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Artificial neural network for location estimation in wireless communication systems.

Chen CS - Sensors (Basel) (2012)

Bottom Line: In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS).Artificial neural networks (ANN) are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships.The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments.

View Article: PubMed Central - PubMed

Affiliation: Department of Information Management, Tainan University of Technology, Yongkang District, Tainan, Taiwan. t00243@mail.tut.edu.tw

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.

No MeSH data available.


RMS errors reduction versus the number of epochs.
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f9-sensors-12-02798: RMS errors reduction versus the number of epochs.

Mentions: Overtraining the neural network can seriously deteriorate the forecasting results. A series of experiments were performed to determine the appropriate number of epochs. Figure 9 shows how the converged RMS error varies as the number of epochs increases. For both training and test data, the trained model of the proposed algorithm always yields superior performance without creating overtraining. The RMS errors will only slightly decreased for the epoch numbers larger than 1,000. The RMS error with various numbers of hidden-layer neurons are compared in Figure 10.


Artificial neural network for location estimation in wireless communication systems.

Chen CS - Sensors (Basel) (2012)

RMS errors reduction versus the number of epochs.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376586&req=5

f9-sensors-12-02798: RMS errors reduction versus the number of epochs.
Mentions: Overtraining the neural network can seriously deteriorate the forecasting results. A series of experiments were performed to determine the appropriate number of epochs. Figure 9 shows how the converged RMS error varies as the number of epochs increases. For both training and test data, the trained model of the proposed algorithm always yields superior performance without creating overtraining. The RMS errors will only slightly decreased for the epoch numbers larger than 1,000. The RMS error with various numbers of hidden-layer neurons are compared in Figure 10.

Bottom Line: In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS).Artificial neural networks (ANN) are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships.The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments.

View Article: PubMed Central - PubMed

Affiliation: Department of Information Management, Tainan University of Technology, Yongkang District, Tainan, Taiwan. t00243@mail.tut.edu.tw

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.

No MeSH data available.