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

作者： Tari, S Middle E Tech Univ Dept Engn Sci TR-06531 Ankara Turkey

ISBN: (纸本)0819431931

Availability of different imaging modalities requires techniques to process and combine information from different images of the same phenomena. We present a symmetry based approach for combining information from multiple images. fusion is performed at data level. Actual object boundaries and shape descriptors are recovered directly from raw sensor output(s). Method is applicable to arbitrary number of images in arbitrary dimension.

关键词： Image fusion Data fusion Image segmentation sensors Image processing Information fusion Data processing Shape analysis 3D image processing Image analysis

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Convex optimization approach to the fusion of identity information

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3719卷 389-397页

作者： Li, Lingjie Luo, Zhi-Quan Wong, K.Max Bosse, Eloi McMaster Univ Hamilton Canada

We consider the problem of identify fusion for a multi-sensor target tracking system whereby sensors generate reports on the target identities. Since the sensor reports are typically fuzzy, 'incomplete' and inconsistent, the fusion of such sensor reports becomes a major challenge. In this paper, we introduce a new identify fusion approach based on the minimization of inconsistencies between the sensor reports by using a convex Quadratic Programming (QP) and linear programming (LP) formulation. In contrast to the Dempster-Shafer's evidential reasoning approach which suffers from exponentially growing complexity, our approach is highly efficient (polynomial time solvable). Moreover, our approach is capable of fusing 'Ratio type' sensor reports, thus it is more general than the evidential reasoning theory. When the sensor reports are consistent, the solution generated by the new fusion method can be shown to converge to the true probability distribution. Simulation work shows that our method generates reasonable fusion results, and when only 'Subset type' sensor reports are present, it produces fusion results similar to that obtained via the evidential reasoning theory.

关键词： sensor data fusion

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Fuzzy sensor fusion for gas turbine power plants

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3719卷 52-61页

作者： Goebel, Kai Agogino, Alice GE Corp Research & Development Niskayuna United States

In this paper we present a methodology for fuzzy sensor fusion. We then apply this methodology to sensor data from a gas turbine power plant. The developed fusion algorithm tackles several problems: 1.) It aggregates redundant (but uncertain) sensor information; this allows making decisions which sensors (and to what degree) should be considered for propagation of sensor information. 2.) It filters out noise and sensor failure from measurements; this allows a system to operate despite temporary or permanent failure of one or more sensors. For the fusion, we use a combination of direct and functional redundancy. The fusion algorithm uses confidence values obtained for each sensor reading from validation curves and performs a weighted average fusion. With increasing distance from the predicted value, readings are discounted through a non-linear validation function. They are assigned a confidence value accordingly. The predicted value in the described algorithm is obtained through application of a fuzzy exponential weighted moving average time series predictor with adaptive coefficients. Experiments on real data from a gas turbine power plant show the robustness of the fusion algorithm which leads to smooth controller input values.

关键词： sensor data fusion

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Feature level sensor fusion

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3719卷 332-338页

作者： Peli, Tamar Young, Mon Knox, Robert Ellis, Ken Bennett, Fredrick Atlantic Aerospace Electronics Corp Waltham United States

This paper describes two practical fusion techniques (hybrid fusion and cued fusion) for automatic target cueing that combine features derived from each sensor data at the object-level. In the hybrid fusion method each of the input sensor data is prescreened (i.e. Automatic Target Cueing (ATC) is performed) before the fusion stage. The cued fusion method assumes that one of the sensors is designated as a primary sensor, and thus ATC is only applied to its input data. If one of the sensors exhibits a higher Pd and/or a lower false alarm rate, it can be selected as the primary sensor. However, if the ground coverage can be segmented to regions in which one of the sensors is known to exhibit better performance, then the cued fusion can be applied locally/adaptively by switching the choice of a primary sensor. Otherwise, the cued fusion is applied both ways (each sensor as primary) and the outputs of each cued mode are combined. Both fusion approaches use a back-end discrimination stage that is applied to a combined feature vector to reduce false alarms. The two fusion processes were applied to spectral and radar sensor data and were shown to provide substantial false alarm reduction. The approaches are easily extendable to more than two sensors.

关键词： sensor data fusion

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The principle for synthesizing measurements of multiple system parameters into a single parameter

The principle for synthesizing measurements of multiple syst...

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

作者： Pu, QG Univ Sci & Technol Suzhou Ctr Comp Suzhou 215011 Jiangsu Peoples R China

ISBN: (纸本)0819444812

Modern technology provides a great amount of information. In computer monitoring systems or computer control systems, especially real-time expert systems, in order to have the situation in hand, we need one or two parameters to express the quality and/or security of the whole system. This paper presents a principle for synthesizing measurements of multiple system parameters into a single parameter. This principle has been successfully applied in the monitoring of an ultra-energy efficient house in Canada and other applications.

关键词： sensor fusion parameter synthesis computer monitoring systems computer control systems real -time expert systems

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Identity and attribute information fusion using evidential reasoning

Identity and attribute information fusion using evidential r...

