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NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm
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| Zeitschriftentitel: | Remote Sensing |
|---|---|
| Personen und Körperschaften: | , , , , , , |
| In: | Remote Sensing, 11, 2019, 7, S. 773 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
MDPI AG
|
| Schlagwörter: |
| author_facet |
Wang, Lei Chen, Ruizhi Shen, Lili Qiu, Haiyang Li, Ming Zhang, Peng Pan, Yuanjin Wang, Lei Chen, Ruizhi Shen, Lili Qiu, Haiyang Li, Ming Zhang, Peng Pan, Yuanjin |
|---|---|
| author |
Wang, Lei Chen, Ruizhi Shen, Lili Qiu, Haiyang Li, Ming Zhang, Peng Pan, Yuanjin |
| spellingShingle |
Wang, Lei Chen, Ruizhi Shen, Lili Qiu, Haiyang Li, Ming Zhang, Peng Pan, Yuanjin Remote Sensing NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm General Earth and Planetary Sciences |
| author_sort |
wang, lei |
| spelling |
Wang, Lei Chen, Ruizhi Shen, Lili Qiu, Haiyang Li, Ming Zhang, Peng Pan, Yuanjin 2072-4292 MDPI AG General Earth and Planetary Sciences http://dx.doi.org/10.3390/rs11070773 <jats:p>The presence of None-line-of-sight (NLOS) is one of the major challenging issues in time of arrival (TOA) based source localization, especially for the sparse anchor scenarios. Sparse anchors can reduce the system deployment cost, so this has become increasingly popular in the source location. However, fewer anchors bring new challenges to ensure localization precision and reliability, especially in NLOS environments. The maximum likelihood (ML) estimation is the most popular location estimator for its simplicity and efficiency, while it becomes extremely difficult to reliably identify the NLOS measurements when the redundant observations are not enough. In this study, we proposed an NLOS detection algorithm called misclosure check (MC) to overcome this issue, which intends to provide a more reliable location in the sparse anchor environment. The MC algorithm checks the misclosure of different triangles and then obtains the possible NLOS from these misclosures. By forming multiple misclosure conditions, the MC algorithm can identify NLOS measurements reliably, even in a sparse anchor environment. The performance of the MC algorithm is evaluated in a typical sparse anchor environment and the results indicate that the MC algorithm achieves promising NLOS identification capacity without abundant redundant measurements. The real data test also confirmed that the MC algorithm achieves better position precision than other three robust location estimators in an NLOS environment since it can correctly identify more NLOS measurements.</jats:p> NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm Remote Sensing |
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| title |
NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm |
| title_unstemmed |
NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm |
| title_full |
NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm |
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NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm |
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NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm |
| title_short |
NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm |
| title_sort |
nlos mitigation in sparse anchor environments with the misclosure check algorithm |
| topic |
General Earth and Planetary Sciences |
| url |
http://dx.doi.org/10.3390/rs11070773 |
| publishDate |
2019 |
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773 |
| description |
<jats:p>The presence of None-line-of-sight (NLOS) is one of the major challenging issues in time of arrival (TOA) based source localization, especially for the sparse anchor scenarios. Sparse anchors can reduce the system deployment cost, so this has become increasingly popular in the source location. However, fewer anchors bring new challenges to ensure localization precision and reliability, especially in NLOS environments. The maximum likelihood (ML) estimation is the most popular location estimator for its simplicity and efficiency, while it becomes extremely difficult to reliably identify the NLOS measurements when the redundant observations are not enough. In this study, we proposed an NLOS detection algorithm called misclosure check (MC) to overcome this issue, which intends to provide a more reliable location in the sparse anchor environment. The MC algorithm checks the misclosure of different triangles and then obtains the possible NLOS from these misclosures. By forming multiple misclosure conditions, the MC algorithm can identify NLOS measurements reliably, even in a sparse anchor environment. The performance of the MC algorithm is evaluated in a typical sparse anchor environment and the results indicate that the MC algorithm achieves promising NLOS identification capacity without abundant redundant measurements. The real data test also confirmed that the MC algorithm achieves better position precision than other three robust location estimators in an NLOS environment since it can correctly identify more NLOS measurements.</jats:p> |
