TY - GEN

T1 - Resilient back-propagation neural network for approximation 2-D GDOP

AU - Chen, Chien Sheng

AU - Su, Szu Lin

PY - 2010/12/1

Y1 - 2010/12/1

N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:79952387243

SN - 9789881701282

T3 - Proceedings of the International MultiConference of Engineers and Computer Scientists 2010, IMECS 2010

SP - 900

EP - 904

BT - Proceedings of the International MultiConference of Engineers and Computer Scientists 2010, IMECS 2010

T2 - International MultiConference of Engineers and Computer Scientists 2010, IMECS 2010

Y2 - 17 March 2010 through 19 March 2010

ER -