Chemical sensing system for classification of mine-like objects by explosives detection

被引:4
|
作者
Chambers, WB [1 ]
Rodacy, PJ [1 ]
Jones, EE [1 ]
Gomez, BJ [1 ]
Woodfin, RL [1 ]
机构
[1] Sandia Natl Labs, Albuquerque, NM 87185 USA
关键词
landmines; mine detection; unexploded ordnance (UXO); ion mobility spectrometer (IMS);
D O I
10.1117/12.324219
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
Sandia National Laboratories has conducted research in chemical sensing and analysis of explosives for many years. Recently, that experience has been directed towards detecting mines and unexploded ordnance (UXO) by sensing the low-level explosive signatures associated with these objects. Our focus has been on the classification of UXO in shallow water and anti-personnel/anti tank mines on land. The objective of this work is to develop a field portable chemical sensing system which can be used to examine mine-like objects (MLO) to determine whether there are explosive molecules associated with the MLO. Two sampling subsystems have been designed, one for water collection and one for soil/vapor sampling. The water sampler utilizes a flow-through chemical adsorbent canister to extract and concentrate the explosive molecules. Explosive molecules are thermally desorbed from the concentrator and trapped in a focusing stage for sapid desorption into an ion-mobility spectrometer (IMS). We will describe a prototype system which consists of a sampler, concentrator-focuser, and detector. The soil sampler employs a light-weight probe for extracting and concentrating explosive vapor from the soil in the vicinity of an MLO. The chemical sensing system is capable of sub-part-per-billion detection of TNT and related explosive munition compounds. We will present the results of field and laboratory tests on buried landmines, which demonstrate our ability to detect the explosive signatures associated with these objects.
引用
收藏
页码:453 / 461
页数:9
相关论文
共 50 条
  • [21] Preliminary assessment of electrical impedance tomography technology to detect mine-like objects
    Wort, PM
    Church, PM
    Gagnon, S
    DETECTION AND REMEDIATION TECHNOLOGIES FOR MINES AND MINELIKE TARGETS IV, PTS 1 AND 2, 1999, 3710 : 895 - 905
  • [22] Identification of metallic mine-like objects using low frequency magnetic fields
    Riggs, LS
    Mooney, JE
    Lawrence, DE
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2001, 39 (01): : 56 - 66
  • [23] Explicit Feature Mapping via Multi-Layer Perceptron and its Application to Mine-Like Objects Detection
    Shao, Hang
    Japkowicz, Nathalie
    PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2014, : 1055 - 1062
  • [24] Superellipse Fitting for the Recovery and Classification of Mine-Like Shapes in Sidescan Sonar Images
    Dura, Esther
    Bell, Judith
    Lane, Dave
    IEEE JOURNAL OF OCEANIC ENGINEERING, 2008, 33 (04) : 434 - 444
  • [25] Distinguishing shape details of buried non-metallic mine-like objects with GPR
    Rappaport, CM
    Wu, S
    Kilmer, M
    Miller, E
    DETECTION AND REMEDIATION TECHNOLOGIES FOR MINES AND MINELIKE TARGETS IV, PTS 1 AND 2, 1999, 3710 : 1419 - 1428
  • [26] Classifying dielectric mine-like objects using the Huynen-Fork polarization parameters
    Sadjadi, Firooz
    Chun, Cornell C. S.
    Sullivan, Anders
    Gaunaurd, Guillermo C.
    2006 IEEE RADAR CONFERENCE, VOLS 1 AND 2, 2006, : 186 - +
  • [27] Robust A*-Search Image Segmentation Algorithm for Mine-like Objects Segmentation in SONAR Images
    Aleksi, Ivan
    Matic, Tomislav
    Lehmann, Benjamin
    Kraus, Dieter
    INTERNATIONAL JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING SYSTEMS, 2020, 11 (02) : 53 - 66
  • [28] Discrimination of bottom underwater mine-like objects in different conditions using a wideband data set
    Robinson, M
    Azimi-Sadjadi, MR
    Jamshidi, AA
    DETECTION AND REMEDIATION TECHNOLOGIES FOR MINES AND MINELIKE TARGETS VII, PTS 1 AND 2, 2002, 4742 : 462 - 473
  • [29] Mine-Like Objects Detection In Side-Scan Sonar Images Using A Shadows-Highlights Geometrical Features Space
    Sinai, Azriel
    Amu, Alon
    Gilboa, Guy
    OCEANS 2016 MTS/IEEE MONTEREY, 2016,
  • [30] Superellipse fitting for the classification of mine-like shapes in side-scan sonar images
    Durá, E
    Bell, JM
    Lane, DM
    OCEANS 2002 MTS/IEEE CONFERENCE & EXHIBITION, VOLS 1-4, CONFERENCE PROCEEDINGS, 2002, : 23 - 28