학술논문
Indoor positioning in complex environments using modified probabilistic neural network
Document Type
Conference
Author
Source
2013 International Symposium on Next-Generation Electronics Next-Generation Electronics (ISNE), 2013 IEEE International Symposium on. :248-251 Feb, 2013
Subject
Language
Abstract
This paper presents a modified probabilistic neural network (MPNN) based indoor positioning technique, which can be used in complex environment. Firstly, the received signal strengths (RSS) are measured between an object and stations. An average filter is applied to remove noise of RSS set. The extracted RSS features are transformed into reliable distances. Then, A MPNN engine determines coordinate of the object with the input distances. The experiments perform significantly better than triangulation technique when the RSS data are unstable in complex environments.