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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.


The CDF of location error of various methods for the biased uniform random variable model.
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f11-sensors-12-02798: The CDF of location error of various methods for the biased uniform random variable model.

Mentions: The former NLOS propagation model is called the uniformly distributed noise model [13], in which the TOA measurement error is assumed to be uniformly distributed over (0,Ui), for i = 1,2,3, where Ui is the upper bound of the error. Among various training methods for neural network, single hidden layer is the most widely used. It is well enough to model arbitrarily complex nonlinear functions. Positioning accuracy is measured in terms of root-mean-square (RMS) error between the actual MS location and the desired MS location. The important factors influencing the performance of the neural network are the number of training iterations (epochs) and the number of neurons in the hidden layer. In Figures 4 to 11, each abbreviation used is as follows: SCG: Scaled Conjugate Gradient, CGF: Conjugate Gradient with Fletcher-Reeves Updates, CGP: Conjugate Gradient with Polak-Ribiere Updates, Rprop: Resilient back-propagation, LM: Levenburg-Marquardt.


Artificial neural network for location estimation in wireless communication systems.

Chen CS - Sensors (Basel) (2012)

The CDF of location error of various methods for the biased uniform random variable model.
© Copyright Policy
Related In: Results  -  Collection

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

f11-sensors-12-02798: The CDF of location error of various methods for the biased uniform random variable model.
Mentions: The former NLOS propagation model is called the uniformly distributed noise model [13], in which the TOA measurement error is assumed to be uniformly distributed over (0,Ui), for i = 1,2,3, where Ui is the upper bound of the error. Among various training methods for neural network, single hidden layer is the most widely used. It is well enough to model arbitrarily complex nonlinear functions. Positioning accuracy is measured in terms of root-mean-square (RMS) error between the actual MS location and the desired MS location. The important factors influencing the performance of the neural network are the number of training iterations (epochs) and the number of neurons in the hidden layer. In Figures 4 to 11, each abbreviation used is as follows: SCG: Scaled Conjugate Gradient, CGF: Conjugate Gradient with Fletcher-Reeves Updates, CGP: Conjugate Gradient with Polak-Ribiere Updates, Rprop: Resilient back-propagation, LM: Levenburg-Marquardt.

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.