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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 and a line.
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f2-sensors-12-02798: Geometry layout of the three circles and a line.

Mentions: It is well known that a single AOA measurement constrains the MS along a line. Denote by θ as the angle between MS and its serving BS, with respect to a reference direction (for instance the x-axis). The AOA measurement can be expressed as:(22)Line 1:tanθ⋅x−y=0The intersections of three TOA circles and AOA line can provide the MS location estimation, the result is as shown in Figure 2. These feasible intersections still must satisfy Equations (19–21). To improve the accuracy of the MS location even further, we apply various neural network algorithms to obtain the approximation of the MS location. The feasible intersections are applied to the input layer, and the output layer is MS location estimation.


Artificial neural network for location estimation in wireless communication systems.

Chen CS - Sensors (Basel) (2012)

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

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

f2-sensors-12-02798: Geometry layout of the three circles and a line.
Mentions: It is well known that a single AOA measurement constrains the MS along a line. Denote by θ as the angle between MS and its serving BS, with respect to a reference direction (for instance the x-axis). The AOA measurement can be expressed as:(22)Line 1:tanθ⋅x−y=0The intersections of three TOA circles and AOA line can provide the MS location estimation, the result is as shown in Figure 2. These feasible intersections still must satisfy Equations (19–21). To improve the accuracy of the MS location even further, we apply various neural network algorithms to obtain the approximation of the MS location. The feasible intersections are applied to the input layer, and the output layer is MS location estimation.

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.