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检索条件"任意字段=Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2014"

共 283 条记录，以下是151-160 订阅

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A heterogeneous sensor network simulation system with integrated terrain data for real-time target detection in 3D space

A heterogeneous sensor network simulation system with integr...

引用

Conference on multisensor, multisource information fusion

作者： Lin, Hong Tanner, Steve Rushing, John Graves, Sara Criswell, Evans Univ Alabama Informat Technol & Syst Ctr Huntsville AL 35899 USA

ISBN: (纸本)9780819471659

Large scale sensor networks composed of many low-cost small sensors networked together with a small number of high fidelity position sensors can provide a robust, fast and accurate air defense and warning system. The team has been developing simulations of such large networks, and is now adding terrain data in an effort to provide more realistic analysis of the approach. This work, a heterogeneous sensor network simulation system with integrated terrain data for real-time target detection in a three-dimensional environment is presented. The sensor network can be composed of large numbers of low fidelity binary and bearing-only sensors, and small numbers of high fidelity position sensors, such as radars. The binary and bearing-only sensors are randomly distributed over a large geographic region;while the position sensors are distributed evenly. The elevations of the sensors are determined through the use of DTED Level 0 dataset. The targets are located through fusing measurement information from all types of sensors modeled by the simulation. The network simulation utilizes the same search-based optimization algorithm as in our previous two-dimensional sensor network simulation with some significant modifications. The fusion algorithm is parallelized using spatial decomposition approach: the entire surveillance area is divided into small regions and each region is assigned to one compute node. Each node processes sensor measurements and terrain data only for the assigned sub region. A master process combines the information from all the compute nodes to get the overall network state. The simulation results have indicated that the distributed fusion algorithm is efficient enough so that an optimal solution can be reached before the arrival of the next sensor data with a reasonable time interval, and real-time target detection can be achieved. The simulation was performed on a Linux cluster with communication between nodes facilitated by the Message Passing Interface (

关键词： heterogeneous sensor network data fusion terrain data real-time target detection three-dimensional space

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multisensor, multisource information fusion: architectures, algorithms, and applications 2006

Multisensor, Multisource Information Fusion: Architectures, ...

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multisensor, multisource information fusion: architectures, algorithms, and applications 2006

ISBN: (纸本)0819462985

The proceedings contain 33 papers. The topics discussed include: multisource evidence fusion for cyber-situation assessment;combining elements of information fusion and knowledge-based systems to support situation analysis;measuring situational awareness and resolving inherent high-level fusion obstacles;higher-level fusion for military operations based on abductive inference: proof of principle;fusion terrain and goals: agent control in urban environments;hybrid methods for multisource information fusion and decision support;classifiers and distance-based evidential fusion for road travel time estimation;feature extraction and fusion for protein structure identification in cryo-electron microscopic images using independent component analysis and the projection-slice synthetic filter;and prediction of adverse outcomes of acute coronary syndrome using intelligent fusion of triage information with HUMINT.

关键词： Knowledge based systems

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Signal-to-noise ratio for cross-sensor fusion approach

Signal-to-noise ratio for cross-sensor fusion approach

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Conference on multisensor, multisource information fusion

作者： Kozaitis, S. P. Ouendeno, M. Florida Inst Technol Dept Elect & Comp Engn 150 W Univ Blvd Melbourne FL 32901 USA

We determined the signal-to-noise ratios for an image fusion approach that is suitable for application to systems with disparate sensors. We found there was reconstruction error present when the forward and reverse tr... 详细信息

ISBN: (纸本)9780819466938

关键词： image fusion image transform signal-to-noise ratio wavelet wavelet transform

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Novel classification fusion techniques for military target classification

Novel classification fusion techniques for military target c...

