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检索条件"任意字段=Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2012"

共 282 条记录，以下是121-130 订阅

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Field-based data collection techniques for the evaluation of information fusion systems

Field-based data collection techniques for the evaluation of...

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multisensor, multisource information fusion: architectures, algorithms, and applications 2009

作者： Antonishek, Brian Weiss, Brian A. Schlenoff, Craig National Institute of Standards and Technology 100 Bureau Drive Gaithersburg MD 20899 United States

ISBN: (纸本)9780819476111

In order to effectively evaluate information fusion systems or emerging technologies, it is critical to quickly, efficient, and accurately collect functional and observational data about such systems. One of the best ways to test a system's capabilities is to have an end user operate it in controlled but realistic field-based situations. Evaluation data of the systems' performance as well as observational data of the user's interactions can then be collected and analyzed. This analysis often gives insight into how the system may perform in the intended environment and of any potential areas for improvement. One common method for collection of this data involves an evaluator/observer generating hand-written notes, comments, and sketches. This often proves to be inefficient in complex sensor technology field-based evaluation environments. Personnel at the National Institute of Standards and Technology (NIST) have been tasked with collecting such evaluation data for emerging soldier-worn sensor systems. Lessons learned from the on-going development of efficient field-based evaluation data collection techniques will be discussed. The most recent evaluation data collection using a personal digital assistant (PDA)-style system and details of its use during an evaluation of a multi-team study will also be described. © 2009 SPIE.

关键词： Personal digital assistants

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A local approach for focussed Bayesian fusion

A local approach for focussed Bayesian fusion

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multisensor, multisource information fusion: architectures, algorithms, and applications 2009

作者： Sander, Jennifer Heizmann, Michael Goussev, Igor Beyerera, Jürgen Lehrstuhl für Interaktive Echtzeitsysteme Adenauerring 4 D-76131 Karlsruhe Germany Fraunhofer-Institut für Informations- und Datenverarbeitung IITB Fraunhoferstraße 1 D-76131 Karlsruhe Germany

ISBN: (纸本)9780819476111

Local Bayesian fusion approaches aim to reduce high storage and computational costs of Bayesian fusion which is separated from fixed modeling assumptions. Using the small world formalism, we argue why this proceeding is conform with Bayesian theory. Then, we concentrate on the realization of local Bayesian fusion by focussing the fusion process solely on local regions that are task relevant with a high probability. The resulting local models correspond then to restricted versions of the original one. In a previous publication, we used bounds for the probability of misleading evidence to show the validity of the pre-evaluation of task specific knowledge and prior information which we perform to build local models. In this paper, we prove the validity of this proceeding using information theoretic arguments. For additional efficiency, local Bayesian fusion can be realized in a distributed manner. Here, several local Bayesian fusion tasks are evaluated and unified after the actual fusion process. For the practical realization of distributed local Bayesian fusion, software agents are predestinated. There is a natural analogy between the resulting agent based architecture and criminal investigations in real life. We show how this analogy can be used to improve the efficiency of distributed local Bayesian fusion additionally. Using a landscape model, we present an experimental study of distributed local Bayesian fusion in the field of reconnaissance, which highlights its high potential. © 2009 SPIE.

关键词： Efficiency

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Data fusion engine (DFE) for the force protection joint experiment (FPJE)

Data fusion engine (DFE) for the force protection joint expe...

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multisensor, multisource information fusion: architectures, algorithms, and applications 2009

作者： Barngrover, C.M. Laird, R.T. Kramer, T.A. Cruickshanks, J.R. Cutler, S.H. 53560 Hull Street San Diego CA 92152 United States

ISBN: (纸本)9780819476111

The FPJE was an experiment to consider the best way to create a system of systems in the realm of Force Protection. It was sponsored by Physical Security Equipment Action Group (PSEAG) and Joint Program Manager - Guardian (JPMG), and was managed by the Product Manager - Force Protection Systems (PM-FPS). The experiment attempted to understand the challenges associated with integrating disparate systems into a cohesive unit, and then the compounding challenge of handling the flow of data into the system and its dispersion to all subscribed Command and Control (C2) nodes. To handle this data flow we created the DFE based on the framework of the Joint Battlespace Command and Control System for Manned and Unmanned Assets (JBC2S). The DFE is a data server that receives information from the network of systems via the Security Equipment Integration Working Group (SEIWG) ICD-0100 protocol, processes the data through static fusion algorithms, and then publishes the fused data to the C2 nodes, in this case JBC2S and the Tactical Automated Security System (TASS). The DFE uses only known concepts and algorithms for its fusion efforts. This paper discusses the analyzed impact of the fusion on C2 nodes displays and in turn on the operators. Also, this paper discusses the lessons learned about networked control combined with DFE generated automatic response. Finally, this paper discusses possible future efforts and their benefits for providing the useful operational picture to the operator. © 2009 SPIE.

