TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Lorenz, F. P.
A1  - Safenreiter, K.
A1  - Varela, M.
A1  - Wieneke, M.
A1  - Kaul, P.
A1  - Maurer, S.
A1  - Warmer, J.
T1  - NATO CNAD PoW Defence Against Terrorism: Experimental Results and Lessons Learned from COMMON SHIELD 08
T2  - Elsner (Ed.): Fraunhofer Symposium Future Security. 4th Security Research Conference, September 29th - October 1st 2009, Karlsruhe, Germany.
N2  - 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.
Y1  - 2009
SN  - 978-3-8396-0051-1
SB  - 978-3-8396-0051-1
SP  - 501
EP  - 509
ER  -