Heuristic techniques for maximum likelihood localization of radioactive sources via a sensor network

Assem Abdelhakim

doi:10.1007/s41365-023-01267-3

Your Location：

Home >

Browse articles >

Heuristic techniques for maximum likelihood localization of radioactive sources via a sensor network

NUCLEAR PHYSICS AND INTERDISCIPLINARY RESEARCH | Updated：2023-09-19

- Heuristic techniques for maximum likelihood localization of radioactive sources via a sensor network
- Nuclear Science and Techniques Vol. 34, Issue 8, Article number: 127(2023)
- Affiliations：
  
  1.Department of Radiation Engineering, National Center for Radiation Research and Technology, Egyptian Atomic Energy Authority, 11787, Cairo, Egypt.
- Author bio：
  
  assem.abdelhakim@eaea.org.eg
- Funds：
- DOI：10.1007/s41365-023-01267-3
  CLC：
Scan for full text
Assem Abdelhakim. Heuristic techniques for maximum likelihood localization of radioactive sources via a sensor network. [J]. Nuclear Science and Techniques 34(8):127(2023)
DOI：

Assem Abdelhakim. Heuristic techniques for maximum likelihood localization of radioactive sources via a sensor network. [J]. Nuclear Science and Techniques 34(8):127(2023) DOI： 10.1007/s41365-023-01267-3.

摘要

Abstract

Maximum likelihood estimation (MLE) is an effective method for localizing radioactive sources in a given area. However, it requires an exhaustive search for parameter estimation, which is time consuming. In this study, heuristic techniques were employed to search for radiation source parameters that provide the maximum likelihood by using a network of sensors. Hence, the time consumption of MLE would be effectively reduced. First, the radiation source was detected using the ,k,-sigma method. Subsequently, the MLE was applied for parameter estimation using the readings and positions of the detectors that have detected the radiation source. A comparative study was performed in which the estimation accuracy and time consumption of the MLE were evaluated for traditional methods and heuristic techniques. The traditional MLE was performed via a grid search method using fixed and multiple resolutions. Additionally, four commonly used heuristic algorithms were applied: the firefly algorithm (FFA), particle swarm optimization (PSO), ant colony optimization (ACO), and artificial bee colony (ABC). The experiment was conducted using real data collected by the Low Scatter Irradiator facility at the Savannah River National Laboratory as part of the Intelligent Radiation Sensing System program. The comparative study showed that the estimation time was 3.27 s using fixed resolution MLE and 0.59 s using multi-resolution MLE. The time consumption for the heuristic-based MLE was 0.75, 0.03, 0.02, and 0.059 s for FFA, PSO, ACO, and ABC, respectively. The location estimation error was approximately 0.4 m using either the grid search-based MLE or the heuristic-based MLE. Hence, heuristic-based MLE can provide comparable estimation accuracy through a less time consuming process than traditional MLE

关键词

Keywords

Radioactive sourceMaximum Likelihood Estimation Multi-resolution MLEk-sigmaFirefly algorithmParticle swarm optimizationAnt colony optimizationArtificial bee colony

references

J. Guizerix, V. Markovic, P. Airey, Radioisotopes and radiation technology in industry. IAEA Bulletin 29(2), 20-24, (1987)

S. Jain, Radiation in medical practice & health effects of radiation: Rationale, risks, and rewards. J. Family Med. Prim. Care 10(4), 1520-1524 (2021). doi: 10.4103/jfmpc.jfmpc_2292_20http://doi.org/10.4103/jfmpc.jfmpc_2292_20

L. Zhou, S.-M. Wang, D.-Q. Fang et al., Recent progress in two-proton radioactivity. Nucl. Sci. Tech. 33(8), 105 (2022). doi: 10.1007/s41365-022-01091-1http://doi.org/10.1007/s41365-022-01091-1

B. Li, N. Tang, Y.-H. Zhang et al., Production of p-rich nuclei with Z= 20-25 based on radioactive ion beams. Nucl. Sci. Tech. 33(5), 55 (2022). doi: 10.1007/s41365-022-01048-4http://doi.org/10.1007/s41365-022-01048-4

IAEA: Incident and trafficking database (ITDB), https://www.iaea.org/resources/databases/itdbhttps://www.iaea.org/resources/databases/itdb.

S. Sen, N.S. Rao, C.Q. Wu et al., Performance analysis of Wald-statistic based network detection methods for radiation sources. IEEE 19th International Conference on Information Fusion (FUSION), 2016.

