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检索条件"任意字段=Multisensor. Multisource Information Fusion: Architectures, Algorithms, and Applications 2003"

共 284 条记录，以下是51-60 订阅

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Analysis of Decision fusion algorithms in Handling Uncertainties For Integrated Health Monitoring Systems

Analysis of Decision Fusion Algorithms in Handling Uncertain...

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Conference on multisensor. multisource information fusion - architectures, algorithms, and applications

作者： Zein-Sabatto, Saleh Mikhail, Maged Bodruzzaman, Mohammad DeSimio, Martin Derriso, Mark Behbahani, Alireza Tennessee State Univ Coll Engn Nashville TN 37209 USA UDRI Dayton OH USA Air Force Res Lab Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9780819490858

It has been widely accepted that data fusion and information fusion methods can improve the accuracy and robustness of decision-making in structural health monitoring systems. It is arguably true nonetheless, that decision-level is equally beneficial when applied to integrated health monitoring systems. Several decisions at low-levels of abstraction may be produced by different decision-makers;however, decision-level fusion is required at the final stage of the process to provide accurate assessment about the health of the monitored system as a whole. An example of such integrated systems with complex decision-making scenarios is the integrated health monitoring of aircraft. Thorough understanding of the characteristics of the decision-fusion methodologies is a crucial step for successful implementation of such decision-fusion systems. In this paper, we have presented the major information fusion methodologies reported in the literature, i.e., probabilistic, evidential, and artificial intelligent based methods. The theoretical basis and characteristics of these methodologies are explained and their performances are analyzed. Second, candidate methods from the above fusion methodologies, i.e., Bayesian, Dempster-Shafer, and fuzzy logic algorithms are selected and their applications are extended to decisions fusion. Finally, fusion algorithms are developed based on the selected fusion methods and their performance are tested on decisions generated from synthetic data and from experimental data. Also in this paper, a modeling methodology, i.e. cloud model, for generating synthetic decisions is presented and used. Using the cloud model, both types of uncertainties;randomness and fuzziness, involved in real decision-making are modeled. Synthetic decisions are generated with an unbiased process and varying interaction complexities among decisions to provide for fair performance comparison of the selected decision-fusion algorithms. For verification purposes, implementatio

关键词： Decision fusion Decision Modeling Theory of Evidence Decision Uncertainties Cloud Theory

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Navigation of uncertain terrain by fusion of information from real and synthetic imagery

Navigation of uncertain terrain by fusion of information fro...

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Conference on multisensor. multisource information fusion - architectures, algorithms, and applications

作者： Lyons, Damian M. Nirmal, Paramesh Benjamin, D. Paul Fordham Univ Robot & Comp Vis Lab Bronx NY 10458 USA Pace Univ Dept Comp Sci New York NY 10023 USA

ISBN: (纸本)9780819490858

We consider the scenario where an autonomous platform that is searching or traversing a building may observe unstable masonry or may need to travel over unstable rubble. A purely behaviour-based system may handle these challenges but produce behaviour that works against long-terms goals such as reaching a victim as quickly as possible. We extend our work on ADAPT, a cognitive robotics architecture that incorporates 3D simulation and image fusion, to allow the robot to predict the behaviour of physical phenomena, such as falling masonry, and take actions consonant with long-term goals. We experimentally evaluate a cognitive only and reactive only approach to traversing a building filled with various numbers of challenges and compare their performance. The reactive only approach succeeds only 38% of the time, while the cognitive only approach succeeds 100% of the time. While the cognitive only approach produces very impressive behaviour, our results indicate how much better the combination of cognitive and behaviour-based can be.

关键词： cognitive robotics navigation sensory fusion

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multisensor. multisource information fusion: architectures, algorithms, and applications 2011

Multisensor, Multisource Information Fusion: Architectures, ...

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multisensor. multisource information fusion: architectures, algorithms, and applications 2011

ISBN: (纸本)9780819486387

The proceedings contain 20 papers. The topics discussed include: image fusion for remote sensing using fast, large-scale neuroscience models;the continuum fusion theory of signal detection applied to a bi-modal fusion problem;feature aided Monte Carlo probabilistic data association filter for ballistic missile tracking;architectures, algorithms, and applications using Bayesian networks;fusion of hyperspectral and ladar data for autonomous target detection;a relaxed fusion of information from real and synthetic images to predict complex behavior;the perception problem and the impact on robotics and computer vision;a hidden Markov model for multimodal biometrics score fusion;multi sensor remote sensing information fusion for urban area classification and change detection;and fusion of chemical, biological, and meteorological observations for agent source term estimation and hazard refinement.

