Abstract
Modeling of background radiation for the urban environment plays an important role in homeland security. However, background radiation is difficult to assess due to its spatial-temporal fluctuations caused by the variation in soil composition, building materials, and weather patterns etc. To address the challenge of background radiation modeling, we developed a mobile sensor network to continuously monitor the background radiation; we also proposed a maximum likelihood estimation algorithm to decouple and estimate the background’s spatial distribution and temporal fluctuation. Experimental results demonstrated how this background radiation monitoring system accurately recognized high background regions in the experimental area, and successfully captured temporal fluctuation trends of background radiation during rains. Our system provides an efficient solution to model the temporal fluctuation and spatial distribution of background radiation.
Highlights
In the area of homeland security, environmental monitoring, and radiation regulation, sensor networks have been used to monitor a geographic region’s radiation level and detect anomalous radiation sources [1,2,3]
We proposed a background radiation estimation algorithm that models the spatial distribution and temporal fluctuation of background radiation based on measurements from the mobile sensor network
Our mobile sensor network is capable of measuring gamma-ray spectra, we focused on the gross count rate of background radiation as an initial study
Summary
In the area of homeland security, environmental monitoring, and radiation regulation, sensor networks have been used to monitor a geographic region’s radiation level and detect anomalous radiation sources [1,2,3]. In [10], the spatial distribution of background radiation was taken into account during the source detection experiment When initializing their MLE algorithm, they manually divided the experimental area into high and low background regions. The major contribution of this paper is the development of a real-time data streaming mobile sensor network and a maximum-likelihood based algorithm for background radiation modeling (the BR-MLE algorithm). This provides an efficient solution to long-term monitoring of an area’s radiation and to model the detailed background radiation distribution in both space and time. We deployed a one-node sensor network on the campus of University of Illinois, and applied the BR-MLE algorithm to estimate the spatial distribution and temporal fluctuation of background radiation
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