关键词： Command and control systems

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Soft adaptive fusion of sensor energy for large-scale sensor networks (SAFE)

Soft adaptive fusion of sensor energy for large-scale sensor...

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multisensor, multisource information fusion: architectures, algorithms, and applications 2009

作者： Rababaah, Haroun Shirkhodaie, Amir Center of Excellence for Battlefield Sensor Fusion Department of Mechanical and Manufacturing Engineering Tennessee State University 3500 John Merritt Blvd. Nashville TN 37209 United States

ISBN: (纸本)9780819476111

Target tracking for network surveillance systems has gained significant interest especially in sensitive areas such as homeland security, battlefield intelligence, and facility surveillance. Most of the current sensor network protocols do not address the need for multi-sensor fusion-based target tracking schemes, which is crucial for the longevity of the sensor network. In this paper, we present an efficient fusion model for target tracking in a cluster-based large sensor networks. This new scheme is inspired by the image processing techniques by perceiving a sensor network as an energy map of sensor stimuli and applying typical image processing techniques on this map such as: filtering, convolution, clustering, segmentation, etc to achieve high-level perceptions and understanding of the situation. The new fusion model is called Soft Adaptive fusion of Sensor Energies (SAFE). SAFE performs soft fusion of the energies collected by a local region of sensors in a large-scale sensor network. This local fusion is then transmitted by the head node to a base-station to update the common operation picture with evolving events of interest. Simulated scenarios showed that SAFE is promising by demonstrating a significant improvement in target tracking reliability, uncertainty, and efficiency. © 2009 SPIE.

关键词： Sensor networks

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Energy logic (EL): A Novel fusion engine of multi-modality multi-agent data/information fusion for intelligent surveillance systems

Energy logic (EL): A Novel fusion engine of multi-modality m...

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multisensor, multisource information fusion: architectures, algorithms, and applications 2009

作者： Rababaah, Haroun Shirkhodaie, Amir Center of Excellence for Battlefield Sensor Fusion Dept. of Mechanical and Manufacturing Engineering Tennessee State University 3500 John Merritt Blvd. Nashville TN 37209 United States

ISBN: (纸本)9780819476111

The rapidly advancing hardware technology, smart sensors and sensor networks are advancing environment sensing. One major potential of this technology is Large-Scale Surveillance Systems (LS3) especially for, homeland security, battlefield intelligence, facility guarding and other civilian applications. The efficient and effective deployment of LS3 requires addressing number of aspects impacting the scalability of such systems. The scalability factors are related to: computation and memory utilization efficiency, communication bandwidth utilization, network topology (e.g., centralized, ad-hoc, hierarchical or hybrid), network communication protocol and data routing schemes;and local and global data/information fusion scheme for situational awareness. Although, many models have been proposed to address one aspect or another of these issues but, few have addressed the need for a multi-modality multi-agent data/information fusion that has characteristics satisfying the requirements of current and future intelligent sensors and sensor networks. In this paper, we have presented a novel scalable fusion engine for multi-modality multi-agent information fusion for LS3. The new fusion engine is based on a concept we call: Energy Logic. Experimental results of this work as compared to a Fuzzy logic model strongly supported the validity of the new model and inspired future directions for different levels of fusion and different applications. © 2009 SPIE.

关键词： Scalability

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information abstraction for enhanced image fusion based surveillance systems

Information abstraction for enhanced image fusion based surv...

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multisensor, multisource information fusion: architectures, algorithms, and applications 2009

作者： Page, Scott F. Smith, Moira I. Hickman, Duncan Kimber, Paul K. Waterfall Solutions Ltd Parklands Guildford Surrey GU2 9JX United Kingdom SELEX Galileo Ltd Sigma House Christopher Martin Road Basildon Essex SS14 3EL United Kingdom

