Resilient back-propagation neural network for approximation 2-D GDOP

Chien Sheng Chen, Szu Lin Su

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Geometric dilution of precision (GDOP) represents the geometric effect on the relationship between measurement error and positioning determination error. If the measurement variances are equal in each other, GDOP could be the most appropriate selection criterion of location measurement units. The object of this paper is to obtain the optimal position estimates from the available measurement. The conventional matrix inversion method for GDOP calculation has a large amount of operation, which would be a burden for real time application. This paper employs an artificial neural network approach, namely, the resilient back-propagation (Rprop) method to implement GDOP. This paper also presents two novel architectures to implement the Rprop-based GDOP for the 2D location estimation. Simulation results show that the proposed architectures always yield superior estimation accuracy with much reduced computational complexity, compared to conventional implementation methods for GDOP. The proposed architectures are applicable to cellular communication systems regardless of the number of the measurement units.

Original languageEnglish
Title of host publicationProceedings of the International MultiConference of Engineers and Computer Scientists 2010, IMECS 2010
Pages900-904
Number of pages5
Publication statusPublished - 2010 Dec 1
EventInternational MultiConference of Engineers and Computer Scientists 2010, IMECS 2010 - Kowloon, Hong Kong
Duration: 2010 Mar 172010 Mar 19

Publication series

NameProceedings of the International MultiConference of Engineers and Computer Scientists 2010, IMECS 2010

Other

OtherInternational MultiConference of Engineers and Computer Scientists 2010, IMECS 2010
CountryHong Kong
CityKowloon
Period10-03-1710-03-19

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)

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