C.Q. Wu, M.L. Berry, K.M. Grieme et al., Network detection of radiation sources using localization-based approaches. IEEE T. Ind. Inf. 15(4), 2308-2320 (2019). doi: 10.1109/TII.2019.2891253http://doi.org/10.1109/TII.2019.2891253

R. J. Nemzek, J. S. Dreicer, D. C. Torney et al., Distributed sensor networks for detection of mobile radioactive sources. IEEE T Nucl. Sci. 51(4), 1693-1700 (2004). doi: 10.1109/NSSMIC.2003.1352153http://doi.org/10.1109/NSSMIC.2003.1352153

A. Gunatilaka, B. Ristic, R. Gailis, On localisation of a radiological point source. 2007 Information, Decision and Control, 2007. doi: 10.1109/IDC.2007.374556http://doi.org/10.1109/IDC.2007.374556

C. J. Sullivan, Radioactive source localization in urban environments with sensor networks and the Internet of Things. 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 2016. doi: 10.1109/MFI.2016.7849518http://doi.org/10.1109/MFI.2016.7849518

J.Y. Hesterman, L. Caucci, M.A. Kupinski et al., Maximum-likelihood estimation with a contracting-grid search algorithm. IEEE T Nucl. Sci. 57(3), 1077-1084 (2010). doi: 10.1109/TNS.2010.2045898http://doi.org/10.1109/TNS.2010.2045898

C. Avram, S. Folea, D. Radu et al., Wireless Radiation Monitoring System. ECMS, 2017.

E.-w. Bai, A. Heifetz, P. Raptis et al., Maximum likelihood localization of radioactive sources against a highly fluctuating background. IEEE T Nucl. Sci. 62(6), 3274-3282 (2015). doi: 10.1109/TNS.2015.2497327http://doi.org/10.1109/TNS.2015.2497327

Z. Liu, S. Abbaszadeh, Double Q-learning for radiation source detection. Sensors, 19(4), 960 (2019). doi: 10.3390/s19040960http://doi.org/10.3390/s19040960

J. Zhao, Z. Zhang, C.J. Sullivan, Identifying anomalous nuclear radioactive sources using Poisson kriging and mobile sensor networks. PloS one, 14(5), e0216131 (2019). doi: 10.1371/journal.pone.0216131http://doi.org/10.1371/journal.pone.0216131

A. Reinhart, An integrated system for gamma-ray spectral mapping and anomaly detection. University of texas, 2013

I.J. Michaud, K. Schmidt, R.C. Smith et al., A hierarchical Bayesian model for background variation in radiation source localization. Nucl. Instrum. Methods A 1002, 165288 (2021). doi: 10.1016/j.nima.2021.165288http://doi.org/10.1016/j.nima.2021.165288

A. Bukartas, R. Finck, J. Wallin et al., A Bayesian method to localize lost gamma sources. Appl. Radiat. Isotopes 145, 142-147, (2019). doi: 10.1016/j.apradiso.2018.11.008http://doi.org/10.1016/j.apradiso.2018.11.008

J.-C. Chin, D.K. Yau, N.S. Rao et al., Accurate localization of low-level radioactive source under noise and measurement errors. Proceedings of the 6th ACM conference on Embedded network sensor systems, 2008.

J.-C. Chin, N.S. Rao, D.K. Yau et al., Identification of low-level point radioactive sources using a sensor network. ACM T Sensor Network 7(3), 1-35 (2010). doi: 10.1145/1807048.1807050http://doi.org/10.1145/1807048.1807050

M.R. Morelande, B. Ristic, Radiological source detection and localisation using Bayesian techniques. IEEE T Signal Proces 57(11), 4220-4231 (2009). doi: 10.1109/TSP.2009.2026618http://doi.org/10.1109/TSP.2009.2026618

J.-C. Chin, D. K. Yau, N. S. Rao, Efficient and robust localization of multiple radiation sources in complex environments. IEEE 31st International Conference on Distributed Computing Systems, 2011. doi: 10.1109/ICDCS.2011.94http://doi.org/10.1109/ICDCS.2011.94

N. S. Rao, S. Sen, N. J. Prins et al., Network algorithms for detection of radiation sources. Nucl. Instrum. Methods A 784, 326-331 (2015). doi: 10.1016/j.nima.2015.01.037http://doi.org/10.1016/j.nima.2015.01.037

Z. Liu, S. Abbaszadeh, C.J. Sullivan, Spatial-temporal modeling of background radiation using mobile sensor networks. PloS one 13(10), e0205092 (2018). doi: 10.1371/journal.pone.0205092http://doi.org/10.1371/journal.pone.0205092

M. Morelande, B. Ristic, A. Gunatilaka, Detection and parameter estimation of multiple radioactive sources. IEEE 10th International Conference on Information Fusion, 2007. doi: 10.1109/ICIF.2007.4408094http://doi.org/10.1109/ICIF.2007.4408094

B. Deb, Iterative estimation of location and trajectory of radioactive sources with a networked system of detectors. IEEE T Nucl. Sci. 60(2), 1315-1326 (2013). doi: 10.1109/TNS.2013.2247060http://doi.org/10.1109/TNS.2013.2247060

M. Mitchell, An introduction to genetic algorithms. (MIT press, 1998).

X.-S. Yang, Nature-inspired metaheuristic algorithms. (Luniver press, 2010).