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Spectral and Spatial Algorithm Architecture for Classification of Hyperspectral and LIDAR

Spectral and Spatial Algorithm Architecture for Classificati...

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Conference on multisensor. multisource information fusion - architectures, algorithms, and applications

作者： Rand, Robert S. Khuon, Timothy S. Natl Geospatial Intelligence Agcy Off Innovis Bas & Appl Res Springfield VA 22153 USA

ISBN: (纸本)9780819490858

Architecture for neural net multi-sensor data fusion is introduced and analyzed. This architecture consists of a set of independent sensor neural nets, one for each sensor, coupled to a fusion net. The neural net of each sensor is trained (from a representative data set of the particular sensor) to map to a hypothesis space output. The decision outputs from the sensor nets are used to train the fusion net to an overall decision. To begin the processing, the 3D point cloud LIDAR data is classified based on a multi-dimensional mean-shift segmentation and classification into clustered objects. Similarly, the multi-band HSI data is spectrally classified by the Stochastic Expectation-Maximization (SEM) into a classification map containing pixel classes. For sensor fusion, spatial detections and spectral detections complement each other. They are fused into final detections by a cascaded neural network, which consists of two levels of neural nets. The first layer is the sensor level consisting of two neural nets: spatial neural net and spectral neural net. The second level consists of a single neural net, that is the fusion neural net. The success of the system in utilizing sensor synergism for an enhanced classification is clearly demonstrated by applying this architecture for classifying on November 2010 airborne data collection of LIDAR and HSI over the Gulfport, MS, area.

关键词： Sensor fusion Architecture Neural Net HSI LIDAR Classification Mean-shift Stochastic Expectation-Maximization Imagery Back-propagation Perceptron Probability of detection and false alarm

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Colour-the-INSight: combining a direct view rifle sight with fused intensified and thermal imagery

Colour-the-INSight: combining a direct view rifle sight with...

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Conference on multisensor. multisource information fusion - architectures, algorithms, and applications

作者： Hogervorst, M. A. Jansen, C. Toet, A. Bijl, P. Bakker, P. Hiddema, A. C. van Vliet, S. F. TNO Human Factors POB 23 NL-3769 GZ Soesterberg Netherlands Thales Netherland BV Huizen Netherlands Photonis Netherlands BV NL-9300 AB Groningen Netherlands Minist Def Joint Soldier & Equipment Ctr Expertise The Hague Netherlands

ISBN: (纸本)9780819490858

We present the design and evaluation of a new demonstrator rifle sight viewing system containing direct view, red aim point and fusion of an (uncooled, LWIR) thermal sensor with a digital image intensifier. Our goal is to create a system that performs well under a wide variety of (weather) conditions during daytime and nighttime and combines the advantages of the various sensor systems. A real-time colour image with salient hot targets is obtained from the night vision sensors by implementing the Colour-the-Night fusion method (Hogervorst & Toet, 2010) on the on-board processor. The prototype system was evaluated in a series of field trials with military observers performing detection and identification tasks. The tests showed that during daytime the addition of a thermal image to direct vision is advantageous, e. g. for the detection of hot targets. At nighttime, the fusion of thermal and image intensified imagery results in increased situational awareness and improved detection of (hot) targets. For identification of small (handheld) objects, the technology needs to be further refined

关键词： image fusion false colour natural colour mapping real-time fusion night vision

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Real-Time Threat Assessment for Critical Infrastructure Protection: Data Incest and Conflict in Evidential Reasoning

Real-Time Threat Assessment for Critical Infrastructure Prot...

