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检索条件"任意字段=Conference on Sensor Fusion - Architectures, Algorithms, and Applications"

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Behavior analysis of grid-map based sensor fusion algorithms

Behavior analysis of grid-map based sensor fusion algorithms

引用

International conference on Robotics and applications

作者： Cohen, O Gil, S Korach, E Edan, Y Ben Gurion Univ Negev Dept Ind Engn & Management IL-84105 Beer Sheva Israel

ISBN: (纸本)0889863571

Many algorithms for sensor fusion exist;some algorithms have better performance than others. The purpose of this paper is to present a procedure to analyze the behaviour of a sensor fusion algorithm. The analysis reveals the necessary behaviours for performing successful sensory fusion. The analysis methodology was developed for a sensor fusion framework, which assumes three basic concepts: logical sensors, grid map paradigm and performance measures. Behaviour analysis of the sensor fusion algorithms is conducted by analyzing transition matrices. Three types of behaviours are characterized: normal, dissimilarity and hysteresis. Five algorithms were compared: Three logical algorithms (AND, OR and MOST) and two adaptive algorithms: adaptive Fuzzy Logic algorithm and adaptive Dempster Shafer algorithm. The analysis indicated that the MOST, OR and adaptive Fuzzy Logic algorithms are all normal, but the adaptive Fuzzy Logic algorithm has better performance. The adaptive Dempster Shafer has dissimilarity and therefore in some cases diverges.

关键词： data fusion logical sensors mobile robots performance analysis

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Studying the complexities in distributed object tracking systems

Studying the complexities in distributed object tracking sys...

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IEEE International conference on Systems, Man and Cybernetics (SMC 03)

作者： Llinas, J SUNY Buffalo Ctr Multisource Informat Fus Dept Ind Engn Buffalo NY 14260 USA

ISBN: (纸本)0780379527

Tracking of moving objects based on multi-sensor input is a topic that has received considerable attention, and has been the subject of many papers. As web-type capabilities have emerged, along with ever-improving wireless communications capabilities, coupled with the needs of various applications to include "Network-Centric Warfare" on the part of the military, research on distributed multi-object, multi-sensor tracking has received some attention in recent years. However, the study of these distributed problems at the complete-architecture level, accounting for (among yet other factors) organizational information-sharing protocols, dynamic network topologies, mixes of stationary and moving sensors, complex object dynamics, equipment failure modes, node-specific tracking algorithms and data fusion algorithms, does not seem to have been addressed in any "holistic" way. This paper addresses these issues and describes an integrated testbed being developed at the University at Buffalo's Center for Multisource Information fusion for the empirical study of these complex architectures involving, in essence, the interconnection of multiple systems.

关键词： Distributed Tracking filtering estimation information fusion resource management

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Investigation of automated feature extraction using multiple data sources

Investigation of automated feature extraction using multiple...

引用

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

作者： Harvey, NR Perkins, SJ Pope, PA Theiler, J David, NA Porter, RB Los Alamos Natl Lab Space & Remote Sensing Sci Grp Los Alamos NM 87545 USA

ISBN: (纸本)0819449598

An increasing number and variety of platforms are now capable of collecting remote sensing data over a particular scene. For many applications, the information available from any individual sensor may be incomplete, inconsistent or imprecise. However, other sources may provide complementary and/or additional data. Thus, for an application such as image feature extraction or classification, it may be that fusing the mulitple data sources can lead to more consistent and reliable results. Unfortunately, with the increased complexity of the fused data, the search space of feature-extraction or classification algorithms also greatly increases. With a single data source, the determination of a suitable algorithm may be a significant challenge for an image analyst. With the fused data, the search for suitable algorithms can go far beyond the capabilities of a human in a realistic time frame, and becomes the realm of machine learning, where the computational power of modern computers can be harnessed to the task at hand. We describe experiments in which we investigate the ability of a suite of automated feature extraction tools developed at Los Alamos National Laboratory to make use. of multiple data sources for various feature extraction tasks. We compare and contrast this software's capabilities on 1) individual data sets from different data sources 2) fused data sets from multiple data sources and 3) fusion of results from multiple individual data sources.

关键词： supervised classification data fusion support vector machines feature extraction machine learning multispectral

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Integration of language and sensor information

Integration of language and sensor information

引用

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

作者： Perlovsky, LI Weijers, B Air Force Res Lab Bedford MA 01731 USA

ISBN: (纸本)0819449598

The talk describes the development of basic technologies of intelligent systems fusing data from multiple domains and leading to automated computational techniques for understanding data contents. Understanding involves inferring appropriate decisions and recommending proper actions, which in turn requires fusion of data and knowledge about objects, situations, and actions. Data might include sensory data, verbal reports, intelligence intercepts, or public records, whereas knowledge ought to encompass the whole range of objects, situations, people and their behavior, and knowledge of languages. In the past, a fundamental difficulty in combining knowledge with data was the combinatorial complexity of computations, too many combinations of data and knowledge pieces had to be evaluated. Recent progress in understanding of natural intelligent systems, including the human mind, leads to the development of neurophysiologically motivated architectures for solving these challenging problems, in particular the role of emotional neural signals in overcoming combinatorial complexity of old logic-based approaches. Whereas past approaches based on logic tended to identify logic with language and thinking, recent studies in cognitive linguistics have led to appreciation of more complicated nature of linguistic models. Little is known about the details of the brain mechanisms integrating language and thinking. Understanding and fusion of linguistic information with sensory data represent a novel challenging aspect of the development of integrated fusion systems. The presentation will describe a non-combinatorial approach to this problem and outline techniques that can be used for fusing diverse and uncertain knowledge with sensory and linguistic data.

