A dynamic fusion system for fast nuclear source detection and localization with mobile sensor networks

Aude Grelaud, Priyam Mitra, Minge Xie, Rong Chen

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


This paper proposes a dynamic system, with an associated fusion learning inference procedure, to perform real-time detection and localization of nuclear sources using a network of mobile sensors. This is motivated by the need for a reliable detection system in order to prevent nuclear attacks in major cities such as New York City. The approach advocated here installs a large number of relatively inexpensive (and perhaps relatively less accurate) nuclear source detection sensors and GPS devices in taxis and police vehicles moving in the city. Sensor readings and GPS information are sent to a control center at a high frequency, where the information is immediately processed and fused with the earlier signals. We develop a real-time detection and localization method aimed at detecting the presence of a nuclear source and estimating its location and power. We adopt a Bayesian framework to perform the fusion learning and use a sequential Monte Carlo algorithm to estimate the parameters of the model and to perform real-time localization. A simulation study is provided to assess the performance of the method for both stationary and moving sources. The results provide guidance and recommendations for an actual implementation of such a surveillance system.

Original languageEnglish (US)
Pages (from-to)4-19
Number of pages16
JournalApplied Stochastic Models in Business and Industry
Issue number1
StatePublished - Jan 1 2018

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Business, Management and Accounting(all)
  • Management Science and Operations Research


  • Bayesian model choice
  • SMC algorithm
  • nuclear detection
  • real-time localization


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