Resilient back-propagation Neural Network for approximation Weighted Geometric Dilution of Precision

Chien Sheng Chen, Szu-Lin Su, He Nian Shou, Wen Hsiung Liu

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

1 Citation (Scopus)

Abstract

The conventional matrix inversion method for weighted geometric dilution of precision (WGDOP) calculation has a large amount of operation. This paper employs an artificial neural network approach, namely, the resilient back-propagation (Rprop) method to implement WGDOP. We also present two novel architectures to implement the Rprop-based WGDOP. Simulation results show that the proposed architectures always yield superior estimation accuracy with much reduced computational complexity, compared to conventional implementation methods for WGDOP. The proposed architectures are applicable not only to global positioning system (GPS) but also to wireless sensor networks (WSN) and cellular communication systems regardless of the number of the measurement units. To further reduce the complexity, we propose to combine the serving base station (BS) and the optimal three measurements with minimum WGDOP to reduce the number of subsets in cellular communication systems.

Original languageEnglish
Title of host publicationProceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010
Pages53-58
Number of pages6
DOIs
Publication statusPublished - 2010 Nov 1
Event2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010 - Chengdu, China
Duration: 2010 Jul 92010 Jul 11

Publication series

NameProceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010
Volume7

Other

Other2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010
CountryChina
CityChengdu
Period10-07-0910-07-11

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering

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  • Cite this

    Chen, C. S., Su, S-L., Shou, H. N., & Liu, W. H. (2010). Resilient back-propagation Neural Network for approximation Weighted Geometric Dilution of Precision. In Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010 (pp. 53-58). [5563546] (Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010; Vol. 7). https://doi.org/10.1109/ICCSIT.2010.5563546