21 |
An Assessment of the Ability of the U.S. Department of Defense and the Services to Measure and Track Language and Culture Training and Capabilities Among General Purpose Forces
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In: DTIC (2012)
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22 |
Users' Manual and Validation of the Automated Grading System (AGS): Improving the Quality of Intelligence Summaries Using Feedback from an Unsupervised Model of Semantics
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In: DTIC (2012)
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23 |
Conference Report: Cultural and Linguistic Advancement for Mission Success: Enhancing Language, Regional and Cultural Capabilities Across Whole of Government for an Effective COIN Strategy
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In: DTIC (2012)
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24 |
Adding a Capability to Extract Sentiment from Text Using HanDles
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In: DTIC (2012)
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25 |
Capability Challenges in the Human Domain for Intelligence Analysis: Report on Community-Wide Discussions with Canadian Intelligence Professionals
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In: DTIC (2012)
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26 |
Compressed Domain Automatic Level Control Based on ITU-T G.722.2
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In: DTIC (2012)
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27 |
Trends in Human-Computer Interaction to Support Future Intelligence Analysis Capabilities
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In: DTIC (2011)
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28 |
Visual Analytics in Public Safety: Example Capabilities for Example Government Agencies
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In: DTIC (2011)
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29 |
An Intelligence Process Model Based on a Collaborative Approach
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In: DTIC (2011)
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30 |
A Concept Map Knowledge Model of Intelligence Analysis
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In: DTIC (2011)
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31 |
Multiple Detection Probabilistic Data Association Filter for Multistatic Target Tracking
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In: DTIC (2011)
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Abstract:
A standard assumption in most tracking algorithms like the Probabilistic Data Association (PDA) filter, Multiple Hypothesis Tracker (MHT) or the Multiframe Assignment Tracker (MFA), is that a target is detected at most once in a frame of data used for association. This one-to-one assumption is essential for correct measurement-to-track associations. When this assumption is violated, the above algorithms treat the extra detections as random clutter. When multiple detections from the same target fall within the association gate, the PDA filter tries to apportion the association probabilities, but with the fundamental assumption only one of them is correct. The MFA and the MHT algorithms try to spawn multiple tracks to handle the additional measurements from the same target, assuming at most one measurement came from each target. Both of these approaches have undesirable side effects since they ignore the possibility of multiple detections from the same target in a scan of data. Such multiple detection situations occur in multistatic tracking problems. In this paper, we proposed a new Multiple Detection Probabilistic Data Association (MD-PDA) filter for tracking a target when more than one target originated measurement may exist within the validation gate. In the proposed MD-PDA, combinatorial association events are formed to handle the possibility of multiple measurements from the same target. Modified association probabilities are calculated with the explicit assumption of multiple detections. Simulations are presented to demonstrate the effectiveness of the algorithm on a single target tracking problem in clutter. Extensions to handle multiple targets using the Joint PDA, MHT and MFA approaches are under development. ; Presented at the International Conference on Information Fusion (14th) held in Chicago, IL on 5-8 July 2011. Sponsored in part by Office of Naval Research and U.S. Army Research Laboratory.
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Keyword:
*ALGORITHMS; *TARGET DETECTION; *TRACKING; CANADA; COMBINATORIAL ANALYSIS; FOREIGN REPORTS; GATES(CIRCUITS); HYPOTHESES; MEASUREMENT; MULTIFRAME ASSIGNMENT TRACKER; MULTIPLE HYPOTHESIS TRACKER; MULTIPLE TARGETS; Numerical Mathematics; PROBABILISTIC DATA ASSOCIATION; PROBABILITY; Range and Position Finding; Target Direction; VALIDATION
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URL: http://www.dtic.mil/docs/citations/ADA564433 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA564433
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32 |
A Smarter Common Operational Picture: The Application of Abstraction Hierarchies to Naval Command and Control
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In: DTIC (2011)
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33 |
Cognitive Activation Theory of Stress (CATS)
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In: DTIC (2011)
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34 |
Towards a Semiotic Information Position Framework for Network Centric Warfare
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In: DTIC (2011)
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35 |
Semantic Analysis of Military Relevant Texts for Intelligence Purposes
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In: DTIC (2011)
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36 |
Multilingual Content Extraction Extended with Background Knowledge for Military Intelligence
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In: DTIC (2011)
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37 |
Related Entity Finding: University of Waterloo at TREC 2010 Entity Track
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In: DTIC (2010)
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38 |
Speech Understanding in Noise in the Bison Command, Control, Communications and Intelligence (C3I) Mobile Command Post (MCP)
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In: DTIC (2010)
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39 |
Entity Profiling for Intelligence Using the Graphical Overview of Social and Semantic Interactions of People (GOSSIP) Software Tool
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In: DTIC (2010)
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40 |
Issues in Intelligence Production: Summary of Interviews with Canadian Managers of Intelligence Analysts
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In: DTIC (2010)
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