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Conference on sensor fusion - architectures, algorithms, and applications

作者： Bosse, E Simard, MA Defence Research Establishment Valcartier (Canada) Lockheed Martin Canada (Canada)

ISBN: (纸本)081942482X

The aim of this paper is to explore the problem of fusing identity and attribute information emanating from different sources, and to offer the decision maker a quantitative analysis based on statistical methodology that can enhance his/her decision making process regarding the identity of detected objects. Two identity information fusion architectures are discussed here. The first is concerned with the fusion of identity declarations where the sources are expected to provide only useful and complete results such as an identity declaration. The second is concerned with the fusion of attribute information using a modified version of the Dempster-Shafer evidential combination algorithm.

关键词： sensors Information fusion Data fusion Data processing Radar Electronic support measures Telecommunications Signal processing Databases Probability theory

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Information fusion benefits delineation in off-nominal scenarios

Information fusion benefits delineation in off-nominal scena...

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

作者： Dasarathy, BV Dynet Inc Huntsville AL 35814 USA

ISBN: (纸本)0819431931

The potential problem of deterioration In recognition system performance because of imprecise, incomplete, or imperfect training is a serious challenge inherent to most real-world applications. This problem is often referred to in certain applications as degradation of performance under off-nominal conditions. This study presents the results of an investigation carried out to illustrate the scope and benefits of information fusion in such off-nominal scenarios. The research covers features in - decision out (FEI-DEO) fusion as well as decisions in - decision out (DEI-DEO) fusion. The latter spans across both information sources (sensors) and multiple processing tools (classifiers). The investigation delineates the corresponding fusion benefit domains using as an example, real-world data from an audio-visual system for the recognition of French oral vowels embedded in various levels of acoustical noise.

关键词： information fusion off-nominal scenarios audio-visual fusion vowel recognition

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fusion of data from spatially separated sensors using Riemannian manifolds

Fusion of data from spatially separated sensors using Rieman...

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Conference on sensor fusion - architectures, algorithms, and applications

作者： Cain, MP Booz*Allen & Hamilton Inc. (United States)

ISBN: (纸本)081942482X

In this paper, an approach for representing target classes in feature space using Riemannian manifolds is explored. In a formal application of the approach, it is required that several basic assumptions used in the development of differential and Riemannian geometry are satisfied. These assumptions relate to the concepts of allowable parametric representations and allowable coordinate transformations. Developing target class representations which satisfy these assumptions has a direct consequence on the selection of a suitable feature set. Having found a suitable feature set, the approach results in a natural coordinate system in which to calculate distance metrics used in classification algorithms. In this paper, the approach is applied to a situation where an active sensor and a passive sensor are spatially separated and are simultaneously collecting data on a set of targets. It is shown that the use of the natural coordinate system offered by this approach leads to a straightforward and mathematically rigorous method for fusing the sensor data at the feature level.

关键词： Riemann data fusion feature level fusion classification

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Empirical investigations on Dempster-Shafer reasoning system in attribute fusion

Empirical investigations on Dempster-Shafer reasoning system...

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

作者： Silvennoinen, T Korpisaari, P Saarinen, J Tampere Univ Technol Digital & Comp Syst Lab FIN-33101 Tampere Finland

ISBN: (纸本)0819436771

The tracking system with Dempster-Shafer attribute association algorithm is studied. The aim of the paper is to study how the different parameters affect to the association accuracy. The results show that the proposed Dempster-Shafer attribute association algorithm is robust for parameter variations and thus for modelling errors. The simulations are done according to synthesized data.

关键词： Detection and tracking algorithms Kinematics Computing systems Modeling Performance modeling Personal digital assistants algorithms Associative arrays Environmental sensing Target recognition

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Consensus algorithms for power-constrained wireless sensor networks

Consensus algorithms for power-constrained wireless sensor n...

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作者： Savas, Onur Boston University

学位级别：Ph.D.

Wireless sensor networks (SNets) is a cost-efficient technology that is typically comprised of many low-power, low-cost sensors. Current and potential applications of SNets include tracking, automation, control, surveillance, reconnaissance, security, and monitoring. All of these applications require some form of intelligent signal processing and decision making algorithms. Proposed algorithms involving fusion-center-based architectures do not scale well with increasing number of sensors. Instead, scalable, robust and power-efficient distributed signal processing and decision making algorithms are required in order to realize the full potential of SNets. Therefore, in this thesis, we propose and analyze power-efficient in-network signal processing and decision making algorithms for applications that are modeled as distributed classification, data aggregation and symmetric function computation problems. The work in this thesis is a step towards solving distributed signal processing and decision making problems in low-power distributed sensor networks in a cost-efficient manner. The low-power, constrained communication architecture of emerging sensor network technologies demand scalable, asynchronous, energy-efficient and robust sensor network architectures and algorithms. We address these concerns in this thesis by declaring the proposed algorithm as: (i) Scalable because communication is restricted to immediate one-hop neighbors. (ii) Asynchronous because no synchronization between sensor nodes or order of transmission of messages are required. (iii) Energy-efficient in terms of reduced total power and average power consumed. (iv) Robust to node and edge failures. In this thesis we propose our solution in two main categories. (i) We propose and analyze pair-wise message passing algorithms, where sensors achieve the optimal classification performance. (ii) We propose and analyze algorithms in order to calculate the measurement statistics of certain functions. Under b

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