Combination linear lines of position and neural network for mobile station location estimation
To enhance the effectiveness and accuracy of mobile station location estimation, author utilizes time of arrival measurements from three base stations and one angle of arrival information at the serving base station to locate mobile station in non-line-of-sight environments. This article makes use o...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-07-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147717717387 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832556847202041856 |
|---|---|
| author | Chien-Sheng Chen |
| author_facet | Chien-Sheng Chen |
| author_sort | Chien-Sheng Chen |
| collection | DOAJ |
| description | To enhance the effectiveness and accuracy of mobile station location estimation, author utilizes time of arrival measurements from three base stations and one angle of arrival information at the serving base station to locate mobile station in non-line-of-sight environments. This article makes use of linear lines of position, rather than circular lines of position, to give location estimation of the mobile station. It is much easier to solve two linear line equations rather than nonlinear circular ones. Artificial neural networks are widely used techniques in various areas due to overcoming the problem of exclusive and nonlinear relationships. The proposed algorithms employ the intersections of three linear lines of position and one angle of arrival line, based on Levenburg–Marquardt algorithm, to determine the mobile station location without requiring a priori information about the non-line-of-sight error. The simulation results show that the proposed algorithms can always provide much better location estimation than Taylor series algorithm, hybrid lines of position algorithm as well as the geometrical positioning methods for different levels of biased, unbiased, and distance-dependent non-line-of-sight errors. |
| format | Article |
| id | doaj-art-c2943bafe0854b09b41e5f439f0274a4 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2017-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-c2943bafe0854b09b41e5f439f0274a42025-02-03T05:44:19ZengWileyInternational Journal of Distributed Sensor Networks1550-14772017-07-011310.1177/1550147717717387Combination linear lines of position and neural network for mobile station location estimationChien-Sheng ChenTo enhance the effectiveness and accuracy of mobile station location estimation, author utilizes time of arrival measurements from three base stations and one angle of arrival information at the serving base station to locate mobile station in non-line-of-sight environments. This article makes use of linear lines of position, rather than circular lines of position, to give location estimation of the mobile station. It is much easier to solve two linear line equations rather than nonlinear circular ones. Artificial neural networks are widely used techniques in various areas due to overcoming the problem of exclusive and nonlinear relationships. The proposed algorithms employ the intersections of three linear lines of position and one angle of arrival line, based on Levenburg–Marquardt algorithm, to determine the mobile station location without requiring a priori information about the non-line-of-sight error. The simulation results show that the proposed algorithms can always provide much better location estimation than Taylor series algorithm, hybrid lines of position algorithm as well as the geometrical positioning methods for different levels of biased, unbiased, and distance-dependent non-line-of-sight errors.https://doi.org/10.1177/1550147717717387 |
| spellingShingle | Chien-Sheng Chen Combination linear lines of position and neural network for mobile station location estimation International Journal of Distributed Sensor Networks |
| title | Combination linear lines of position and neural network for mobile station location estimation |
| title_full | Combination linear lines of position and neural network for mobile station location estimation |
| title_fullStr | Combination linear lines of position and neural network for mobile station location estimation |
| title_full_unstemmed | Combination linear lines of position and neural network for mobile station location estimation |
| title_short | Combination linear lines of position and neural network for mobile station location estimation |
| title_sort | combination linear lines of position and neural network for mobile station location estimation |
| url | https://doi.org/10.1177/1550147717717387 |
| work_keys_str_mv | AT chienshengchen combinationlinearlinesofpositionandneuralnetworkformobilestationlocationestimation |