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| author | Wang, Lei, Chen, Ruizhi, Shen, Lili, Qiu, Haiyang, Li, Ming, Zhang, Peng, Pan, Yuanjin |
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| description | <jats:p>The presence of None-line-of-sight (NLOS) is one of the major challenging issues in time of arrival (TOA) based source localization, especially for the sparse anchor scenarios. Sparse anchors can reduce the system deployment cost, so this has become increasingly popular in the source location. However, fewer anchors bring new challenges to ensure localization precision and reliability, especially in NLOS environments. The maximum likelihood (ML) estimation is the most popular location estimator for its simplicity and efficiency, while it becomes extremely difficult to reliably identify the NLOS measurements when the redundant observations are not enough. In this study, we proposed an NLOS detection algorithm called misclosure check (MC) to overcome this issue, which intends to provide a more reliable location in the sparse anchor environment. The MC algorithm checks the misclosure of different triangles and then obtains the possible NLOS from these misclosures. By forming multiple misclosure conditions, the MC algorithm can identify NLOS measurements reliably, even in a sparse anchor environment. The performance of the MC algorithm is evaluated in a typical sparse anchor environment and the results indicate that the MC algorithm achieves promising NLOS identification capacity without abundant redundant measurements. The real data test also confirmed that the MC algorithm achieves better position precision than other three robust location estimators in an NLOS environment since it can correctly identify more NLOS measurements.</jats:p> |
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| spelling | Wang, Lei Chen, Ruizhi Shen, Lili Qiu, Haiyang Li, Ming Zhang, Peng Pan, Yuanjin 2072-4292 MDPI AG General Earth and Planetary Sciences http://dx.doi.org/10.3390/rs11070773 <jats:p>The presence of None-line-of-sight (NLOS) is one of the major challenging issues in time of arrival (TOA) based source localization, especially for the sparse anchor scenarios. Sparse anchors can reduce the system deployment cost, so this has become increasingly popular in the source location. However, fewer anchors bring new challenges to ensure localization precision and reliability, especially in NLOS environments. The maximum likelihood (ML) estimation is the most popular location estimator for its simplicity and efficiency, while it becomes extremely difficult to reliably identify the NLOS measurements when the redundant observations are not enough. In this study, we proposed an NLOS detection algorithm called misclosure check (MC) to overcome this issue, which intends to provide a more reliable location in the sparse anchor environment. The MC algorithm checks the misclosure of different triangles and then obtains the possible NLOS from these misclosures. By forming multiple misclosure conditions, the MC algorithm can identify NLOS measurements reliably, even in a sparse anchor environment. The performance of the MC algorithm is evaluated in a typical sparse anchor environment and the results indicate that the MC algorithm achieves promising NLOS identification capacity without abundant redundant measurements. The real data test also confirmed that the MC algorithm achieves better position precision than other three robust location estimators in an NLOS environment since it can correctly identify more NLOS measurements.</jats:p> NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm Remote Sensing |
| spellingShingle | Wang, Lei, Chen, Ruizhi, Shen, Lili, Qiu, Haiyang, Li, Ming, Zhang, Peng, Pan, Yuanjin, Remote Sensing, NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm, General Earth and Planetary Sciences |
| title | NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm |
| title_full | NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm |
| title_fullStr | NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm |
| title_full_unstemmed | NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm |
| title_short | NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm |
| title_sort | nlos mitigation in sparse anchor environments with the misclosure check algorithm |
| title_unstemmed | NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm |
| topic | General Earth and Planetary Sciences |
| url | http://dx.doi.org/10.3390/rs11070773 |