Sequential Multidimensional Scaling with Kalman Filtering for Location Tracking
Localization always plays a critical role in wireless sensor networks for a wide range of applications including military, healthcare, and robotics. Although the classical multidimensional scaling (MDS) is a conventionally effective model for positioning, the accuracy of this method is affected by n...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2015-11-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/584912 |
| _version_ | 1827079858536054784 |
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| author | Lan Anh Trinh Nguyen Duc Thang Dang Viet Hung Tran Cong Hung |
| author_facet | Lan Anh Trinh Nguyen Duc Thang Dang Viet Hung Tran Cong Hung |
| author_sort | Lan Anh Trinh |
| collection | DOAJ |
| description | Localization always plays a critical role in wireless sensor networks for a wide range of applications including military, healthcare, and robotics. Although the classical multidimensional scaling (MDS) is a conventionally effective model for positioning, the accuracy of this method is affected by noises from the environment. In this paper, we propose a solution to attenuate noise effects to MDS by combining MDS with a Kalman filter. A model is built to predict the noise distribution with regard to additive noises to the distance measurements following the Gaussian distribution. From that, a linear tracking system is developed. The characteristics of the algorithm are examined through simulated experiments and the results reveal the advantages of our method over conventional works in dealing with the above challenges. Besides, the method is simplified with a linear filter; therefore it suits small and embedded sensors equipped with limited power, memory, and computational capacities well. |
| first_indexed | 2024-03-12T06:49:00Z |
| format | Article |
| id | doaj.art-3225907a7d1749e2b7ae33e648c0294a |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2025-03-20T02:52:15Z |
| publishDate | 2015-11-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-3225907a7d1749e2b7ae33e648c0294a2024-10-03T07:26:28ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772015-11-011110.1155/2015/584912584912Sequential Multidimensional Scaling with Kalman Filtering for Location TrackingLan Anh Trinh0Nguyen Duc Thang1Dang Viet Hung2Tran Cong Hung3 Electronics Engineering Department, Posts and Telecommunication Institute of Technology, Ho Chi Minh 710225, Vietnam Biomedical Engineering Department, International University-Vietnam National University, Ho Chi Minh 720351, Vietnam Institute of Research and Development, Duy Tan University, Da Nang 555123, Vietnam Science Technology Department, Posts and Telecommunication Institute of Technology, Ho Chi Minh 710225, VietnamLocalization always plays a critical role in wireless sensor networks for a wide range of applications including military, healthcare, and robotics. Although the classical multidimensional scaling (MDS) is a conventionally effective model for positioning, the accuracy of this method is affected by noises from the environment. In this paper, we propose a solution to attenuate noise effects to MDS by combining MDS with a Kalman filter. A model is built to predict the noise distribution with regard to additive noises to the distance measurements following the Gaussian distribution. From that, a linear tracking system is developed. The characteristics of the algorithm are examined through simulated experiments and the results reveal the advantages of our method over conventional works in dealing with the above challenges. Besides, the method is simplified with a linear filter; therefore it suits small and embedded sensors equipped with limited power, memory, and computational capacities well.https://doi.org/10.1155/2015/584912 |
| spellingShingle | Lan Anh Trinh Nguyen Duc Thang Dang Viet Hung Tran Cong Hung Sequential Multidimensional Scaling with Kalman Filtering for Location Tracking International Journal of Distributed Sensor Networks |
| title | Sequential Multidimensional Scaling with Kalman Filtering for Location Tracking |
| title_full | Sequential Multidimensional Scaling with Kalman Filtering for Location Tracking |
| title_fullStr | Sequential Multidimensional Scaling with Kalman Filtering for Location Tracking |
| title_full_unstemmed | Sequential Multidimensional Scaling with Kalman Filtering for Location Tracking |
| title_short | Sequential Multidimensional Scaling with Kalman Filtering for Location Tracking |
| title_sort | sequential multidimensional scaling with kalman filtering for location tracking |
| url | https://doi.org/10.1155/2015/584912 |
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