Abstract :
Location based services (LBS), context aware applications, and people and object tracking depend on the ability to locate mobile devices, also known as localization, in the wireless landscape. Localization enables a diverse set of applications that include, but are not limited to, vehicle guidance in an industrial environment, security monitoring, self-guided tours, personalized communications services, resource tracking, mobile commerce services, guiding emergency workers during fire emergencies, habitat monitoring, environmental surveillance, and receiving alerts. This paper presents a new neural network approach (LENSR) based on a competitive topological counter propagation network (CPN) with k-nearest neighborhood vector mapping, for indoor location estimation based on received signal strength. The advantage of this approach is both speed and accuracy. The tested accuracy of the algorithm was 90.6% within 1 meter and 96.4% within 1.5 meters. Several approaches for location estimation using WLAN technology were reviewed for comparison of results.
Keywords :
mobile communication; neural nets; radio direction-finding; target tracking; wireless LAN; counter propagation network; k-nearest neighborhood vector mapping; location based services; location estimation; mobile commerce services; mobile devices; neural networks; personalized communications services; resource tracking; security monitoring; vehicle guidance; wireless LAN; wireless based object tracking; Business; Communication industry; Communication system security; Context awareness; Fires; Mobile communication; Monitoring; Navigation; Neural networks; Vehicles; CPN; GPS; RSS; k-nearest neighbor; localization; neural network; signature recognition;
