TY - GEN
T1 - Identification of low-level point radiation sources using a sensor network
AU - Rao, Nageswara S.V.
AU - Shankar, Mallikarjun
AU - Srivathsan, Srinivasagopalan
AU - Iyengar, S. Sitharama
AU - Chin, Jren Chit
AU - Yau, David K.Y.
AU - Yang, Yong
AU - Hou, Jennifer C.
PY - 2008
Y1 - 2008
N2 - Identification of a low-level point radiation source amidst background radiation is achieved by a network of radiation sensors using a two-step approach. Based on measurements from three sensors, the geometric difference triangulation 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 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 radiation sources to emulate high intensity ones that cannot be easily and safely handled in laboratory experiments.
AB - Identification of a low-level point radiation source amidst background radiation is achieved by a network of radiation sensors using a two-step approach. Based on measurements from three sensors, the geometric difference triangulation 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 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 radiation sources to emulate high intensity ones that cannot be easily and safely handled in laboratory experiments.
KW - Detection and localization
KW - Point radiation source
KW - Sequential probability ratio test
UR - https://www.scopus.com/pages/publications/51249118817
U2 - 10.1109/IPSN.2008.19
DO - 10.1109/IPSN.2008.19
M3 - Conference contribution
AN - SCOPUS:51249118817
SN - 9780769531571
T3 - Proceedings - 2008 International Conference on Information Processing in Sensor Networks, IPSN 2008
SP - 493
EP - 504
BT - Proceedings - 2008 International Conference on Information Processing in Sensor Networks, IPSN 2008
T2 - 2008 International Conference on Information Processing in Sensor Networks, IPSN 2008
Y2 - 22 April 2008 through 24 April 2008
ER -