关键词： information fusion language sensors intelligent systems understanding linguistic models knowledge non-combinatorial cognitive neural

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Improved image segmentation using an inference fusion architecture

Improved image segmentation using an inference fusion archit...

引用

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

作者： Brucks, T Riddle, J Van Maasdam, P Raytheon Missile Syst Tucson AZ 85734 USA

ISBN: (纸本)0819449598

Image segmentation, a key component in many Automatic Target Recognition (ATR) systems, has received considerable attention in the research community in recent years. A variety of segmentation approaches exist, and attempts have been made to combine various approaches in order to find more robust solutions. In this paper, the authors describe an inference fusion architecture for combining individual segmentation concepts which results in improved performance over the individual algorithms. We consider segmentation algorithms with several disparate cost functions as experts with a narrowly defined set of goals. The information obtained from each expert is combined and weighted with available evidence using an agent based inference system, resulting in an adaptive, robust and highly flexible image segmentation. Results obtained by applying this approach will be presented.

关键词： ATR image segmentation inference systems information fusion

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Sequence images fusion based on wavelet and grey prediction

Sequence images fusion based on wavelet and grey prediction

引用

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

作者： Tao, HJ Xiong, HJ Chen, W Tian, JW Wuhan Univ Technol Wuhan 430074 Peoples R China

ISBN: (纸本)0819449598

We use our proposed discrete multi-resolution wavelet transform and grey system theory prediction to fuse sequence images to generate a high quality image. The fused images are simultaneously obtained via only one wavelet transform and the sequence of images. Several other methods were implemented to compare with the proposed approach. In fusion image, The sequence of images information can supplement each other, so the image fusion not only have abundant information, but also reserve the sequence of images detail. This experiment results also illuminates that image fusion is an important way to improve represent ability of image.

关键词： image fusion sequence image wavelet transform grey system prediction

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Distributed fault-tolerant classification fusion using error correcting codes

Distributed fault-tolerant classification fusion using error...

引用

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

作者： Wang, TY Varshney, PK Han, YS Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

ISBN: (纸本)0819449598

In this paper we propose a new approach for distributed multiclass classification using a hierarchical fusion architecture. Binary decisions from local sensors, possibly in the presence of faults, axe fused locally. Locally fused results are forwarded to the global fusion center that determines the final classification result. Classification fusion in our approach is implemented via error correcting codes to incorporate fault-tolerance capability. This new approach not only provides an improved fault-tolerance capability but also reduces bandwidth requirements as well as computation time and memory requirements at the fusion center. Numerical examples axe provided to illustrate the performance of this new approach.

关键词： distributed classification decision fusion multisensor fusion error correcting codes fault-tolerance

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Fusing multiple images with evidential reasoning

Fusing multiple images with evidential reasoning

引用

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

作者： Yuan, XH Jian, Z Buckles, BP Tulane Univ Dept Elect Engn & Comp Sci New Orleans LA 70001 USA

ISBN: (纸本)0819449598

An approach to fuse multiple images based on Dempster-Shafer evidential reasoning is proposed in this, article. Dempster-Shafer theory provides a complete framework for combining weak evidences from multiple sources. Such situations typically arise in the image fusion problems, where a 'real scene' image has to be estimated from incomplete and unreliable observations. By converting images from their spatial domain into the evidential representations, decisions are made to aggregate evidences such that a fused image is generated. The proposed fusion approach is evaluated on a broad set of images and promising results are given.

关键词： image fusion evidential reasoning multiscale decomposition

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Design of a performance evaluation methodology for data fusion-based multiple target tracking systems

Design of a performance evaluation methodology for data fusi...

引用

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

作者： Rawat, S Llinas, J Bowman, C SUNY Buffalo Ctr Multisource Informat Fus Buffalo NY 14260 USA

ISBN: (纸本)0819449598

The emphasis of this paper is to design a Performance Evaluation Methodology for Data fusion-based Multiple Target Tracking Systems. Within this methodology the Performance Evaluation process is treated as a whole new fusion process. This has two major advantages - (1) Facilitates reusability of existing models and algorithms, and (2) Using standard frameworks and norms makes it easier for the tracking community to easily adopt it - thus giving this aspect of tracking a highly needed jumpstart. A case study implementation of this design methodology is presented. Three different Track-Truth Association strategies were implemented to study the effect of Track-Truth Association strategies on the performance metrics. The case study results conclusively show that the selection of the Track-Truth Association strategy should be done with reference to the scenario characteristics, the "mission" goals and the performance metrics to be evaluated.

关键词： data fusion target tracking performance evaluation

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Fusing multisensor and multisource data with implicit knowledge for monitoring

Fusing multisensor and multisource data with implicit knowle...

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

作者： Ramers, DL Univ N Carolina Sch Engn Charlotte NC 28223 USA

ISBN: (纸本)0819449598

This paper describes a series of experiments in data fusion. of remotely sensed multispectral satellite imagery, in-situ physical measurement data (temperature, pH, salinity), and implicitly encoded knowledge (contained in location and season) to predict values and classified levels of chlorophyll-a using an artificial. neural net (ANN). ANNs inherently fuse data inputs and discover relationships to provide a fused interpretation of the inputs. The experiments investigated the effects of fusing data and knowledge from the three different types of sources: non-contact, physical contact, and implicit. The results indicate that fusing the three source types improved prediction of chlorophyll-a values and classification levels, and that the multisource ANN fusion approach might improve or augment present periodic sample point monitoring methods for chlorophyll-a.

关键词： data fusion neural nets multispectral chlorophyll-a estuary monitoring

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