ISBN: (纸本)9780819476111

The benefits of image fusion for man-in-the-loop Detection, Recognition, and Identification (DRI) tasks are well known. However, the performance of conventional image fusion systems is typically sub-optimal, as they fail to capitalise on high-level information which can be abstracted from the imagery. As part of a larger study into an Intelligent Image fusion (I2F) framework, this paper presents a novel approach which exploits high-level cues to adaptively enhance the fused image via feedback to the pixel-level processing. Two scenarios are chosen for illustrative application of the approach, Situational Awareness and Anomalous Object Detection (AOD). In the Situational Awareness scenario, motion and other cues are used to enhance areas of the image according to predefined tasks, such as the detection of moving targets of a certain size. This yields a large increase in Local Signal-to-Clutter Ratio (LSCR) when compared to a baseline, non-adaptive approach. In the AOD scenario, spatial and spectral information is used to direct a foveal-patch image fusion algorithm. This demonstrates a significant increase in the Probability of Detection on test imagery whilst simultaneously reducing the mean number of false alarms when compared to a baseline, non-foveal approach. This paper presents the rationale for the I2F approach and details two specific examples of how it can be applied to address very different applications. Design details and quantitative performance analysis results are reported. © 2009 SPIE.

关键词： Image fusion

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Biologically-inspired robust and adaptive multi-sensor fusion and active control

Biologically-inspired robust and adaptive multi-sensor fusio...

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multisensor, multisource information fusion: architectures, algorithms, and applications 2009

作者： Khosla, Deepak Dow, Paul A. Huber, David J. HRL Laboratories LLC Malibu CA United States

ISBN: (纸本)9780819476111

In this paper, we describe a method and system for robust and efficient goal-oriented active control of a machine (e.g., robot) based on processing, hierarchical spatial understanding, representation and memory of multimodal sensory inputs. This work assumes that a high-level plan or goal is known a priori or is provided by an operator interface, which translates into an overall perceptual processing strategy for the machine. Its analogy to the human brain is the download of plans and decisions from the pre-frontal cortex into various perceptual working memories as a perceptual plan that then guides the sensory data collection and processing. For example, a goal might be to look for specific colored objects in a scene while also looking for specific sound sources. This paper combines three key ideas and methods into a single closed-loop active control system. (1) Use high-level plan or goal to determine and prioritize spatial locations or waypoints (targets) in multimodal sensory space;(2) collect/store information about these spatial locations at the appropriate hierarchy and representation in a spatial working memory. This includes invariant learning of these spatial representations and how to convert between them;and (3) execute actions based on ordered retrieval of these spatial locations from hierarchical spatial working memory and using the "right" level of representation that can efficiently translate into motor actions. In its most specific form, the active control is described for a vision system (such as a pantilt-zoom camera system mounted on a robotic head and neck unit) which finds and then fixates on high saliency visual objects. We also describe the approach where the goal is to turn towards and sequentially foveate on salient multimodal cues that include both visual and auditory inputs. © 2009 SPIE.

关键词： Data handling

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multisensor information compression and reconstruction

Multisensor information compression and reconstruction

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multisensor, multisource information fusion: architectures, algorithms, and applications 2009

作者： Bing, Du Liang, Liu Jun, Zhang School of Electronics and Information Engineering Beihang Univ. XueYuan Road 37 Haidian District Beijing 100191 China

ISBN: (纸本)9780819476111

In this paper, we propose a method of sampled data compression and reconstruction using the theory of distributed compressed sensing for wireless sensor network, in which the correlation between the sensors is considered for joint sparsity representation, compression and reconstruction of the signals. And incoherent random projection CS matrix in each sensor is as encoding matrix to generate compressed measurements for storing, delivering and processing. The reconstruction algorithm with both acceptable complexity and precision is developed for noise corrupted measurements by fully utilizing of correlations diversity. The simulation shows that the number of measurements only slightly larger than the sparsity of the sampled sensor data is needed for successful recovery. © 2009 SPIE.

关键词： Wireless sensor networks

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Proceedings of SPIE - multisensor, multisource information fusion: architectures, algorithms, and applications 2007

Proceedings of SPIE - Multisensor, Multisource Information F...

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multisensor, multisource information fusion: architectures, algorithms, and applications 2007

ISBN: (纸本)081946693X

The proceedings contain 27 papers. The topics discussed include: a novel method to evaluate the performance of pan-sharpening algorithms;signal-to-noise ratio for cross-sensor fusion approach;fusion and kernel type selection in adaptive image retrieval;merging infrared and color visible images with a contrast enhanced fusion method;multi-sensor detection and fusion technique;novel classification fusion techniques for military target classification;a full-scale prototype multisensor system for fire detection and situational awareness;fusion of disparate information sources in a hybrid decision-support architecture;selection of fusion operations using rank-score diversity for robot mapping and localization;maximum likelihood ensemble filter applied to multisensory systems;assessing the value of information in a fuzzy cognitive map;and multisensor fusion of images for target identification.

关键词： information fusion

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

Multisensor, Multisource Information Fusion: Architectures, ...

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2007年

作者： Belur V Dasarathy

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