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A dynamic fusion system for fast nuclear source detection and localization with mobile sensor networks
Author(s) -
Grelaud Aude,
Mitra Priyam,
Xie Minge,
Chen Rong
Publication year - 2017
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2293
Subject(s) - computer science , real time computing , global positioning system , sensor fusion , taxis , deep learning , inference , bayesian probability , artificial intelligence , data mining , telecommunications , engineering , transport engineering
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. Copyright © 2017 John Wiley & Sons, Ltd.