Institut für Detektionstechnologien (IDT)
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A precise characterization of substances is essential for the safe handling of explosives. One parameter regularly characterized is the impact sensitivity. This is typically determined using a drop hammer. However, the results can vary depending on the test method and even the operator, and it is not possible to distinguish the type of decomposition such as detonation and deflagration. This study monitors the reaction progress by constructing a drop hammer to measure the decomposition reaction of four different primary explosives (tetrazene, silver azide, lead azide, lead styphnate) in order to determine the reproducibility of this method. Additionally, further possible evaluation methods are explored to improve on the current binary statistical analysis. To determine whether classification was possible based on extracted features, the responses of equipped sensor arrays, which measure and monitor the reactions, were studied and evaluated. Features were extracted from this data and were evaluated using multivariate methods such as principal component analysis (PCA) and linear discriminant analysis (LDA). The results indicate that although the measurements show substance specific trends, they also show a large scatter for each substance. By reducing the dimensions of the extracted features, different sample clusters can be represented and the calculated loadings allow significant parameters to be determined for classification. The results also suggest that differentiation of different reaction mechanisms is feasible. Testing of the regressor function shows reliable results considering the comparatively small amount of data.
The identification of energetic materials in containments is an important challenge for analytical methods in the field of safety and security. Opening a package without knowledge of its contents and the resulting hazards is highly involved with risks and should be avoided whenever possible. Therefore, preferable methods work non-destructive with minimal interaction and are capable of identifying target substances in a containment quickly and reliably. Most spectroscopic methods find their limits, if the target substance is shielded by a covering material. To solve this problem, a combined laser drilling method with subsequent identification of the target substance by means of Raman spectroscopic measurements through microscopic bore holes of the covering material is presented. A pulsed laser beam is used for both the drilling process and as an excitation source for Raman measurements in the same optical setup. Results show the ability of this new method to gain high-quality spectra even when performed through microscopic small bore channels. With the laser parameters chosen right, the method can even be performed on highly sensitive explosives like triacetone triperoxide (TATP). Another advantageous effect arises in an observed reduction in unwanted fluorescence signal in the spectral data, resulting from the confocal-like measurement setup with the bore hole acting as aperture.
Explorative experiments were done to figure out differences in the emission of volatile organic compounds (VOCs) of not infested trees and trees infested by Anoplophora glabripennis (Asian longhorn beetle, ALB), a quarantine pest. Therefore, VOCs from some native insect species, Anoplophora glabripennis infested Acer, stressed Acer, healthy Acer, Populus and Salix were obtained by enrichment on adsorbents. Qualitative analysis was done by thermal desorption gas chromatography coupled with a mass selective detector (TD-GC/MS). Altogether 169 substances were identified. 11 substances occur from ALB infested or mechanically damaged trees i.e. stressed trees, but not from healthy trees. (+)-Cyclosativene, (+)-α-longipinene, copaene and caryophyllene are detectable only from ALB-infested Acer not from mechanically damaged or healthy Acer. However, these substances are also emitted by healthy Salix. 2,4-Dimethyl-1-heptene is among all tree samples exclusively present in the ambience of ALB-infested trees. It´s rarely detectable from native insect species’ samples.
Asymmetric threats require powerful surveillance technology which helps to preserve the security. Security checks which focus on Improvised Explosive Devices (IED’s) or the identification of persons carrying hazardous substances are the major task of our research within the HAMLeT+ (Hazardous Material Localization and Person Tracking) project. Further on, there is a pressing need for assisting the security personnel, either civil or military, by extending the detection capabilities and to deliver efficient and reliable, real time decision support for their task to percept threats. Military camp protection with heterogeneous net-worked sensors and comprehensive sensor data fusion could be such an element. The technology developments concentrate on the integration of different sensor types (video, tracking sensors, CBRNE sensors) in order to get a better and comprehensive understanding in a defined entry area. Data fusion is used to combine kinematic data of persons (where, when) with additional attribute information of them (what) in order to identify that single person carrying the attributes and to classify the threat. The project was initiated as a Supporting Activity funded by the EU within the PASR 2006 scheme. With regards to the specific task for military camp protection it was extended and redesigned. In HAMLeT+ several chemical sensors for hydrocarbons like fuels, alcohols or solvents were used. Such chemicals are available in bigger amounts on the free market. Using them e.g. as fire accelerants they can cause a huge damage. Therefore their detection or the detection of persons carrying such substances or having contaminations on their clothes is of great interest. Sensitive devices for the detection of these analytes are e.g. metal oxide sensors [1]. Our presentation illustrates experimental data, which were gathered with the experimental system HAMLeT+ during the NATO “Defense Against Terrorism (DAT)” campaign „COMMON SHIELDS” in August and September 2008.
We introduce our Lessons Learned from the NATO CNAD PoW “Defense Against Terrorism (DAT)” campaign „COMMON SHIELD” from August and September 2008, present our data and illustrate our experience, which were gathered with the experimental system HAMLeT+ (Hazardous Material Localization and Person Tracking Plus) for military camp protection. The focus of „COMMON SHIELD” was the network-centric operation and demon-stration of innovative technologies for Intelligence, Surveillance, Reconnaissance and Target Acquisition of Terrorists (ISRTA). With regard to the specific task for military camp protection, the original demonstrator HAMLeT [1], which was initiated as a Supporting Activity funded by the EU within the PASR 2006 scheme, was extended and redesigned as HAMLeT+. In HAMLeT+ several chemical sensors for hydrocarbons like fuels, alcohols or solvents were used. The identification of persons carrying hazardous substances and the classification of those substances are the major task of our research. Further on, there is a pressing need for assistance systems for the guards, to extend the spectra of detection capabilities and to receive efficient and reliable, real time decision support for the task to percept threats, which so far could not even be realized at an entry control facility. Security assistance by means of heterogeneous net-worked sensors and comprehensive sensor data fusion could be such an element for better protection. New technological developments concentrate on the integration of different sensor types (video, tracking sensors, CRE sensors) in order to get a better and comprehensive understanding of potential threats in a defined area. Multiple sensors data fusion can be used to combine complementary types of data e.g. kinematic data of objects (where, when) with additional attribute information (what) in order to identify those objects carrying the attributes of interest and give a classification of the potential threat.
Timely recognition of threats can be significantly supported by security assistance systems that work continuously in time and call the security personnel in case of anomalous events in the surveillance area. We describe the concept and the realization of an indoor security assistance system for real-time decision support. The system consists of a computer vision module and a person classification module. The computer vision module provides a video event analysis of the entrance region in front of the demonstrator. After entering the control corridor, the persons are tracked, classified, and potential threats are localized inside the demonstrator. Data for the person classification are provided by chemical sensors detecting hazardous materials. Due to their limited spatio-temporal resolution, a single chemical sensor cannot localize this material and associate it with a person. We compensate this deficiency by fusing the output of multiple, distributed chemical sensors with kinematical data from laser-range scanners. Considering both the computer vision formation and the results of the person classification affords the localization of threats and a timely reaction of the security personnel.