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检索条件"任意字段=Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2011"

共 282 条记录，以下是1-10 订阅

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multisensor, multisource information fusion: architectures, algorithms, and applications 2011

Multisensor, Multisource Information Fusion: Architectures, ...

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multisensor, multisource information fusion: architectures, algorithms, and applications 2011

ISBN: (纸本)9780819486387

The proceedings contain 20 papers. The topics discussed include: image fusion for remote sensing using fast, large-scale neuroscience models;the continuum fusion theory of signal detection applied to a bi-modal fusion problem;feature aided Monte Carlo probabilistic data association filter for ballistic missile tracking;architectures, algorithms, and applications using Bayesian networks;fusion of hyperspectral and ladar data for autonomous target detection;a relaxed fusion of information from real and synthetic images to predict complex behavior;the perception problem and the impact on robotics and computer vision;a hidden Markov model for multimodal biometrics score fusion;multi sensor remote sensing information fusion for urban area classification and change detection;and fusion of chemical, biological, and meteorological observations for agent source term estimation and hazard refinement.

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multisensor, multisource information fusion: architectures, algorithms, and applications 2014

Multisensor, Multisource Information Fusion: Architectures, ...

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multisensor, multisource information fusion: architectures, algorithms, and applications 2014

ISBN: (纸本)9781628410587

The proceedings contain 22 papers. The topics discussed include: embedding the results of focused Bayesian fusion into a global context;new results in semi-supervised learning using adaptive classifier fusion;probabilistic multi-source multi-INT Intel fusion benefit analysis;multi-sensor fusion with non-optimal decision rules: the challenges of open world sensing;characterization of computer network events through simultaneous feature selection and clustering of intrusion alerts;a metamaterial-inspired combined inductive-capacitive sensor;synchronous radiation sensing and 3D urban mapping for improved source identification;on an efficient and effective intelligent transportation system (ITS) using field and simulation data;and detection of oil pollution along the pipeline routes in tropical ecosystem from multi-spectral data.

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Towards the Exploitation of Formal Methods for information fusion

Towards the Exploitation of Formal Methods for Information F...

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Conference on multisensor, multisource information fusion - architectures, algorithms, and applications

作者： Clemens, Joachim Wille, Robert Schill, Kerstin Univ Bremen Cognit Neuroinformat D-28359 Bremen Germany Johannes Kepler Univ Linz Inst Integrated Circuits Linz Austria DFKI GmbH Cyber Phys Syst Bremen Germany

ISBN: (纸本)9781510601130

When an autonomous system has to act in or interact with an environment, a suitable representation of it is required. In the past decades, many different representation forms - especially spacial ones - have been proposed and even more information fusion techniques were developed in order to build these representations from multiple information sources. However, most of these algorithms do not exploit the full potential of the available information. This is caused by the fact that they are not able to handle the full complexity of all possible solutions compatible with the information and that they rely on restrictive assumptions (i.e. independencies) in order to make the computation feasible. In this work, a new methodology is envisioned that utilizes formal methods, in particular solvers for Pseudo-Boolean Optimization, to drop some of these assumptions. In order to illustrate the ideas, information fusion based on belief functions and occupancy grid maps are considered. It is shown that this approach allows for considering dependencies among multiple cells and thus significantly reduces the uncertainty in the resulting representation.

关键词： information fusion formal methods belief function theory occupancy grid maps incomplete information Boolean satisfiability

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PASES: Combining radar and video sensing for improved pedestrian safety

PASES: Combining radar and video sensing for improved pedest...

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Conference on multisensor, multisource information fusion - architectures, algorithms, and applications

作者： Chenault, David B. Edmondson, Richard P. Polaris Sensor Technol Inc 200 West Side SqSuite 320 Huntsville AL 35801 USA

ISBN: (纸本)9781510601130

Polaris Sensor Technologies reports on the development of Pedestrian Automated System for Enforcement and Safety (PASES), a radar and video based system used to monitor vehicle and pedestrian traffic with the intent of improving pedestrian safety. Data is fused from a system of multiple sensors and multiple sensor modalities to identify vehicular violations of pedestrian right of way. A focus was placed on the selection of low cost COTS sensors to make the system more widely available to state and local DOTs with limited budgets. applications include automated enforcement, adaptive traffic control, and improved intersection and crosswalk design based on high quality data available for traffic engineering. We discuss early results with high fidelity sensors, and the performance trades made in order to make the system affordable. A discussion of the system processing architecture is included which highlights the treatment of each sensor data type, and the means of combining the processed data products into state information related to traffic incidents involving vehicles and pedestrians.

