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Robust distributed cooperative RSS-based localization for directed graphs in mixed LoS/NLoS environments

Carlino, Luca and Jin, Di and Muma, Michael and Zoubir, Abdelhak M. :
Robust distributed cooperative RSS-based localization for directed graphs in mixed LoS/NLoS environments.
[Online-Edition]
In: EURASIP Journal on Wireless Communications and Networking, 19 ISSN 1687-1499
[Article], (2019)

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Item Type: Article
Title: Robust distributed cooperative RSS-based localization for directed graphs in mixed LoS/NLoS environments
Language: English
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.

Journal or Publication Title: EURASIP Journal on Wireless Communications and Networking
Volume: 19
Publisher: SpringerOpen
Classification DDC: 600 Technik, Medizin, angewandte Wissenschaften > 600 Technik
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Signal Processing
Date Deposited: 12 Jul 2019 12:32
Last Modified: 12 Jul 2019 12:40
DOI: 10.1186/s13638-018-1335-7
Official URL: https://doi.org/10.1186/s13638-018-1335-7
URN: urn:nbn:de:tuda-tuprints-88583
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/8858
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