A novel time difference of arrival localization algorithm using a neural network ensemble model
In a localization system, time difference of arrival technique is widely used to estimate the location of a mobile station. To improve the performance of mobile station location estimation, a novel algorithm-based artificial neural network ensemble and time difference of arrival information is propo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2018-11-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147718815798 |
| _version_ | 1797725271508385792 |
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| author | Zhenkai Zhang Feng Jiang Boyuan Li Bing Zhang |
| author_facet | Zhenkai Zhang Feng Jiang Boyuan Li Bing Zhang |
| author_sort | Zhenkai Zhang |
| collection | DOAJ |
| description | In a localization system, time difference of arrival technique is widely used to estimate the location of a mobile station. To improve the performance of mobile station location estimation, a novel algorithm-based artificial neural network ensemble and time difference of arrival information is proposed in non-line-of-sight environments. Back propagation neural network is a classic artificial neural network and may be effectively used for mathematical modeling and prediction, and an artificial neural network ensemble has better generalization ability and stability than a single network. First, the parameters, such as the weights and biases of the single neural network are optimized by the ant lion optimization method which is novel and effective. Then four types of different information from the time difference of arrival measurements are respectively used to train the individual neural network. Finally, the weighted average method is improved to combine the outputs of the different individual neural network, where weights are determined by the training errors. The estimation accuracy of the locating system is evaluated through experimental measurements. The simulation results show that the proposed algorithm is efficient in improving the generalization ability and localization precision of the neural network ensemble model. |
| first_indexed | 2024-03-12T10:28:46Z |
| format | Article |
| id | doaj.art-7427f2ec1df44fc1b54e27c519d3c647 |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T10:28:46Z |
| publishDate | 2018-11-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-7427f2ec1df44fc1b54e27c519d3c6472023-09-02T09:25:00ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772018-11-011410.1177/1550147718815798A novel time difference of arrival localization algorithm using a neural network ensemble modelZhenkai Zhang0Feng Jiang1Boyuan Li2Bing Zhang3Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB, CanadaSchool of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang, ChinaDepartment of Electrical and Computer Engineering, University of Calgary, Calgary, AB, CanadaSchool of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang, ChinaIn a localization system, time difference of arrival technique is widely used to estimate the location of a mobile station. To improve the performance of mobile station location estimation, a novel algorithm-based artificial neural network ensemble and time difference of arrival information is proposed in non-line-of-sight environments. Back propagation neural network is a classic artificial neural network and may be effectively used for mathematical modeling and prediction, and an artificial neural network ensemble has better generalization ability and stability than a single network. First, the parameters, such as the weights and biases of the single neural network are optimized by the ant lion optimization method which is novel and effective. Then four types of different information from the time difference of arrival measurements are respectively used to train the individual neural network. Finally, the weighted average method is improved to combine the outputs of the different individual neural network, where weights are determined by the training errors. The estimation accuracy of the locating system is evaluated through experimental measurements. The simulation results show that the proposed algorithm is efficient in improving the generalization ability and localization precision of the neural network ensemble model.https://doi.org/10.1177/1550147718815798 |
| spellingShingle | Zhenkai Zhang Feng Jiang Boyuan Li Bing Zhang A novel time difference of arrival localization algorithm using a neural network ensemble model International Journal of Distributed Sensor Networks |
| title | A novel time difference of arrival localization algorithm using a neural network ensemble model |
| title_full | A novel time difference of arrival localization algorithm using a neural network ensemble model |
| title_fullStr | A novel time difference of arrival localization algorithm using a neural network ensemble model |
| title_full_unstemmed | A novel time difference of arrival localization algorithm using a neural network ensemble model |
| title_short | A novel time difference of arrival localization algorithm using a neural network ensemble model |
| title_sort | novel time difference of arrival localization algorithm using a neural network ensemble model |
| url | https://doi.org/10.1177/1550147718815798 |
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