关键词： Pedestrian radar video tracking sensor fusion

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Optimal Rotation Sequences for Active Perception

Optimal Rotation Sequences for Active Perception

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Conference on multisensor, multisource information fusion - architectures, algorithms, and applications

作者： Nakath, David Rachuy, Carsten Clemens, Joachim Schill, Kerstin Univ Bremen Cognit Neuroinformat D-28359 Bremen Germany

ISBN: (纸本)9781510601130

One major objective of autonomous systems navigating in dynamic environments is gathering information needed for self localization, decision making, and path planning. To account for this, such systems are usually equipped with multiple types of sensors. As these sensors often have a limited field of view and a fixed orientation, the task of active perception breaks down to the problem of calculating alignment sequences which maximize the information gain regarding expected measurements. Action sequences that rotate the system according to the calculated optimal patterns then have to be generated. In this paper we present an approach for calculating these sequences for an autonomous system equipped with multiple sensors. We use a particle filter for multi sensor fusion and state estimation. The planning task is modeled as a Markov decision process (MDP), where the system decides in each step, what actions to perform next. The optimal control policy, which provides the best action depending on the current estimated state, maximizes the expected cumulative reward. The latter is computed from the expected information gain of all sensors over time using value iteration. The algorithm is applied to a manifold representation of the joint space of rotation and time. We show the performance of the approach in a spacecraft navigation scenario where the information gain is changing over time, caused by the dynamic environment and the continuous movement of the spacecraft.

关键词： Markov decision process manifold representation particle filter value iteration multi-sensor fusion optimal decision making active perception information gain

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Visualization to support Identification, Exploitation and fusion of Data and information delivered from Heterogeneous Sources in ISR

Visualization to support Identification, Exploitation and Fu...

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Conference on multisensor, multisource information fusion - architectures, algorithms, and applications

作者： Jamboti, Kavyashree Van de Camp, Florian Kuwertz, Achim Haferkorn, Daniel Eck, Ralf Grasemann, Gunther Fraunhofer IOSB Fraunhofer Str 1 D-76131 Karlsruhe Germany

ISBN: (纸本)9781510601130

In ISR (Intelligence, Surveillance and Reconnaissance), heterogeneous sources deliver data and information having spatial and temporal attributes. Real time and non-real time data created for various purposes, present in different formats has to be exploited and fused. The Coalition Shared Data (CSD) concept makes the interoperable sharing of ISR data and information possible. The concept itself and a technical approach for it were developed within the multinational projects CAESAR, MAJIIC and MAJIIC 2 and tested in coalition exercises. The interfaces of software systems providing access to CSD data must allow the user to intuitively use the system and form a substantial part with regard to user acceptance. We describe different systems and approaches we designed and developed to access CSD data that can locate and present the data to the user based on his specific demands. Visualization forms an important part of these systems which share the common challenge of representing data and information with spatial and temporal attributes. The visualization of data and information has to be designed in a manner that supports efficient access, discovery and optionally additional processing (such as filtering and sorting). Given the large amount of data and information that may be available, visualization taking into account their quality and inherent uncertainty is an additional challenge. This publication provides an overview of the systems and approaches we developed to present CSD data and identifies challenges common to these systems. To tackle these challenges, we present new research results regarding visualization of data and information with temporal and spatial attributes.

关键词： ISR visualization spatio-temporal data CSD clustering filtering heat maps

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Uncertainty Characterization Using Copulas for Classification

Uncertainty Characterization Using Copulas for Classificatio...

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Conference on multisensor, multisource information fusion - architectures, algorithms, and applications

作者： Ozdemir, Onur Choi, Sora Allen, Thomas G. Varshney, Pramod K. Boston Fus Corp Burlington MA 01824 USA Syracuse Univ Dept EECS Syracuse NY USA

ISBN: (纸本)9781628416145

We address the problem of characterizing uncertainty for multisensor data fusion in a classification problem. To achieve this goal, we model the joint density of given multivariate data using copula functions while allowing the ability to incorporate any desired marginal distributions, i.e., any desired modalities. The proposed model is data driven in that the corresponding copula functions and their parameters are learned from the data. Our results show that the proposed framework can capture the uncertainties more accurately than current state of the practice, and lead to robust and improved classification performance compared to traditional classifiers.

