A modified probability neural network indoor positioning technique

Abstract

This paper presents an indoor positioning technique using a modified probabilistic neural network (MPNN) scheme. It measures the received signal strength (RSS) between an object and stations, and then transforms the RSS into distances. A MPNN engine determines coordinate of the object with the input distances. The experiments are conducted in a realistic ZigBee sensor network. The proposed approach performs significantly better than triangulation technique when the RSS data are unstable. It can be efficiently applied to applications of location based service (LBS).