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Conference on multisensor. multisource information fusion - architectures, algorithms, and applications

作者： Brandon, R. Page, S. Varndell, J. Digital Barriers Guildford Guildford GU2 9JX Surrey England

ISBN: (纸本)9780819490858

This paper presents a novel application of Evidential Reasoning to Threat Assessment for critical infrastructure protection. A fusion algorithm based on the PCR5 Dezert-Smarandache fusion rule is proposed which fuses alerts generated by a vision-based behaviour analysis algorithm and a-priori watch-list intelligence data. The fusion algorithm produces a prioritised event list according to a user-defined set of event-type severity or priority weightings. Results generated from application of the algorithm to real data and Behaviour Analysis alerts captured at London's Heathrow Airport under the EU FP7 SAMURAI programme are presented. A web-based demonstrator system is also described which implements the fusion process in real-time. It is shown that this system significantly reduces the data deluge problem, and directs the user's attention to the most pertinent alerts, enhancing their Situational Awareness (SA). The end-user is also able to alter the perceived importance of different event types in real-time, allowing the system to adapt rapidly to changes in priorities as the situation evolves. One of the key challenges associated with fusing information deriving from intelligence data is the issue of Data Incest. Techniques for handling Data Incest within Evidential Reasoning frameworks are proposed, and comparisons are drawn with respect to Data Incest management techniques that are commonly employed within Bayesian fusion frameworks (e. g. Covariance Intersection). The challenges associated with simultaneously dealing with conflicting information and Data Incest in Evidential Reasoning frameworks are also discussed.

关键词： Dempster Shafer Evidential Reasoning Data Deluge Data fusion Data Incest Data Conflict

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

Human cognitive and perceptual factors in JDL level 4 hard/s...

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Conference on multisensor. multisource information fusion - architectures, algorithms, and applications

作者： Rimland, Jeffrey C. Hall, David L. Graham, Jacob L. Penn State Univ Coll Informat Sci & Technol University Pk PA 16802 USA

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.

关键词： data fusion soft data fusion participatory sensing tasking cognition perception human factors

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Advances in data representation for hard/soft information fusion

Advances in data representation for hard/soft information fu...

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Conference on multisensor. multisource information fusion - architectures, algorithms, and applications

作者： Rimland, Jeffrey C. Coughlin, Dan Hall, David L. Graham, Jacob L. Penn State Univ Coll IST 101IST Bldg University Pk PA 16802 USA Penn State Univ Paterno Lib University Pk PA 16802 USA

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. Thi

关键词： hard and soft information fusion hybrid cognition service oriented architecture SOA ESB

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Allothetic and Idiothetic Sensor fusion in Rat-inspired Robot Localization

Allothetic and Idiothetic Sensor Fusion in Rat-inspired Robo...

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Conference on multisensor. multisource information fusion - architectures, algorithms, and applications

作者： Weitzenfeld, Alfredo Fellous, Jean-Marc Barrera, Alejandra Tejera, Gonzalo Univ South Florida Polytech Div Informat Technol 3433 Winter Lake Rd Lakeland FL 33803 USA Univ Arizona Dept Psychol & Applied Mat Tucson AZ 85721 USA Dept Comp Engn Inst Technol Autonomo Mexico Mexico City 01080 DF Mexico Univ Republica Inst Comp Montevideo 11300 Uruguay

ISBN: (纸本)9780819490858

We describe a spatial cognition model based on the rat's brain neurophysiology as a basis for new robotic navigation architectures. The model integrates allothetic (external visual landmarks) and idiothetic (internal kinesthetic information) cues to train either rat or robot to learn a path enabling it to reach a goal from multiple starting positions. It stands in contrast to most robotic architectures based on SLAM, where a map of the environment is built to provide probabilistic localization information computed from robot odometry and landmark perception. Allothetic cues suffer in general from perceptual ambiguity when trying to distinguish between places with equivalent visual patterns, while idiothetic cues suffer from imprecise motions and limited memory recalls. We experiment with both types of cues in different maze configurations by training rats and robots to find the goal starting from a fixed location, and then testing them to reach the same target from new starting locations. We show that the robot, after having pre-explored a maze, can find a goal with improved efficiency, and is able to (1) learn the correct route to reach the goal, (2) recognize places already visited, and (3) exploit allothetic and idiothetic cues to improve on its performance. We finally contrast our biologically-inspired approach to more traditional robotic approaches and discuss current work in progress.

关键词： robotics biologically-inspired spatial cognition navigation localization SLAM

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multisource Causal Data Mining

Multisource Causal Data Mining

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Conference on multisensor. multisource information fusion - architectures, algorithms, and applications

作者： Woodley, Robert Gosnell, Michael Shallenberger, Kevin 21st Century Syst Inc 6825 Pine StSuite 141Box 10 Omaha NE 68106 USA

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 data-mining 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.

关键词： Causal data mining Causal data relationships Mahalanobis Taguchi System Evidence Reasoning information fusion Knowledge based systems Intelligent agent software Data mining and clustering

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