Carlino, L. ; Jin, D. ; Muma, M. ; Zoubir, A. 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
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
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, L. ; Jin, D. ; Muma, M. ; Zoubir, A. M. |
| Art des Eintrags: | Bibliographie |
| Titel: | Robust Distributed Cooperative RSS-based Localization for Directed Graphs in Mixed LoS/NLoS Environments |
| Sprache: | Englisch |
| Publikationsjahr: | 24 Januar 2019 |
| Verlag: | Springer |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | EURASIP Journal on Wireless Communications and Networking |
| Jahrgang/Volume einer Zeitschrift: | 2019 |
| URL / URN: | https://doi.org/10.1186/s13638-018-1335-7 |
| Zugehörige Links: | |
| 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. |
| Zusätzliche Informationen: | Art.No.: 19, Erstveröffentlichung |
| 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: | 11 Sep 2017 07:20 |
| Letzte Änderung: | 03 Jul 2024 02:28 |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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Robust distributed cooperative RSS-based localization for directed graphs in mixed LoS/NLoS environments. (deposited 12 Jul 2019 12:32)
- Robust Distributed Cooperative RSS-based Localization for Directed Graphs in Mixed LoS/NLoS Environments. (deposited 11 Sep 2017 07:20) [Gegenwärtig angezeigt]
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