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Human cognitive and perceptual factors in JDL level 4 hard / soft data fusion

Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Rimland, Jeffrey C. Hall, David L. Graham, Jacob L. Pennsylvania State University College of Information Sciences and Technology 101 IST Building University Park PA 16802-6822 United States

ISBN: (纸本)9780819490858

Utilization of human participants as "soft sensors" is becoming increasingly important for gathering information related to a wide range of phenomena including natural and man-made disasters, environmental changes over time, crime prevention, and other roles of the "citizen scientist." The ubiquity of advanced mobile devices is facilitating the role of humans as "hybrid sensor platforms", allowing them to gather data (e.g. video, still images, GPS coordinates), annotate it based on their intuitive human understanding, and upload it using existing infrastructure and social networks. However, this new paradigm presents many challenges related to source characterization, effective tasking, and utilization of massive quantities of physical sensor, human-based, and hybrid hard/soft data in a manner that facilitates decision making instead of simply amplifying information overload. In the Joint Directors of Laboratories (JDL) data fusion process model, "level 4" fusion is a meta-process that attempts to improve performance of the entire fusion system through effective source utilization. While there are well-defined approaches for tasking and categorizing physical sensors, these methods fall short when attempting to effectively utilize a hybrid group of physical sensors and human observers. While physical sensor characterization can rely on statistical models of performance (e.g. accuracy, reliability, specificity, etc.) under given conditions, "soft" sensors add the additional challenges of characterizing human performance, tasking without inducing bias, and effectively balancing strengths and weaknesses of both human and physical sensors. This paper addresses the challenges of the evolving human-centric fusion paradigm and presents cognitive, perceptual, and other human factors that help to understand, categorize, and augment the roles and capabilities of humans as observers in hybrid systems. © 2012 SPIE.

关键词： Data fusion

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multisource causal data mining

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Woodley, Robert Gosnell, Michael Shallenberger, Kevin 21st Century Systems Inc. 6825 Pine Street Suite 141 Box 10 Omaha NE 68106-2857 United States

ISBN: (纸本)9780819490858

Analysts are faced with mountains of data, and finding that relevant piece of information is the proverbial needle in a haystack, only with dozens of haystacks. Analysis tools that facilitate identifying causal relationships across multiple data sets are sorely needed. 21st Century Systems, Inc. (21CSi) has initiated research called Causal-View, a causal datamining visualization tool, to address this challenge. Causal-View is built on an agent-enabled framework. Much of the processing that Causal-View will do is in the background. When a user requests information, Data Extraction Agents launch to gather information. This initial search is a raw, Monte Carlo type search designed to gather everything available that may have relevance to an individual, location, associations, and more. This data is then processed by Data-Mining Agents. The Data-Mining Agents are driven by user supplied feature parameters. If the analyst is looking to see if the individual frequents a known haven for insurgents he may request information on his last known locations. Or, if the analyst is trying to see if there is a pattern in the individual's contacts, the mining agent can be instructed with the type and relevance of the information fields to look at. The same data is extracted from the database, but the Data Mining Agents customize the feature set to determine causal relationships the user is interested in. At this point, a Hypothesis Generation and Data Reasoning Agents take over to form conditional hypotheses about the data and pare the data, respectively. The newly formed information is then published to the agent communication backbone of Causal-View to be displayed. Causal-View provides causal analysis tools to fill the gaps in the causal chain. We present here the Causal-View concept, the initial research into data mining tools that assist in forming the causal relationships, and our initial findings. © 2012 SPIE.

关键词： Data mining

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Advances in data representation for hard/soft information fusion

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Rimland, Jeffrey C. Coughlin, Dan Hall, David L. Graham, Jacob L. Pennsylvania State University College of IST 101IST Building University Park PA 16802-6822 United States Pennsylvania State University OE03 Paterno Library University Park PA 16802 United States

ISBN: (纸本)9780819490858

information fusion is becoming increasingly human-centric. While past systems typically relegated humans to the role of analyzing a finished fusion product, current systems are exploring the role of humans as integral elements in a modular and extensible distributed framework where many tasks can be accomplished by either human or machine performers. For example, "participatory sensing" campaigns give humans the role of "soft sensors" by uploading their direct observations or as "soft sensor platforms" by using mobile devices to record human-annotated, GPS-encoded high quality photographs, video, or audio. Additionally, the role of "human-in-the-loop", in which individuals or teams using advanced human computer interface (HCI) tools such as stereoscopic 3D visualization, haptic interfaces, or aural "sonification" interfaces can help to effectively engage the innate human capability to perform pattern matching, anomaly identification, and semantic-based contextual reasoning to interpret an evolving situation. The Pennsylvania State University is participating in a Multi-disciplinary University Research Initiative (MURI) program funded by the U.S. Army Research Office to investigate fusion of hard and soft data in counterinsurgency (COIN) situations. In addition to the importance of this research for Intelligence Preparation of the Battlefield (IPB), many of the same challenges and techniques apply to health and medical informatics, crisis management, crowd-sourced "citizen science", and monitoring environmental concerns. One of the key challenges that we have encountered is the development of data formats, protocols, and methodologies to establish an information architecture and framework for the effective capture, representation, transmission, and storage of the vastly heterogeneous data and accompanying metadata -- including capabilities and characteristics of human observers, uncertainty of human observations, "soft" contextual data, and information pedigree. This

关键词： Service oriented architecture (SOA)

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多传感器和多源信息的融合：结构、算法和应用（SPIEV01．5813）

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红外 2007年第5期28卷 47-48页

作者：顾聚兴（译）

一、图像融合1．利用主成分分析对压缩数据投影限幅图像进行融合（***，美国科学应用国际公司）2．实时执行图像的配准和融合（***，英国Octec公司）

一、图像融合 1．利用主成分分析对压缩数据投影限幅图像进行融合（***，美国科学应用国际公司） 2．实时执行图像的配准和融合（***，英国Octec公司）

关键词：科学应用多源信息多传感器结构算法图像融合主成分分析 tec公司

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