatic re-calibration and increased precision.
Keywords :
feedback; indoor communication; mobility management (mobile radio); optimisation; radio networks; radiowave propagation; telecommunication security; ubiquitous computing; channel modelling; location-awareness; mobile services; optimisation feedback; privacy issues; radio propagation; self-learning radio-based localization systems; ubiquitous availability; wireless networks; Accuracy; Adaptation models; Adaptive systems; Artificial neural networks; Databases; Radar tracking; Shadow mapping; indoor applications; localization techniques; propagation models;
Conference_Titel :
Pervasive Computing and Communications Workshops (PERCOM Workshops), 2012 IEEE International Conference on
Conference_Location :
Lugano
Print_ISBN :
978-1-4673-0905-9
Electronic_ISBN :
978-1-4673-0906-6
DOI :
10.1109/PerComW.2012.6197572
