Next Article in Journal
Next Article in Special Issue
Previous Article in Journal
Previous Article in Special Issue
Sensors 2012, 12(3), 2798-2817; doi:10.3390/s120302798
Article

Artificial Neural Network for Location Estimation in Wireless Communication Systems

Received: 12 January 2012; in revised form: 22 February 2012 / Accepted: 23 February 2012 / Published: 1 March 2012
(This article belongs to the Special Issue Collaborative Sensors)
View Full-Text   |   Download PDF [477 KB, uploaded 21 June 2014]
Abstract: In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS). To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA) measurements and the angle of arrival (AOA) information to locate MS when three base stations (BSs) are available. Artificial neural networks (ANN) are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line), based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS) environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments.
Keywords: time of arrival (TOA); angle of arrival (AOA); non-line-of-sight (NLOS); artificial neural networks (ANN) time of arrival (TOA); angle of arrival (AOA); non-line-of-sight (NLOS); artificial neural networks (ANN)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Chen, C.-S. Artificial Neural Network for Location Estimation in Wireless Communication Systems. Sensors 2012, 12, 2798-2817.

AMA Style

Chen C-S. Artificial Neural Network for Location Estimation in Wireless Communication Systems. Sensors. 2012; 12(3):2798-2817.

Chicago/Turabian Style

Chen, Chien-Sheng. 2012. "Artificial Neural Network for Location Estimation in Wireless Communication Systems." Sensors 12, no. 3: 2798-2817.


Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert