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检索条件"任意字段=Sensor Fusion: Architectures, Algorithms and Applications II"

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Optimal features in feature out (FEI-FEO) fusion for decisions in multisensor environments

Optimal features in feature out (FEI-FEO) fusion for decisio...

引用

Conference on sensor fusion - architectures, algorithms, and applications ii

作者： Belur, SV Dasarathy, BV Comp Sci Corp Dept Appl Res Syst Sci Div Lanham MD 20706 USA

ISBN: (纸本)0819428256

The study presents a formal methodology to the problem of feature level fusion, that had been previously addressed in the literature mostly in an ad hoc manner on a case by case basis only. The input set of features extracted from multiple sensors (data sources) are optimally fused to derive a synthetic feature so as to enhance the effective discrimination potential among the defined set of decision classes. This features in - feature out (FEI-FEO)' fusion process, unlike most other fusion schemes reported in the literature, is designed through a formal learning phase in which an optimal mapping from the multisensor derived feature space to a single unified feature is developed. This learning, accomplished through a new composite random and deterministic search based optimization teal, defines the transformation for the FEI-FEO process. This transformation is applied to the multi-sensor generated feature sets in the operational phase to derive the fused feature values corresponding to the objects under observation. The corresponding classification decisions are made on the basis of relative closeness of these feature values to the different class mean values in the transformed single dimensional feature space. The new methodology has been implemented in MATLAB which, being a vector/matrix oriented language, is an ideal candidate for solving problems in patter recognition and learning. The method is applied to well-known data sets available on the web for testing pattern recognition algorithms to assess its effectiveness relative to the traditional classification methods from both conceptual as well as computational view points.

关键词： feature level fusion feature optimization features in - feature out fusion feature synthesis

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Adaptive local fusion systems for novelty detection and diagnostics in condition monitoring

Adaptive local fusion systems for novelty detection and diag...

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Conference on sensor fusion - architectures, algorithms, and applications ii

作者： Taylor, O MacIntyre, J Univ Sunderland Sch Comp & Informat Sci Ctr Adapt Syst Sunderland SR6 0DD England

ISBN: (纸本)0819428256

This paper describes the application of Kohonen Self Organising Maps in a dynamic machine condition monitoring application which learns fault conditions over time. The authors describe the implementation of a novelty detection and adaptive diagnostic system which forms a modular component of a larger on-line condition monitoring system. NEURAL-MAINE, a project under the European Union's EUREKA programme, aims to advance on-line condition monitoring applications by the use of neural networks, data fusion and multiple sensor technology. NEURAL-MAINE aims to implement a system on two levels: at the first,, Local fusion Systems (LFS) are used to model individual machine components, such as high pressure turbines;at the second, a larger 'Overseer' system sits above the LFS and takes in their input, as well as plant operating parameters, in order to give a global view of the condition of the plant. This paper describes the implementation and testing of the two major components of the Local fusion System, namely the novelty detection and the adaptive diagnostic systems. Novelty detection works by using a Kohonen-based neural network(1) to learn the normal operating state of the component that is being modeled, with new values being passed into the network to see if it is similar to the learned normal states of operation. If the new fused sensor values are unlike the values which the Kohonen neural network represents, this is nagged as novel, and the values are passed to the adaptive diagnostic system. The adaptive diagnostic system takes as its input the values that the novelty detection system identified as novel and passes these into the Kohonen-based diagnostic networks. If the diagnostic networks recognize the values then a local diagnosis is given and passed to the Overseer system. If the diagnostic networks do not recognize the values, then these patterns are dynamically learned as the new fault condition occurs, and the diagnostic networks are updated. This system a

关键词： neural networks data fusion novelty detection adaptive diagnostic system

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architectures, algorithms, and applications Using Bayesian Networks

Architectures, Algorithms, and Applications Using Bayesian N...

引用

Conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Kingsbury, Todd Gen Dynam Adv Informat Syst San Diego CA 92108 USA

ISBN: (纸本)9780819486387

A Bayesian network is a tree structure where each branch represents a classification candidate. The leaves of the tree represent observable target features such as frequency or length. An optimized tree groups similar features together, e.g. frequency and pulse width, while collecting dissimilar or disparate information, e. g. spectral and kinematics, all within the same unifying structure. A vehicular track then is a subset of the a priori candidate library and contains only feasible branches. The algorithm for updating the confidence of each feasible candidate according to Bayes' rule is embedded in each track, as is the ability of a track to learn, apply a priori probability distributions, switch modes, switch among kinematics models, apply tracking history to classification and apply classification history to tracking, and support multisensor correlation and sensor fusion.

关键词： Bayesian tracking classification sensor fusion

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Decision level data fusion using Bayesian inference

Decision level data fusion using Bayesian inference

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Conference on sensor fusion - architectures, algorithms, and applications

作者： Parish, J TRW Inc. (United States)

ISBN: (纸本)081942482X

This paper describes a blackboard system for integrating observations from multiple sensors. Multiple sensors report observations to the blackboard system. The blackboard system correlates the observations to a set of active models, and the models are both temporally limited and also probabilistic. The design is object-oriented to allow for extensions that accommodate new models and sensors. An example application to a grid of sensors is presented

关键词： Bayesian inference blackboard systems data fusion

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Syntax and semantics in knowledge fusion:: a mixed approach

Syntax and semantics in knowledge fusion:: a mixed approach

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Conference on sensor fusion - architectures, algorithms, and applications VI

作者： Grégoire, É Univ Artois CRIL F-62307 Lens France

ISBN: (纸本)0819444812

In this paper, a series of knowledge fusion operators are motivated and analyzed. They are defined in a semantic way, although syntactical facets of knowledge are taken into account. More precisely, they rely on a rank-ordering of interpretations that is based on the number of formulas that the interpretations falsify. It is briefly discussed how these operators could be refined, by taking into account various distribution policies of the falsified information among the knowledge sources, syntactical properties of formulas to be fused and forms of integrity constraints preference among literals.