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Conference on multisensor, multisource information fusion

作者： Liu, Ting Lampropoulos, George A. AUG Signals 73 Richmond St W Toronto ON Canada

ISBN: (纸本)9780819466938

The objective of this paper is to develop novel classification structures for military targets detection and recognition by employing different fusion techniques. In real applications, the great diversity of materials in the background areas and the similarity between the background and target signatures result in high false alarm rates and large miss classification errors. In this paper, three new systems are proposed using different fusion techniques: pixel level fusion, decision fusion, and classification fusion employing confidence vectors. These new developed systems are tested using an experimental data to show its effectiveness.

关键词： hyperspectral detection classification fusion confusion matrix false alarm

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fusion of disparate information sources in a hybrid decision-support architecture

Fusion of disparate information sources in a hybrid decision...

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Conference on multisensor, multisource information fusion

作者： Braun, Jerome J. Glina, Yan Brattain, Laura MIT Lincoln Lab 244 Wood St Lexington MA 02420 USA

ISBN: (纸本)9780819466938

Disparity and uncertainty of information sources are both significant problems in information fusion. This paper investigates the problem of disparity in general, and in conjunction with FLASH - a hybrid information-fusion cognitive-processing approach we developed. Different forms of disparity are identified and their categorization is presented, and their implications on the information fission process are discussed. The issue of feature-level vs. decision-level fusion is investigated, and the methods of coping with disparity within FLASH are presented. Source uncertainty estimation techniques are discussed as well. Disparity studies and the results of computational experiments related to them are presented. These studies are suggestive of the potential of the FLASH hybrid approach for fusion of disparate information sources.

关键词： information fusion machine intelligence disparity uncertainty. biodefense

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fusion and kernel type selection in adaptive image retrieval

Fusion and kernel type selection in adaptive image retrieval

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Conference on multisensor, multisource information fusion

作者： Doloc-Mihu, Anca Raghavan, Vijay V. Univ SW Louisiana Ctr Adv Comp Studies Lafayette LA 70504 USA

ISBN: (纸本)9780819466938

In this work we investigate the relationships between features representing images, fusion schemes for these features and kernel types used in an Web-based Adaptive Image Retrieval System. Using the Kernel Rocchio learning method, several kernels having polynomial and Gaussian forms are applied to general images represented by annotations and by color histograms in RGB and HSV color spaces. We propose different fusion schemes, which incorporate kernel selector component(s). We perform experiments to study the relationships between a concatenated vector and several kernel types. Experimental results show that an appropriate kernel could significantly improve the performance of the retrieval system.

关键词： fusion image retrieval kernel learning

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A Bayesian framework for ATR decision-level fusion experiments

A Bayesian framework for ATR decision-level fusion experimen...

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Conference on multisensor, multisource information fusion

作者： Morgan, Douglas R. Ross, Timothy D. Consultant BAE Mountain View CA USA AFRL SNAR Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9780819466938

The US Air Force Research Laboratory (AFRL) fusion for Identifying Targets Experiment (FITE) program aims to determine the benefits of decision-level fusion (DLF) of Automatic Target Recognition (ATR) products. This paper describes the Bayesian framework used to characterize the trade-space for DLF approaches and applications. The overall fusion context is represented as a Bayesian network and the fusion algorithms use Bayesian probability computations. Bayesian networks conveniently organize the large sets of random variables and distributions appearing in fusion system models, including models of operating conditions, prior knowledge, ATR performance, and fusion algorithms. The relationship between fuser performance and these models may be analytically stated (the FITE equation), but must be solved via stochastic system modeling and Monte Carlo simulation. A key element of the DLF trade-space is the degree to which the various models depend on ATR operating conditions, since these will determine the fuser's complexity and performance and will suggest new requirements on source ATRs.

关键词： Automatic target recognition Sensors Monte Carlo methods Performance modeling Data fusion Detection and tracking algorithms Systems modeling Autoregressive models Data modeling Environmental sensing

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Sensitivity analysis of an information fusion tool: OWA operator

Sensitivity analysis of an information fusion tool: OWA oper...

