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检索条件"任意字段=Conference on Signal Processing, Sensor Fusion, and Target Recognition XXII"

共 426 条记录，以下是1-10 订阅

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signal processing, sensor fusion, and target recognition xxii

Signal Processing, Sensor Fusion, and Target Recognition XXI...

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signal processing, sensor fusion, and target recognition xxii

ISBN: (纸本)9780819495365

The proceedings contain 58 papers. The topics discussed include: estimability of thrusting trajectories in 3D from a single passive sensor;advances in displaying uncertain estimates of multiple targets;overview of Dempster-Shafer and belief function tracking methods;decentralized closed-loop collaborative surveillance and tracking performance sensitivity to communications connectivity;stochastic context-free grammars for scale-dependent intent inference;sensor selection for target localization in a network of proximity sensors and bearing sensors;evaluating detection and estimation capabilities of magnetometer-based vehicle sensors;urban multitarget tracking via gas-kinetic dynamics models;tracking, identification, and classification with random finite sets;divergence detectors for multitarget tracking algorithms;and a Gaussian mixture ensemble transform filter for vector observations.

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Sequential testing over multiple stages and performance analysis of data fusion

Sequential testing over multiple stages and performance anal...

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conference on signal processing, sensor fusion, and target recognition xxii

作者： Thakur, Gaurav Mitre Corp Mclean VA 22102 USA

ISBN: (纸本)9780819495365

We describe a methodology for modeling the performance of decision-level data fusion between different sensor configurations, implemented as part of the JIEDDO Analytic Decision Engine (JADE). We first discuss a Bayesian network formulation of classical probabilistic data fusion, which allows elementary fusion structures to be stacked and analyzed efficiently. We then present an extension of the Wald sequential test for combining the outputs of the Bayesian network over time. We discuss an algorithm to compute its performance statistics and illustrate the approach on some examples. This variant of the sequential test involves multiple, distinct stages, where the evidence accumulated from each stage is carried over into the next one, and is motivated by a need to keep certain sensors in the network inactive unless triggered by other sensors.

关键词： sensors Data fusion sensor fusion sensor networks Statistical analysis Data modeling Tin Matrices Performance modeling sensor performance

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Multisource Information fusion For Enhanced Simultaneous Tracking and recognition

Multisource Information Fusion For Enhanced Simultaneous Tra...

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conference on signal processing, sensor fusion, and target recognition xxii

作者： Kahler, Bart Sci Applicat Int Corp Beavercreek OH 45431 USA

ISBN: (纸本)9780819495365

A layered sensing approach helps to mitigate sensor, target, and environmental operating conditions affecting target tracking and recognition performance. Radar sensors provide standoff sensing capabilities over a range of weather conditions;however, operating conditions such as obscuration can hinder radar target tracking. By using other sensing modalities such as electro-optical (EO) building cameras or eye witness reports, continuous target tracking and recognition may be achieved when radar data is unavailable. Information fusion is necessary to associate independent multisource data to ensure accurate target track and identification is maintained. Exploiting the unique information obtained from multiple sensor modalities with non-sensor sources will enhance vehicle track and recognition performance and increase confidence in the reported results by providing confirmation of target tracks when multiple sources have overlapping coverage of the vehicle of interest. The author uses a fusion performance model in conjunction with a tracking and recognition performance model to assess which combination of information sources produce the greatest gains for both urban and rural environments for a typical sized ground vehicle.

关键词： Information fusion tracking automatic target recognition (ATR) identification (ID) performance modeling operating conditions (OCs) text reports radar electro-optical (EO)

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Fundamentals of the Dempster-Shafer Theory

Fundamentals of the Dempster-Shafer Theory

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conference on signal processing, sensor fusion, and target recognition XXI

作者： Peri, Joseph S. J. Johns Hopkins Univ Appl Phys Lab Laurel MD 20723 USA

ISBN: (纸本)9780819490704

In this paper, I discuss the basic notions of the Dempster Shafer theory. Using a simple engineering example, I highlight sources of confusion in the Dempster Shafer literature, and some questions that arise in the course of applying the Dempster Shafer algorithm Finally, I discuss the measure theoretic foundation that reveals the intimate connections between the Dempster Shafer theory and Probability theory.

关键词： Probability theory sensors Algorithms Applied physics Current controlled current source Head Mathematics sensor fusion signal processing target recognition

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Multi-Parametric Data fusion for Enhanced Object Identification and Discrimination

Multi-Parametric Data Fusion for Enhanced Object Identificat...

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conference on signal processing, sensor fusion, and target recognition xxii

作者： Kupiec, Stephen Markov, Vladimir Chavez, Joseph Adv Syst & Technol 23 Mauchly 109 Irvine CA 92618 USA

ISBN: (纸本)9780819495365

Effective fusion of multi-parametric heterogeneous data is essential for better object identification, characterization and discrimination. In this report we discuss a practical example of fusing the data provided by imaging and non-imaging electro-optic sensors. The proposed approach allows the processing, integration and interpretation of such data streams from the sensors. Practical examples of improved accuracy in discriminating similar but non-identical objects are presented.

