Identification of low-level point radioactive sources using a sensor network

Abstract

Identification of a low-level point radioactive source amidst background radiation is achieved by a network of radiation sensors using a two-step approach. Based on measurements from three or more sensors, a geometric difference triangulation method or an N-sensor localization method is used to estimate the location and strength of the source. Then a sequential probability ratio test based on current measurements and estimated parameters is employed to finally decide: (1) the presence of a source with the estimated parameters, or (2) the absence of the source, or (3) the Author's address: J.-C. Chin: email: jcchin@cs.purdue.edu. c 2010 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by a contractor or affiliate of the U.S. Government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 ( insufficiency of measurements to make a decision. This method achieves specified levels of false alarm and missed detection probabilities, while ensuring a close-to-minimal number of measurements for reaching a decision. This method minimizes the ghost-source problem of current estimation methods, and achieves a lower false alarm rate compared with current detection methods. This method is tested and demonstrated using: (1) simulations, and (2) a test-bed that utilizes the scaling properties of point radioactive sources to emulate high intensity ones that cannot be easily and safely handled in laboratory experiments.

Constrained decision-making for low-count radiation detection by mobile sensors

“…For the most part, approaches in available literature attempt to identify not only the nature of the target but also its location solely based on radiation counters (Brennan et al 2005;Chin et al 2010;Nemzek et al 2004). Such approaches essentially face a combination of problems-detection and localization-which is inherently very challenging, both at an analytical and computational level.…”

“…Such approaches essentially face a combination of problems-detection and localization-which is inherently very challenging, both at an analytical and computational level. For static sensors and source, a location estimator can be constructed, and a sequential probability ratio test can be formed (Chin et al 2010). In addition to estimating the source's position, algorithms within a Bayesian framework can also estimate source intensity (Brennan et al 2005;Nemzek et al 2004).…”

A robust hypothesis test for the sensitive detection of constant speed radiation moving sources

“…Such networks, of variable geometries and dimensions, are particularly praised for source localization applications, in which they typically appear twodimensional (2-D) and large-scale. Dedicated algorithms have then been elaborated to track the source trajectory without prior knowledge, as carried out with Markov Chains calculations and Bayesian methods by Brennan et al [7] or triangulation by Chin et al [8].…”

Active sensor networks for nuclear detection

“…Such approaches essentially face a combination of problems-detection and localization-which is inherently very challenging, both at an analytical and computational level. For static sensors and source, a location estimator can be constructed, and a sequential probability ratio test can be formed [6]. In addition to target state, intensity estimators have also been developed, within a Bayesian framework [5], [7].…”