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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.


Geometry layout of the three circles.
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f1-sensors-12-02798: Geometry layout of the three circles.

Mentions: Taking into account the constraint on hearability, the number of BSs is three. As shown in Figure 1, the coordinates for BS1, BS2, BS3 are given by (X1, Y1) = (0, 0), (X2, Y2) = (X2, 0), and (X3, Y3), respectively. The distances between BSi and the MS can be expressed as:(1)ri=c⋅ti=(x−Xi)2+(y−Yi)2where c is the signal propagation speed, (x, y) and (Xi, Yi) are the location of the MS and BSi, respectively. If (xv, yv) is the initial estimated position, let x = xv + δx, y = yv + δy. By linearizing the TOA equations using Taylor series expansion and retaining the first two terms, we have:(2)Aδ≅zwhere , ,, , , .


Artificial neural network for location estimation in wireless communication systems.

Chen CS - Sensors (Basel) (2012)

Geometry layout of the three circles.
© Copyright Policy
Related In: Results  -  Collection

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

f1-sensors-12-02798: Geometry layout of the three circles.
Mentions: Taking into account the constraint on hearability, the number of BSs is three. As shown in Figure 1, the coordinates for BS1, BS2, BS3 are given by (X1, Y1) = (0, 0), (X2, Y2) = (X2, 0), and (X3, Y3), respectively. The distances between BSi and the MS can be expressed as:(1)ri=c⋅ti=(x−Xi)2+(y−Yi)2where c is the signal propagation speed, (x, y) and (Xi, Yi) are the location of the MS and BSi, respectively. If (xv, yv) is the initial estimated position, let x = xv + δx, y = yv + δy. By linearizing the TOA equations using Taylor series expansion and retaining the first two terms, we have:(2)Aδ≅zwhere , ,, , , .

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.