Y. Shi, Particle swarm optimization: developments, applications and resources. IEEE Proceedings of the 2001 congress on evolutionary computation (IEEE Cat. No. 01TH8546), 2001. doi: 10.1109/CEC.2001.934374http://doi.org/10.1109/CEC.2001.934374

M. Dorigo, Optimization, learning and natural algorithms. Politecnico di Milano, 1992

A.E. Ezugwu, J.O. Agushaka, L. Abualigah et al., Prairie dog optimization algorithm. Neural Comput. Appl. 34(22), 20017-20065 (2022). doi: 10.1007/s00521-022-07530-9http://doi.org/10.1007/s00521-022-07530-9

J.O. Agushaka, A.E. Ezugwu, L. Abualigah, Gazelle Optimization Algorithm: A novel nature-inspired metaheuristic optimizer. Neural Comput. Appl. 35, 1-33, (2022). doi: 10.1007/s00521-022-07854-6http://doi.org/10.1007/s00521-022-07854-6

J.O. Agushaka, A.E. Ezugwu, L. Abualigah, Dwarf mongoose optimization algorithm. Comput. Method. Appl. M. 391, 114570 (2022). doi: 10.1016/j.cma.2022.114570http://doi.org/10.1016/j.cma.2022.114570

L. Abualigah, M. Abd Elaziz, P. Sumari et al., Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer. Expert Syst. Appl. 191, 116158 (2022). doi: 10.1016/j.eswa.2021.116158http://doi.org/10.1016/j.eswa.2021.116158

L. Abualigah, D. Yousri, M. Abd Elaziz et al., Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput. Industrial Engineering, 157, 107250 (2021). doi: 10.1016/j.cie.2021.107250http://doi.org/10.1016/j.cie.2021.107250

G. Cordone, Improvements to MLE algorithm for localizing radiation sources with a distributed detector network. Clemson University, 2019

H. E. Baidoo-Williams, Maximum likelihood localization of radiation sources with unknown source intensity. arXiv:160800427, (2016)

G. Cordone, R. R. Brooks, S. Sen et al., Improved multi-resolution method for mle-based localization of radiation sources. IEEE 20th International Conference on Information Fusion (Fusion), 2017. doi: 10.23919/ICIF.2017.8009626http://doi.org/10.23919/ICIF.2017.8009626

S. Arora, S. Singh, The firefly optimization algorithm: convergence analysis and parameter selection. International Journal of Computer Applications, 69(3), 48-52 (2013). doi: 10.5120/11826-7528http://doi.org/10.5120/11826-7528

Y. Shi, R. Eberhart, A modified particle swarm optimizer. IEEE international conference on evolutionary computation proceedings, 1998. doi: 10.1109/ICEC.1998.699146http://doi.org/10.1109/ICEC.1998.699146

A. Engelbrecht, Particle swarm optimization: Velocity initialization. IEEE Congress on Evolutionary Computation, Brisbane, QLD, Australia, 2012, pp. 1-8. doi: 10.1109/CEC.2012.6256112http://doi.org/10.1109/CEC.2012.6256112

K. Socha, M. Dorigo, Ant colony optimization for continuous domains. Eur. J. Oper. Res. 185(3), 1155-1173 (2008). doi: 10.1109/ICNC.2012.6234538http://doi.org/10.1109/ICNC.2012.6234538

S. Pourtakdoust, H. Nobahari, An extension of ant colony to continuous optimization problems. Proceedings of the ANTS 2004–Fourth International Workshop on Ant Colony Optimization and Swarm Intelligence, 2004. doi: 10.1007/978-3-540-28646-2_27http://doi.org/10.1007/978-3-540-28646-2_27

D. Karaboga, An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes university, 2005

D. A. Cooper, R. J. Ledoux, K. Kamieniecki et al., Intelligent radiation sensor system (irss) advanced technology demonstrator (atd). IEEE Conference on Technologies for Homeland Security (HST), 2012. doi: 10.1109/THS.2012.6459901http://doi.org/10.1109/THS.2012.6459901

IRSS "Canonical irss datasets" Available: https://github.com/raonsv/canonical-datasetshttps://github.com/raonsv/canonical-datasets.

J. Klumpp, Statistical methods for the detection and analysis of radioactive sources. Colorado State University, 2014

Y. Shi, R. C. Eberhart, Parameter selection in particle swarm optimization. Evolutionary Programming VII International Conference, 1998. doi: 10.1007/BFb0040810http://doi.org/10.1007/BFb0040810

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

The study of a neutron spectrum unfolding method based on Particle Swarm Optimization combined with Maximum Likelihood Expectation Maximization

Related Author

No data

Related Institution

Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province, Chengdu University of Technology

National Key Laboratory for Metrology and Calibration Techniques, China Institute of Atomic Energy

Address：Editorial Office of NST, 2019 Jialuo Road, Jiading, Shanghai 201800, China Postal code：201800
Email：nst@sinap.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 沪ICP备05005479号-1 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