Physics-Based Detection of Radioactive Contraband: A Sequential Bayesian Approach

被引:18
|
作者
Candy, J. V. [1 ]
Breitfeller, E. [1 ]
Guidry, B. L. [1 ]
Manatt, D. [2 ]
Sale, K. [1 ]
Chambers, D. H. [1 ]
Axelrod, M. A. [1 ]
Meyer, A. M. [1 ]
机构
[1] Lawrence Livermore Natl Lab, Livermore, CA 94551 USA
[2] SAIC, Adv Engn & Appl Sci Div, San Diego, CA 92127 USA
关键词
Kalman filter; particle filter; physics-based approach; sequential Bayesian processor; sequential Monte Carlo; sequential radionuclide detection; DECONVOLUTION;
D O I
10.1109/TNS.2009.2034374
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The timely and accurate detection of nuclear contraband is an extremely important problem of national security. The development of a prototype sequential Bayesian processor that incorporates the underlying physics of gamma-ray emissions and the measurement of photon energies and their interarrival times that offers a physics-based approach to attack this challenging problem is described. A basic radionuclide representation in terms of its gamma-ray energies along with photon interarrival times is used to extract the physics information available from the uncertain measurements. It is shown that not only does this approach lead to a physics-based structure that can be used to develop an effective threat detection technique, but also motivates the implementation of this approach using advanced sequential Monte Carlo processors or particle filters to extract the required information. The resulting processor is applied to experimental data to demonstrate its feasibility.
引用
收藏
页码:3694 / 3711
页数:18
相关论文
共 50 条
  • [41] Using physics-based priors in a Bayesian algorithm to enhance infrasound source location
    Marcillo, Omar
    Arrowsmith, Stephen
    Whitaker, Rod
    Anderson, Dale
    Nippress, Alexandra
    Green, David N.
    Drob, Douglas
    GEOPHYSICAL JOURNAL INTERNATIONAL, 2014, 196 (01) : 375 - 385
  • [42] Physics-based Bayesian linear regression model for predicting length of mixed oil
    Yuan, Ziyun
    Chen, Lei
    Liu, Gang
    Shao, Weiming
    Zhang, Yuhan
    Yang, Wen
    GEOENERGY SCIENCE AND ENGINEERING, 2023, 223
  • [43] Correlating tau pathology to brain atrophy using a physics-based Bayesian model
    Amelie Schäfer
    Pavanjit Chaggar
    Alain Goriely
    Ellen Kuhl
    Engineering with Computers, 2022, 38 : 3867 - 3877
  • [44] A Zone-Based Approach for Physics-Based FET Compact Models
    Trew, R. J.
    2016 21ST INTERNATIONAL CONFERENCE ON MICROWAVE, RADAR AND WIRELESS COMMUNICATIONS (MIKON), 2016,
  • [45] A Sequential Model Parameter Extraction Technique for Physics-Based IGBT Compact Models
    Navarro, Dondee
    Sano, Takeshi
    Furui, Yoshiharu
    IEEE TRANSACTIONS ON ELECTRON DEVICES, 2013, 60 (02) : 580 - 586
  • [46] Spectral-360: A Physics-Based Technique for Change Detection
    Sedky, Mohamed
    Moniri, Mansour
    Chibelushi, Claude C.
    2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW), 2014, : 405 - 408
  • [47] Physics-Based Self-Supervised Grasp Pose Detection
    Ruiz, Jon Ander
    Iriondo, Ander
    Lazkano, Elena
    Ansuategi, Ander
    Maurtua, Inaki
    MACHINES, 2025, 13 (01)
  • [48] A watchdog model for physics-based anomaly detection in digital substations
    Tarazi, Hussam
    Sutton, Sara
    Olinjyk, John
    Bond, Benjamin
    Rrushi, Julian
    INTERNATIONAL JOURNAL OF CRITICAL INFRASTRUCTURE PROTECTION, 2024, 44
  • [49] Moving Cast Shadow Detection using Physics-based Features
    Huang, Jia-Bin
    Chen, Chu-Song
    CVPR: 2009 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOLS 1-4, 2009, : 2310 - 2317
  • [50] Is Physics-based Liveness Detection Truly Possible with a Single Image?
    Bai, Jiamin
    Ng, Tian-Tsong
    Gao, Xinting
    Shi, Yun-Qing
    2010 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, 2010, : 3425 - 3428