An SOCP Estimator for Hybrid RSS and AOA Target Localization in Sensor Networks
This work addresses the problem of target localization in three-dimensional wireless sensor networks (WSNs). The proposed algorithm is based on a hybrid system that employs angle of arrival (AOA) and received signal strength (RSS) measurements, where the target’s transmit power is considered as an u...
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| 语言: | English |
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MDPI AG
2021-03-01
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| 丛编: | Sensors |
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| 在线阅读: | https://www.mdpi.com/1424-8220/21/5/1731 |
| _version_ | 1827603991429644288 |
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| author | Marcelo Salgueiro Costa Slavisa Tomic Marko Beko |
| author_facet | Marcelo Salgueiro Costa Slavisa Tomic Marko Beko |
| author_sort | Marcelo Salgueiro Costa |
| collection | DOAJ |
| description | This work addresses the problem of target localization in three-dimensional wireless sensor networks (WSNs). The proposed algorithm is based on a hybrid system that employs angle of arrival (AOA) and received signal strength (RSS) measurements, where the target’s transmit power is considered as an unknown parameter. Although both cases of a known and unknown target’s transmit power have been addressed in the literature, most of the existing approaches for unknown transmit power are either carried out recursively, or require a high computational cost. This results in an increased execution time of these algorithms, which we avoid in this work by proposing a single-iteration solution with moderate computational complexity. By exploiting the measurement models, a non-convex least squares (LS) estimator is derived first. Then, to tackle its nonconvexity, we resort to second-order cone programming (SOCP) relaxation techniques to transform the non-convex estimator into a convex one. Additionally, to make the estimator tighter, we exploit the angle between two vectors by using the definition of their inner product, which arises naturally from the derivation steps that are taken. The proposed method not only matches the performance of a computationally more complex state-of-the-art method, but it outperforms it for small <i>N</i>. This result is of a significant value in practice, since one desires to localize the target using the least number of anchor nodes as possible due to network costs. |
| first_indexed | 2024-03-09T05:50:43Z |
| format | Article |
| id | doaj.art-d5ae78f6fc4944b0b6dff234b13f22f9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T05:50:43Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d5ae78f6fc4944b0b6dff234b13f22f92023-12-03T12:18:07ZengMDPI AGSensors1424-82202021-03-01215173110.3390/s21051731An SOCP Estimator for Hybrid RSS and AOA Target Localization in Sensor NetworksMarcelo Salgueiro Costa0Slavisa Tomic1Marko Beko2Cognitive and People-Centric Computing Labs (COPELABS), Universidade Lusófona de Humanidades e Tecnologias, Campo Grande 376, 1749-024 Lisboa, PortugalCognitive and People-Centric Computing Labs (COPELABS), Universidade Lusófona de Humanidades e Tecnologias, Campo Grande 376, 1749-024 Lisboa, PortugalCognitive and People-Centric Computing Labs (COPELABS), Universidade Lusófona de Humanidades e Tecnologias, Campo Grande 376, 1749-024 Lisboa, PortugalThis work addresses the problem of target localization in three-dimensional wireless sensor networks (WSNs). The proposed algorithm is based on a hybrid system that employs angle of arrival (AOA) and received signal strength (RSS) measurements, where the target’s transmit power is considered as an unknown parameter. Although both cases of a known and unknown target’s transmit power have been addressed in the literature, most of the existing approaches for unknown transmit power are either carried out recursively, or require a high computational cost. This results in an increased execution time of these algorithms, which we avoid in this work by proposing a single-iteration solution with moderate computational complexity. By exploiting the measurement models, a non-convex least squares (LS) estimator is derived first. Then, to tackle its nonconvexity, we resort to second-order cone programming (SOCP) relaxation techniques to transform the non-convex estimator into a convex one. Additionally, to make the estimator tighter, we exploit the angle between two vectors by using the definition of their inner product, which arises naturally from the derivation steps that are taken. The proposed method not only matches the performance of a computationally more complex state-of-the-art method, but it outperforms it for small <i>N</i>. This result is of a significant value in practice, since one desires to localize the target using the least number of anchor nodes as possible due to network costs.https://www.mdpi.com/1424-8220/21/5/1731wireless sensor networkstarget localizationoptimizationReceived Signal Strength (RSS)Angle of Arrival (AOA) |
| spellingShingle | Marcelo Salgueiro Costa Slavisa Tomic Marko Beko An SOCP Estimator for Hybrid RSS and AOA Target Localization in Sensor Networks Sensors wireless sensor networks target localization optimization Received Signal Strength (RSS) Angle of Arrival (AOA) |
| title | An SOCP Estimator for Hybrid RSS and AOA Target Localization in Sensor Networks |
| title_full | An SOCP Estimator for Hybrid RSS and AOA Target Localization in Sensor Networks |
| title_fullStr | An SOCP Estimator for Hybrid RSS and AOA Target Localization in Sensor Networks |
| title_full_unstemmed | An SOCP Estimator for Hybrid RSS and AOA Target Localization in Sensor Networks |
| title_short | An SOCP Estimator for Hybrid RSS and AOA Target Localization in Sensor Networks |
| title_sort | socp estimator for hybrid rss and aoa target localization in sensor networks |
| topic | wireless sensor networks target localization optimization Received Signal Strength (RSS) Angle of Arrival (AOA) |
| url | https://www.mdpi.com/1424-8220/21/5/1731 |
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