Carlino, Luca ; Jin, Di ; Muma, Michael ; Zoubir, Abdelhak M. (2019)
Robust distributed cooperative RSS-based localization for directed graphs in mixed LoS/NLoS environments.
In: EURASIP Journal on Wireless Communications and Networking, 2019, 2019
Artikel, Zweitveröffentlichung
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Kurzbeschreibung (Abstract)
The accurate and low-cost localization of sensors using a wireless sensor network is critically required in a wide range of today’s applications. We propose a novel, robust maximum likelihood-type method for distributed cooperative received signal strength-based localization in wireless sensor networks. To cope with mixed LoS/NLoS conditions, we model the measurements using a two-component Gaussian mixture model. The relevant channel parameters, including the reference path loss, the path loss exponent, and the variance of the measurement error, for both LoS and NLoS conditions, are assumed to be unknown deterministic parameters and are adaptively estimated. Unlike existing algorithms, the proposed method naturally takes into account the (possible) asymmetry of links between nodes. The proposed approach has a communication overhead upper-bounded by a quadratic function of the number of nodes and computational complexity scaling linearly with it. The convergence of the proposed method is guaranteed for compatible network graphs, and compatibility can be tested a priori by restating the problem as a graph coloring problem. Simulation results, carried out in comparison to a centralized benchmark algorithm, demonstrate the good overall performance and high robustness in mixed LoS/NLoS environments.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Carlino, Luca ; Jin, Di ; Muma, Michael ; Zoubir, Abdelhak M. |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Robust distributed cooperative RSS-based localization for directed graphs in mixed LoS/NLoS environments |
| Sprache: | Englisch |
| Publikationsjahr: | 2019 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 24 Januar 2019 |
| Verlag: | SpringerOpen |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | EURASIP Journal on Wireless Communications and Networking |
| Jahrgang/Volume einer Zeitschrift: | 2019 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/8858 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung aus gefördertem Golden Open Access |
| Kurzbeschreibung (Abstract): | The accurate and low-cost localization of sensors using a wireless sensor network is critically required in a wide range of today’s applications. We propose a novel, robust maximum likelihood-type method for distributed cooperative received signal strength-based localization in wireless sensor networks. To cope with mixed LoS/NLoS conditions, we model the measurements using a two-component Gaussian mixture model. The relevant channel parameters, including the reference path loss, the path loss exponent, and the variance of the measurement error, for both LoS and NLoS conditions, are assumed to be unknown deterministic parameters and are adaptively estimated. Unlike existing algorithms, the proposed method naturally takes into account the (possible) asymmetry of links between nodes. The proposed approach has a communication overhead upper-bounded by a quadratic function of the number of nodes and computational complexity scaling linearly with it. The convergence of the proposed method is guaranteed for compatible network graphs, and compatibility can be tested a priori by restating the problem as a graph coloring problem. Simulation results, carried out in comparison to a centralized benchmark algorithm, demonstrate the good overall performance and high robustness in mixed LoS/NLoS environments. |
| URN: | urn:nbn:de:tuda-tuprints-88583 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 600 Technik |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Robust Data Science 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Signalverarbeitung |
| Hinterlegungsdatum: | 12 Jul 2019 12:32 |
| Letzte Änderung: | 15 Nov 2023 10:41 |
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| Suche nach Titel in: | TUfind oder in Google |
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- Robust distributed cooperative RSS-based localization for directed graphs in mixed LoS/NLoS environments. (deposited 12 Jul 2019 12:32) [Gegenwärtig angezeigt]
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