关键词： data fusion heterogeneous data fusion Markov process electro-optic sensors

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Interactive target recognition in images using machine-learning techniques

Interactive target recognition in images using machine-learn...

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conference on signal processing, sensor fusion, and target recognition XX

作者： Michaeli, Ariel Camon, Irit RAFAEL Advanced Defense Systems Ltd. (Israel)

ISBN: (纸本)9780819486240

Imagery analysis systems utilize Automatic target recognition (ATR) methods in order to improve the accuracy of human-based analysis and save time. Often, ATR methods perform poorly in obtaining these objectives, due to reliance on outdated prior information, while human operators possess updated information that remains unused. This paper presents an interactive target recognition (or ITR) application. The operator marks sample target pixels by an intuitive user-interface. Then machine-learning techniques generate algorithms tailored for their recognition in imagery. The resulting detection map is dynamically controlled by the operator, suiting his needs. The application enables target recognition in zero prior information environments.

关键词： ATR Interactive target recognition Machine Learning User interface sensor fusion

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Bayesian cluster detection and tracking using a generalized Cheeseman approach

Bayesian cluster detection and tracking using a generalized ...

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conference on signal processing, sensor fusion, and target recognition XII

作者： Mahler, R Lockheed Martin Tactical Syst Eagan MN USA

ISBN: (纸本)0819449563

Cluster tracking is the problem of detecting and tracking clustered formations of large numbers of targets, without necessarily being obligated to track each and every individual target. We address this problem by generalizing to the dynamic case a static Bayesian finite-mixture data-clustering approach due to P. Cheeseman. After summarizing Cheeseman's approach, we show that it implicitly draws on random set theory. Making this connection explicit allows us to incorporate it into a multitarget recursive Bayes filter, thereby leading to a rigorous Bayesian foundation for finite-mixture cluster tracking. A computational approach is proposed, based on an approximate, multitarget first-order moment filter ("cluster PHD" filter).

关键词： Digital filtering target detection Electronic filtering Filtering (signal processing) Data modeling Statistical modeling Detection and tracking algorithms sensor fusion signal processing target recognition

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Hierarchical multifeature integration for automatic target recognition

Hierarchical multifeature integration for automatic target r...

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conference on signal processing, sensor fusion, and target recognition VIII

作者： Shah, S Aggarwal, JK Wayne State Univ Comp Vis Lab Dept Elect & Comp Engn Detroit MI 48202 USA

ISBN: (纸本)081943194X

This paper presents a methodology for object recognition in complex scenes by learning multiple feature object representations in second generation Forward Looking InfraRed (FLIR) images. A hierarchical recognition framework is developed which solves the recognition task by performing classification using decisions at the lower levels and the input features. The system uses new algorithms for detection and segmentation of objects and a Bayesian formulation for combining multiple object features for improved discrimination. Experimental results on a large database of FLIR images is presented to validate the robustness of the system, and its applicability to FLIR imagery obtained from real scenes.

关键词： target recognition sensor fusion Bayesian classification

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fusion-based methods for target identification in the absence of quantitative classifier confidence

Fusion-based methods for target identification in the absenc...

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conference on signal processing, sensor fusion, and target recognition VI

作者： Llinas, J SUNY/Buffalo (United States)

ISBN: (纸本)0819424838

In an era of reduced defense budgets, there is increased pressure to reuse any available technology or capability to the extent possible. For data fusion applications, this requirement can lead to situations where the output of disparate individual algorithms would like to be fused;ideally, this would be done in the most quantitative way possible. This paper reviews, integrates, and comments on various prior works in both the data fusion, remote sensing, and character recognition communities which are helpful to the data fusion algorithm/process designer dealing, in particular, with target identification and classification problems. It is shown that generalized voting and rank-based methods may be useful in these cases;the issue of source reliability is also addressed and methods for incorporating assigned reliabilities are described.

关键词： data fusion sensor fusion target identification target classification ranking methods voting methods

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Consumer-oriented social data fusion: controlled learning in social environments, social advertising and more

Consumer-oriented social data fusion: controlled learning in...

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conference on signal processing, sensor fusion, and target recognition xxii

作者： Grewe, L. Calif State Univ East Bay Dept Comp Sci Hayward CA 94542 USA

ISBN: (纸本)9780819495365

This paper explores the current practices in social data fusion and analysis as it applies to consumer-oriented applications in a slew of areas including business, economics, politics, sciences, medicine, education and more. A categorization of these systems is proposed and contributions to each area are explored preceded by a discussion of some special issues related to social data and networks. From this work, future paths of consumer-based social data analysis research and current outstanding problems are discovered.

关键词： Social Network Analysis Social Data Analysis

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