PMID- 22163844
OWN - NLM
STAT- MEDLINE
DCOM- 20120801
LR  - 20211021
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 11
IP  - 4
DP  - 2011
TI  - Applying Rprop neural network for the prediction of the mobile station location.
PG  - 4207-30
LID - 10.3390/s110404207 [doi]
AB  - Wireless location is the function used to determine the mobile station (MS) 
      location in a wireless cellular communications system. When it is very hard for 
      the surrounding base stations (BSs) to detect a MS or the measurements contain 
      large errors in non-line-of-sight (NLOS) environments, then one need to integrate 
      all available heterogeneous measurements to increase the location accuracy. In 
      this paper we propose a novel algorithm that combines both time of arrival (TOA) 
      and angle of arrival (AOA) measurements to estimate the MS in NLOS environments. 
      The proposed algorithm utilizes the intersections of two circles and two lines, 
      based on the most resilient back-propagation (Rprop) neural network learning 
      technique, to give location estimation of the MS. The traditional Taylor series 
      algorithm (TSA) and the hybrid lines of position algorithm (HLOP) have 
      convergence problems, and even if the measurements are fairly accurate, the 
      performance of these algorithms depends highly on the relative position of the MS 
      and BSs. Different NLOS models were used to evaluate the proposed methods. 
      Numerical results demonstrate that the proposed algorithms can not only preserve 
      the convergence solution, but obtain precise location estimations, even in severe 
      NLOS conditions, particularly when the geometric relationship of the BSs relative 
      to the MS is poor.
FAU - Chen, Chien-Sheng
AU  - Chen CS
AD  - Department of Information Management, Tainan University of Technology, Tainan 
      City, Taiwan. t00243@mail.tut.edu.tw
FAU - Lin, Jium-Ming
AU  - Lin JM
LA  - eng
PT  - Journal Article
DEP - 20110408
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Algorithms
MH  - Humans
MH  - *Neural Networks, Computer
MH  - *Wireless Technology
PMC - PMC3231337
OTO - NOTNLM
OT  - angle of arrival (AOA)
OT  - back-propagation nseural network (BPNN)
OT  - resilient back-propagation (Rprop)
OT  - time of arrival (TOA)
EDAT- 2011/12/14 06:00
MHDA- 2012/08/02 06:00
PMCR- 2011/01/01
CRDT- 2011/12/14 06:00
PHST- 2011/01/05 00:00 [received]
PHST- 2011/03/17 00:00 [revised]
PHST- 2011/04/01 00:00 [accepted]
PHST- 2011/12/14 06:00 [entrez]
PHST- 2011/12/14 06:00 [pubmed]
PHST- 2012/08/02 06:00 [medline]
PHST- 2011/01/01 00:00 [pmc-release]
AID - s110404207 [pii]
AID - sensors-11-04207 [pii]
AID - 10.3390/s110404207 [doi]
PST - ppublish
SO  - Sensors (Basel). 2011;11(4):4207-30. doi: 10.3390/s110404207. Epub 2011 Apr 8.