关键词： knowledge fusion knowledge representation

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A simple test-bed for real-time sensor-to-shooter applications of decentralised data fusion

A simple test-bed for real-time sensor-to-shooter applicatio...

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Conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2003

作者： Binns, L Cooke, A Fryers, S Greenway, P Mullin, D Nicholson, D Valachis, D BAE Syst Adv Technol Ctr Bristol BS34 7QW Avon England

ISBN: (纸本)0819449598

In this paper we describe a simple physical test-bed, developed to allow practical experimentation in the use of Decentralised Data fusion (DDF) in sensor-to-shooter applications. Running DDF over an ad hoc network of distributed sensors produces target location information. This is used to guide a Leica laser-tracker system to designate currently tracked targets. We outline how the system is robust to network and/or node failure. Moreover, we discuss the system properties that lead to it being completely "plug-and-play", as, like the distributed sensor nodes, the "shooter" does not require knowledge of the overall network topology and can connect at any point.

关键词： data fusion network centric warfare ad-hoc networks sensor-to-shooter

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The equivalence of Bayesian multi-sensor information fusion and neural networks

The equivalence of Bayesian multi-sensor information fusion ...

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Conference on sensor fusion - architectures, algorithms, and applications V

作者： Aarabi, P Stanford Univ Dept Comp Sci Stanford CA 94305 USA

ISBN: (纸本)0819440809

This paper proposes a Bayesian multi-sensor object localization approach that keeps track of the observability of the sensors in order to maximize the accuracy of the final decision. This is accomplished by adaptively monitoring the mean-square-error of the results of the localization system. Knowledge of this error and the distribution of the system's object localization estimates allow the result of each sensor to be scaled and combined in an optimal Bayesian sense. It is shown that under conditions of normality, the Bayesian sensor fusion approach is directly equivalent to a single layer neural network with a sigmoidal non-linearity. Furthermore, spatial and temporal feedback in the neural networks can be used to compensate for practical difficulties such as the spatial dependencies of adjacent positions. Experimental results using 10 binary microphone arrays yield an order of magnitude improvement in localization error for the proposed approach when compared to previous techniques.

关键词： object localization sensor fusion neural networks sound localization

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Objective pixel-level image fusion performance measure

Objective pixel-level image fusion performance measure

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Conference on sensor fusion: architectures, algorithms, and applications IV

作者： Xydeas, C Petrovic, V Univ Lancaster Dept Commun Syst Lancaster LA1 4YR England

ISBN: (纸本)0819436771

This paper addresses the issue of objectively measuring the performance of pixel level image fusion systems. The proposed fusion performance metric models the accuracy with which visual information is transferred from the input images to the fused image. Experimental results clearly indicate that the metric is perceptually meaningful.

关键词： Image fusion Image processing Information visualization Visualization Image quality Wavelets Coherence (optics) Computing systems Image analysis Image filtering

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sensor fusion cognition using belief filtering for tracking and identification

Sensor fusion cognition using belief filtering for tracking ...

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Conference on sensor fusion - architectures, algorithms, and applications iiI

作者： Blasch, E Hong, L SNAT AFRL Wright Patterson AFB OH 45433 USA

ISBN: (纸本)0819431931

Humans exhibit remarkable abilities to estimate, filter, predict, and fuse information in target tracking tasks. To improve track quality, we extend previous tracking approaches by investigating human cognitive-level fusion for constraining the set of plausible targets where the number of targets is not known a priori. The target track algorithm predicts a belief in the position and pose for a set of targets and an automatic target recognition algorithm uses the pose estimate to calculate an accumulated target-belief classification confidence measure. The human integrates the target track information and classification confidence measures to determine the number and identification of targets. This paper implements the cognitive belief filtering approach for sensor fusion and resolves target identify through a set-theory approach by determining a plausible set of targets being tracked.

关键词： sensor fusion target recognition classification & identification tracking cognitive belief filter HRR

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Virtual Associative Networks: A new paradigm for sensor fusion

Virtual Associative Networks: A new paradigm for Sensor Fusi...

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Conference on sensor fusion - architectures, algorithms, and applications iiI

作者： Malhotra, RP Yufik, YM USAF Res Lab Sensors Directorate Wright Patterson AFB OH 45433 USA

ISBN: (纸本)0819431931

We discuss Virtual Associative Networks (VANs) emd their relevance for addressing computationally prohibitive sensor fusion problems (with results in Dynamic sensor Management). To our knowledge, this discussion of VAN technology for sensor fusion is unique and our current result involving VANs for Dynamic sensor Management is the first of its kind. The following provides methodology, results, and extensions.

关键词： neurocomputation cognitive science sensor fusion sensor Management neural networks

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