Multiple Sources Localization by the WSN Using the Direction-of-Arrivals Classified by the Genetic Algorithm
| Autor: | Yuan Zhang, Yue Ivan Wu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Fitness function
General Computer Science Computer science 010401 analytical chemistry Real-time computing General Engineering triangulation 02 engineering and technology multiple sources localization 01 natural sciences 0104 chemical sciences wireless sensor network Direction-of-arrival Orders of magnitude (time) Sensor node Genetic algorithm genetic algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing General Materials Science lcsh:Electrical engineering. Electronics. Nuclear engineering Cluster analysis lcsh:TK1-9971 Wireless sensor network |
| Zdroj: | IEEE Access, Vol 7, Pp 173626-173635 (2019) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2019.2956825 |
| Popis: | Simultaneously locating multiple sources passively in the wireless sensor networks (WSN) is challenging in the internet of things (IoT) applications, where reducing the computation and communication load is of great importance due to the requirement on real-time processing and the energy constraint. This is especially true when the number of sources or the number of sensor nodes is large. In this paper, a localization algorithm to estimate multiple sources' positions in the three-dimensional space is proposed. With the direction-of-arrival (DOA) estimates for multiple sources obtained at each sensor node, it is crucial to discriminate which estimate corresponds to which source. To save the computation resources, a classification method based on the genetic algorithm is proposed to handle the multiple sources. A fitness function is designed to assess the clustering of the DOA estimates. Extensive simulations are carried out to analyze the algorithm performance under the various settings. Numerical examples show that the proposed method could lower the computational burden by orders of magnitude compared to the conventional method, without significantly sacrificing the estimation accuracy. |
| Databáze: | OpenAIRE |
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