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Information-sharing and decision-making in networks of radiation detectors

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Abstract

A network of sensors observes a time-inhomo-geneous Poisson signal and within a fixed time interval has to decide between two hypotheses regarding the signal’s intensity. The paper reveals an interplay between network topology, essentially determining the quantity of information available to different sensors, and the quality of individual sensor information as captured by the sensor’s likelihood ratio. Armed with analytic expressions of bounds on the error probabilities associated with the binary hypothesis test regarding the intensity of the observed signal, the insight into the interplay between sensor communication and data quality helps in deciding which sensor is better positioned to make a decision on behalf of the network, and links the analysis to network centrality concepts. The analysis is illustrated on networked radiation detection examples, first in simulation and then on cases utilizing field measurement data available through a U.S. Domestic Nuclear Detection Office (dndo) database.

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Notes

  1. For a test \(B_1\), the probability of detection is given by \(\mathbb {P}_1(B_1)\); of course, this equals \(1-\mathbb {P}_1(\varOmega \setminus B_1)\) where \(\mathbb {P}_1(\varOmega \setminus B_1)\) is the probability of miss.

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Acknowledgements

This work is supported in part by DTRA under award #HDTRA1-16-1-0039.

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Authors and Affiliations

  1. University of Delaware, Newark, USA

    Indrajeet Yadav, Herbert G. Tanner & Ioannis Poulakakis

  2. Indian Institute of Technology, Gandhinagar, India

    Chetan D. Pahlajani

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  1. Indrajeet Yadav
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  2. Chetan D. Pahlajani
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  3. Herbert G. Tanner
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  4. Ioannis Poulakakis
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Correspondence to Indrajeet Yadav.

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This is one of several papers published in Autonomous Robots comprising the “Special Issue on Distributed Robotics: From Fundamentals to Applications”.

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Yadav, I., Pahlajani, C.D., Tanner, H.G. et al. Information-sharing and decision-making in networks of radiation detectors. Auton Robot 42, 1715–1730 (2018). https://doi.org/10.1007/s10514-018-9716-7

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