关键词： multisource data fusion copula theory classification

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STAC: A Comprehensive Sensor fusion Model for Scene Characterization

STAC: A Comprehensive Sensor Fusion Model for Scene Characte...

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Conference on multisensor, multisource information fusion - architectures, algorithms, and applications

作者： Kira, Zsolt Wagner, Alan R. Kennedy, Chris Zutty, Jason Tuell, Grady Georgia Tech Res Inst Atlanta GA 30332 USA

ISBN: (纸本)9781628416145

We are interested in data fusion strategies for Intelligence, Surveillance, and Reconnaissance (ISR) missions. Advances in theory, algorithms, and computational power have made it possible to extract rich semantic information from a wide variety of sensors, but these advances have raised new challenges in fusing the data. For example, in developing fusion algorithms for moving target identification (MTI) applications, what is the best way to combine image data having different temporal frequencies, and how should we introduce contextual information acquired from monitoring cell phones or from human intelligence? In addressing these questions we have found that existing data fusion models do not readily facilitate comparison of fusion algorithms performing such complex information extraction, so we developed a new model that does. Here, we present the Spatial, Temporal, Algorithm, and Cognition (STAC) model. STAC allows for describing the progression of multi-sensor raw data through increasing levels of abstraction, and provides a way to easily compare fusion strategies. It provides for unambiguous description of how multi-sensor data are combined, the computational algorithms being used, and how scene understanding is ultimately achieved. In this paper, we describe and illustrate the STAC model, and compare it to other existing models.

关键词： multisensor fusion data fusion scene characterization scene understanding

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Reliable sources and uncertain decisions in multisensor systems

Reliable sources and uncertain decisions in multisensor syst...

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Conference on multisensor, multisource information fusion - architectures, algorithms, and applications

作者： Minor, Christian Johnson, Kevin Nova Res Inc Alexandria VA 22308 USA Naval Res Lab Washington DC 20375 USA

ISBN: (纸本)9781628416145

Conflict among information sources is a feature of fused multisource and multisensor systems. Accordingly, the subject of conflict resolution has a long history in the literature of data fusion algorithms such as that of Dempster-Shafer theory (DS). Most conflict resolution strategies focus on distributing the conflict among the elements of the frame of discernment (the set of hypotheses that describe the possible decisions for which evidence is obtained) through rescaling of the evidence. These "closed-world" strategies imply that conflict is due to the uncertainty in evidence sources stemming from their reliability. An alternative approach is the "open-world" hypothesis, which allows for the presence of "unknown" elements not included in the original frame of discernment. Here, conflict must be considered as a result of uncertainty in the frame of the discernment, rather than solely the province of evidence sources. Uncertainty in the operating environment of a fused system is likely to appear as an open-world scenario. Understanding the origin of conflict (source versus frame of discernment uncertainty) is a challenging area for research in fused systems. Determining the ratio of these uncertainties provides useful insights into the operation of fused systems and confidence in their decisions for a variety of operating environments. Results and discussion for the computation of these uncertainties are presented for several combination rules with simulated data sets.

关键词： multisensor systems data fusion sensor reliability interferants open-world sensing Dempster-Shafer theory combination rules

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A Theoretical Performance Analysis of Discrete Data Classification When Fusing Two Features

A Theoretical Performance Analysis of Discrete Data Classifi...

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Conference on multisensor, multisource information fusion - architectures, algorithms, and applications

作者： Lynch, Robert Willett, Peter Analyt Informat Fus Syst LLC Old Mystic CT 06372 USA Univ Connecticut ECE Dept Storrs CT 02841 USA

ISBN: (纸本)9781628416145

In this work, an analytical model has been developed to demonstrate classification performance when fusing two quantized features. Specifically, it is of interest to demonstrate theoretically the effect that the overall quantization of the features, M, has on the relative performance of the Bayesian Data Reduction Algorithm (BDRA). The primary results show, and with a training data model independent of distribution, conditions on the data under which dimensionality reduction improves overall theoretical classification performance. This result is significant for those interested in the theoretical performance of fusing discrete data (i.e., attributes or classifier decisions), and is an important step towards proving that BDRA always converges to a unique solution.

关键词： Dirichlet distribution Discrete features Dimensionality reduction Classifier and Feature fusion Theoretical modeling

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