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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.


Comparison of location error CDFs when NLOS errors are modeled as CDSM.
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f8-sensors-12-02798: Comparison of location error CDFs when NLOS errors are modeled as CDSM.

Mentions: Under highly NLOS conditions, the average location errors of TSA and LLOP are at least two times larger than the proposed algorithm. The proposed algorithm is less sensitive to the increasing in NLOS magnitude compared to the TSA, LLOP and RSA. The proposed algorithm can provide a more accurate MS location estimation and reduce the errors caused by the effect of NLOS propagation. As shown in Figure 8, the improvement in location accuracy using the proposed algorithm can also be seen in the cumulative distribution functions (CDF) curves of the location errors. The radius of the scatterers is set to be 200 m. Compared with the other traditional methods, the accuracy of MS location was indeed improved with the proposed algorithm. It is clear that TSA and LLOP predict the MS location with poor accuracy and the proposed algorithm always achieves the best performance.


Artificial neural network for location estimation in wireless communication systems.

Chen CS - Sensors (Basel) (2012)

Comparison of location error CDFs when NLOS errors are modeled as CDSM.
© Copyright Policy
Related In: Results  -  Collection

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

f8-sensors-12-02798: Comparison of location error CDFs when NLOS errors are modeled as CDSM.
Mentions: Under highly NLOS conditions, the average location errors of TSA and LLOP are at least two times larger than the proposed algorithm. The proposed algorithm is less sensitive to the increasing in NLOS magnitude compared to the TSA, LLOP and RSA. The proposed algorithm can provide a more accurate MS location estimation and reduce the errors caused by the effect of NLOS propagation. As shown in Figure 8, the improvement in location accuracy using the proposed algorithm can also be seen in the cumulative distribution functions (CDF) curves of the location errors. The radius of the scatterers is set to be 200 m. Compared with the other traditional methods, the accuracy of MS location was indeed improved with the proposed algorithm. It is clear that TSA and LLOP predict the MS location with poor accuracy and the proposed algorithm always achieves the best performance.

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.