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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.


Cell layout showing the relationship between the true ranges and inter-BS distances.
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f3-sensors-12-02798: Cell layout showing the relationship between the true ranges and inter-BS distances.

Mentions: We performed computer simulations to examine the performance of the proposed location algorithm. The coordinates of the BSs are respectively set to BS1: (0, 0), BS2: (1,732 m, 0), and BS3: (866 m, 1,500 m) [13]. The MS location is chosen randomly in accordance with a uniform distribution within the region formed by the points BS1, I, J, and K as shown in Figure 3. Before we apply the neural network to estimate MS location, we must set the parameter first, such as the numbers of hidden neurons, and training iterations (epochs). To avoid constructing worse network models, the parameter setting for network architectures must be determined carefully; otherwise it would cause more computational cost and produce worse results. To determine the optimal configuration of the neural network, trial-and-error methods are used to determine the parameter settings for network architectures. We attempted to keep finding the optimal parameter and maintaining gook performance both at the time. Regarding the NLOS effects in the simulations, three error models for NLOS propagation are adopted in this paper, namely, the uniformly distributed noise model [13], circular disk of scatterers model (CDSM) [13,33] and biased uniform random variable model [30].


Artificial neural network for location estimation in wireless communication systems.

Chen CS - Sensors (Basel) (2012)

Cell layout showing the relationship between the true ranges and inter-BS distances.
© Copyright Policy
Related In: Results  -  Collection

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

f3-sensors-12-02798: Cell layout showing the relationship between the true ranges and inter-BS distances.
Mentions: We performed computer simulations to examine the performance of the proposed location algorithm. The coordinates of the BSs are respectively set to BS1: (0, 0), BS2: (1,732 m, 0), and BS3: (866 m, 1,500 m) [13]. The MS location is chosen randomly in accordance with a uniform distribution within the region formed by the points BS1, I, J, and K as shown in Figure 3. Before we apply the neural network to estimate MS location, we must set the parameter first, such as the numbers of hidden neurons, and training iterations (epochs). To avoid constructing worse network models, the parameter setting for network architectures must be determined carefully; otherwise it would cause more computational cost and produce worse results. To determine the optimal configuration of the neural network, trial-and-error methods are used to determine the parameter settings for network architectures. We attempted to keep finding the optimal parameter and maintaining gook performance both at the time. Regarding the NLOS effects in the simulations, three error models for NLOS propagation are adopted in this paper, namely, the uniformly distributed noise model [13], circular disk of scatterers model (CDSM) [13,33] and biased uniform random variable model [30].

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.