Contaminant cloud boundary monitoring using network of UAV sensors

Brian A. White; Antonios Tsourdos; Immanuel Ashokaraj; S. Subchan; Rafał Zbikowski

doi:10.1109/JSEN.2008.2004298

Contaminant cloud boundary monitoring using network of UAV sensors

Brian A. White, Antonios Tsourdos, Immanuel Ashokaraj, S. Subchan, Rafał Zbikowski

Cranfield University

Research output: Contribution to journal › Article › peer-review

75 Citations (Scopus)

Abstract

In this paper, we describe research work currently being undertaken to detect, model, and track the shape of a contaminant cloud boundary using air borne sensor swarms. A model of the contaminant cloud boundary is first developed using a splinegon, defined by a set of vertices linked by segments of constant curvature. This model is then used in an estimator to predict the evolution of the contaminant cloud. This approach is efficient in that only the vertices and segment curvatures are required to define the cloud boundary, rather than using a distribution function to represent the dispersion density.

Original language	English
Article number	4631426
Pages (from-to)	1681-1692
Number of pages	12
Journal	IEEE Sensors Journal
Volume	8
Issue number	10
DOIs	https://doi.org/10.1109/JSEN.2008.2004298
Publication status	Published - Oct 2008
Externally published	Yes

Keywords

Data fusion
Environmental monitoring
Sensor network
Unmanned autonomous systems

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10.1109/JSEN.2008.2004298

Cite this

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title = "Contaminant cloud boundary monitoring using network of UAV sensors",

abstract = "In this paper, we describe research work currently being undertaken to detect, model, and track the shape of a contaminant cloud boundary using air borne sensor swarms. A model of the contaminant cloud boundary is first developed using a splinegon, defined by a set of vertices linked by segments of constant curvature. This model is then used in an estimator to predict the evolution of the contaminant cloud. This approach is efficient in that only the vertices and segment curvatures are required to define the cloud boundary, rather than using a distribution function to represent the dispersion density.",

keywords = "Data fusion, Environmental monitoring, Sensor network, Unmanned autonomous systems",

author = "White, {Brian A.} and Antonios Tsourdos and Immanuel Ashokaraj and S. Subchan and Rafa{\l} Zbikowski",

note = "Funding Information: Manuscript received October 2, 2007; revised March 11, 2008; accepted March 26, 2008. This work was supported by the Data and Information Fusion Defence Technology Centre. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Fabien Josse.",

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AU - Zbikowski, Rafał

N1 - Funding Information: Manuscript received October 2, 2007; revised March 11, 2008; accepted March 26, 2008. This work was supported by the Data and Information Fusion Defence Technology Centre. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Fabien Josse.

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N2 - In this paper, we describe research work currently being undertaken to detect, model, and track the shape of a contaminant cloud boundary using air borne sensor swarms. A model of the contaminant cloud boundary is first developed using a splinegon, defined by a set of vertices linked by segments of constant curvature. This model is then used in an estimator to predict the evolution of the contaminant cloud. This approach is efficient in that only the vertices and segment curvatures are required to define the cloud boundary, rather than using a distribution function to represent the dispersion density.

AB - In this paper, we describe research work currently being undertaken to detect, model, and track the shape of a contaminant cloud boundary using air borne sensor swarms. A model of the contaminant cloud boundary is first developed using a splinegon, defined by a set of vertices linked by segments of constant curvature. This model is then used in an estimator to predict the evolution of the contaminant cloud. This approach is efficient in that only the vertices and segment curvatures are required to define the cloud boundary, rather than using a distribution function to represent the dispersion density.

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Contaminant cloud boundary monitoring using network of UAV sensors

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Keywords

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