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Conference on multisensor, multisource information fusion

作者： Zarghaami, Mahdi Ardakanian, Reza Szidarovszky, Ferenc Sharif Univ Tech Dept Civil Engn 1 Tehran 113659313 Iran Univ Arizona Syst & Ind Engn Dept Tucson AZ 85721 USA

ISBN: (纸本)9780819466938

The successful design and application of the Ordered Weighted Averaging (OWA) method as an information fusion tool depend on the efficient computation of its order weights. The most popular methods for determining the order weights are the Fuzzy Linguistic Quantifiers approach and the Minimal Variability method which give different behavior patterns for OWA. These methods will be compared by using Sensitivity Analysis on the outputs of OWA with respect to the optimism degree of the decision maker. The theoretical results are illustrated in a water resources management problem. The Fuzzy Linguistic Quantifiers approach gives more information about the behavior of the OWA outputs in comparison to the Minimal Variability method. However, in using the Minimal Variability method, the OWA has a linear behavior with respect to the optimism degree and therefore it has better computation efficiency.

关键词： information fusion OWA sensitivity analysis optimism degree fuzzy linguistic quantifiers minimal variability method

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Multi-sensor detection and fusion technique

Multi-sensor detection and fusion technique

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Conference on multisensor, multisource information fusion

作者： Bhargave, Ashish Arnbrose, Barry Lin, Freddie Kazantzidis, Manthos Broadata Commun Inc Torrance CA USA

ISBN: (纸本)9780819466938

A multi-sensor detection and fusion technology is described in this paper. The system consist of inputs from three sensors, Infra Red, Doppler Motion, and Stereo Video. This choice of sensors is designed to give high reliability, infra red and Doppler to provide detection ability at night, stereo video has the ability to analyze depth and range information. The combination of these sensors has the ability to provide high probability of detection and very low false alarm rate. The technique consists of three processing parts corresponding to each sensor data, and a fusion module, which makes the final decision based on the inputs from the three parts. The signal processing and detection algorithms process the inputs from each sensors and provides a specific information to the fusion module. fusion module is based on bayes belief propagation theory. It takes the processed inputs from all the sensor modules and provides a final decision on the presence and absense of objects, as well as their reliability based on the iterative belief propagation algorithm operating on decision graphs. A prototype system was built using the technique to study the feasibility of intrusion detection for NASA's launch danger zone protection. The system verified the potential of the proposed algorithms and proved the feasibility of high probability of detection and low false alarm rates compared to many existing techniques.

关键词： Sensors Video Detection and tracking algorithms Image processing Doppler effect Prototyping Signal processing Infrared sensors Surveillance Video surveillance

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Multi sensor fusion of images for target identification

Multi sensor fusion of images for target identification

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Conference on multisensor, multisource information fusion

作者： Muthukumar, A. Anitha, G. Shanmugam, J. Madras Inst Technol Dept Elect Engn Div Avion Madras Tamil Nadu India

ISBN: (纸本)9780819466938

multisensor data fusion is an emerging technology applied to defense and non-defense applications. In this paper, a image fusion algorithm using different texture parameters is proposed to identify long-range targets. The method uses a semi-supervised approach for detecting single target from the input images. The procedure consists of three steps: Feature extraction, Feature level fusion and Sensor level fusion. In this study, two methods of texture feature extraction using co-occurrence matrix and run-length matrix are considered. Texture parameters are calculated at each pixel of the selected training image, and target non-target pixels identified manually. Some of the texture features calculated at the target position differ from those in the background. Discriminant analysis is used to perform feature level fusion on the training image, which classify target and non-target pixels. By applying the discriminant function to the feature space of textural parameters, a new image is created. The maxima of this image correspond to target point. The same discriminant function can be applied to the other images for detecting the trained targets regions. Sensor level fusion combines images obtained from feature level fusion of visual and IR images. The method was first tested with synthetically generated images and then with real images. Results are obtained using both co-occurrence and run-length method of texture feature extraction for target identification.

关键词： image fusion texture features discriminant analysis

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