Robust Least-SquareLocalization Based on Relative Angular Matrix in Wireless Sensor Networks
Accurate position information plays an important role in wireless sensor networks (WSN), and cooperative positioning based on cooperation among agents is a promising methodology of providing such information. Conventional cooperative positioning algorithms, such as least squares (LS), rely on approx...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/11/2627 |
| _version_ | 1811184385268908032 |
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| author | Yuyang Tian Jing Lv Shiwei Tian Jinfei Zhu Wei Lu |
| author_facet | Yuyang Tian Jing Lv Shiwei Tian Jinfei Zhu Wei Lu |
| author_sort | Yuyang Tian |
| collection | DOAJ |
| description | Accurate position information plays an important role in wireless sensor networks (WSN), and cooperative positioning based on cooperation among agents is a promising methodology of providing such information. Conventional cooperative positioning algorithms, such as least squares (LS), rely on approximate position estimates obtained from prior measurements. This paper explores the fundamental mechanism underlying the least squares algorithm’s sensitivity to the initial position selection and approaches to dealing with such sensitivity. This topic plays an essential role in cooperative positioning, as it determines whether a cooperative positioning algorithm can be implemented ubiquitously. In particular, a sufficient and unnecessary condition for the least squares cost function to be convex is found and proven. We then propose a robust algorithm for wireless sensor network positioning that transforms the cost function into a globally convex function by detecting the null space of the relative angle matrix when all the targets are located inside the convex polygon formed by its neighboring nodes. Furthermore, we advance one step further and improve the algorithm to apply it in both the time of arrival (TOA) and angle of arrival/time of arrival (AOA/TOA) scenarios. Finally, the performance of the proposed approach is quantified via simulations, and the results show that the proposed method has a high positioning accuracy and is robust in both line-of-sight (LOS) and non-line-of-sight (NLOS) positioning environments. |
| first_indexed | 2024-04-11T13:12:24Z |
| format | Article |
| id | doaj.art-349f81b460384faf82e5626974d9ba6d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:12:24Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-349f81b460384faf82e5626974d9ba6d2022-12-22T04:22:33ZengMDPI AGSensors1424-82202019-06-011911262710.3390/s19112627s19112627Robust Least-SquareLocalization Based on Relative Angular Matrix in Wireless Sensor NetworksYuyang Tian0Jing Lv1Shiwei Tian2Jinfei Zhu3Wei Lu4College of Communications Engineering, Army Engineering University of PLA, Nanjing 210042, ChinaCollege of Communications Engineering, Army Engineering University of PLA, Nanjing 210042, ChinaCollege of Communications Engineering, Army Engineering University of PLA, Nanjing 210042, ChinaCollege of Communications Engineering, Army Engineering University of PLA, Nanjing 210042, ChinaCollege of Communications Engineering, Army Engineering University of PLA, Nanjing 210042, ChinaAccurate position information plays an important role in wireless sensor networks (WSN), and cooperative positioning based on cooperation among agents is a promising methodology of providing such information. Conventional cooperative positioning algorithms, such as least squares (LS), rely on approximate position estimates obtained from prior measurements. This paper explores the fundamental mechanism underlying the least squares algorithm’s sensitivity to the initial position selection and approaches to dealing with such sensitivity. This topic plays an essential role in cooperative positioning, as it determines whether a cooperative positioning algorithm can be implemented ubiquitously. In particular, a sufficient and unnecessary condition for the least squares cost function to be convex is found and proven. We then propose a robust algorithm for wireless sensor network positioning that transforms the cost function into a globally convex function by detecting the null space of the relative angle matrix when all the targets are located inside the convex polygon formed by its neighboring nodes. Furthermore, we advance one step further and improve the algorithm to apply it in both the time of arrival (TOA) and angle of arrival/time of arrival (AOA/TOA) scenarios. Finally, the performance of the proposed approach is quantified via simulations, and the results show that the proposed method has a high positioning accuracy and is robust in both line-of-sight (LOS) and non-line-of-sight (NLOS) positioning environments.https://www.mdpi.com/1424-8220/19/11/2627wireless sensor networksleast-squares localizationconvexityrelative angular matrixnon-line-of-sight |
| spellingShingle | Yuyang Tian Jing Lv Shiwei Tian Jinfei Zhu Wei Lu Robust Least-SquareLocalization Based on Relative Angular Matrix in Wireless Sensor Networks Sensors wireless sensor networks least-squares localization convexity relative angular matrix non-line-of-sight |
| title | Robust Least-SquareLocalization Based on Relative Angular Matrix in Wireless Sensor Networks |
| title_full | Robust Least-SquareLocalization Based on Relative Angular Matrix in Wireless Sensor Networks |
| title_fullStr | Robust Least-SquareLocalization Based on Relative Angular Matrix in Wireless Sensor Networks |
| title_full_unstemmed | Robust Least-SquareLocalization Based on Relative Angular Matrix in Wireless Sensor Networks |
| title_short | Robust Least-SquareLocalization Based on Relative Angular Matrix in Wireless Sensor Networks |
| title_sort | robust least squarelocalization based on relative angular matrix in wireless sensor networks |
| topic | wireless sensor networks least-squares localization convexity relative angular matrix non-line-of-sight |
| url | https://www.mdpi.com/1424-8220/19/11/2627 |
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