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

共 433 条记录，以下是1-10 订阅

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Proceedings of SPIE - signal processing, sensor fusion, and target recognition xv

Proceedings of SPIE - Signal Processing, Sensor Fusion, and ...

引用

signal processing, sensor fusion, and target recognition xv

ISBN: (纸本)0819462918

The proceedings contain 60 papers. The topics discussed include: observable operator model based joint target tracking and classification;impact time and point prediction: an analysis of the capabilities of a neural extended Kalman filter;a nonlinear filtering and predication (NFP) method for maneuvering target tracking;radar signals dismount tracking for urban operations;multiple target tracking with a steerable airborne video sensor;global motion estimation and target detection with region search;using localized particle subset for target tracking in videos;autonomous search, tracking, and classification by multiple cooperative UAVs;an entropy-based approach to wide area surveillance;a sparse sampling planner for sensor resource management;resource manager for an autonomous coordinated team of UAVs;farsighted sensor management for feature-aided tracking;and a rollout algorithm to coordinate multiple sensor resources to track and discriminate targets.

关键词： signal processing

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Information management and target detection for multisensor airborne platforms

Information management and target detection for multisensor ...

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

作者： Jaeger, Klaus Hebel, Marcus Armbruster, Walter Bers, Karlheinz FOM Res Inst Optron & Pattern Recognit FGAN Gutleuthausstr 1 D-76275 Ettlingen Germany

ISBN: (纸本)0819462918

Future military helicopters will be provided with multiple information sources for self-protection and reconnaissance, e.g. imaging IR, laser radar and GPS. In addition, knowledge bases like maps, aerial images, geographical information (GIS) and other previously acquired data can be used for the interpretation of the current scenario. To support the mission, results of data fusion and information management have to be presented to the pilot in an appropriate way. This paper describes concepts and results of our work on IR and laser data fusion for airborne systems. Data is gathered by forward-looking sensors mounted in a helicopter. For further improvement, fusion with collateral information (laser elevation data, aerial images) is used for change detection and definition of regions of interest with respect to the stored and continuously updated database. Results are demonstrated by the analysis of an exemplary data set, showing a scenario with a group of vehicles. Two moving vehicles are detected automatically in both channels (IR, laser) and the results are combined to achieve improved visualization for the pilot.

关键词： sensor fusion target detection information management knowledge base moving platform

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Fundamentals of the Dempster-Shafer Theory

Fundamentals of the Dempster-Shafer Theory

引用

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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Model-based multi-source fusion for exploitation, classification and recognition

Model-based multi-source fusion for exploitation, classifica...

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

作者： Williams, Wayne Keydel, Eric McCarty, Sean SAIC Ann Arbor Res & Dev Ctr 3600 Green Ct Suite 650 Ann Arbor MI 48105 USA

ISBN: (纸本)0819462918

A model-based multi-sensor fusion framework has previously been developed that supports improved target recognition by fusing target signature information obtained from sensor imagery [1], [2]. Image- based signature features, however, are not the only source of information that may be exploited to advantage by a target recognition system. This paper presents a review of the key features of the model-based fusion framework and shows how it can be expanded to support information derived from imaging sensors as well as data from other non-imaging sources. The expanded model-based multi-source framework supports not only the combination of image data, such as Synthetic Aperture Radar (SAR) and electro-optical (EO), but also various types of non-image data that may be derived from those, or other sensor measurements. The paper illustrates the flexibility of the model-based framework by describing the combination of spatial information from an imaging sensor with scattering characteristics derived from polarimetric phase history data. The multi-source fusion is achieved by relating signature features to specific structural elements on the 3-D target geometry. The 3-D model is used as a sensor neutral, view independent, common reference for the combination of multi-source information.

关键词： multi-sensor fusion exploitation model-based recognition

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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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Image fusion for improved perception

Image fusion for improved perception

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

作者： Ouendeno, M. Kozaitis, S. P. Florida Inst Technol Dept Elect & Comp Engn 150 W Univ Blvd Melbourne FL 32901 USA

ISBN: (纸本)0819462918

We developed a method to fuse imagery for from different sensor types. The core of our method uses different forward transforms of input images and a common transform to reconstruct the final result. When measuring the entropy and power of the fused result, we found that our method gave generally better results when compared to a more conventional approach. Our method could form the basis of a new image fusion approach because it offers results not possible with a conventional approach.

关键词： image fusion image transform wavelet wavelet transform

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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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Tactical Multi sensor fusion (TMSF) an SAIC white paper

Tactical Multi Sensor Fusion (TMSF) an SAIC white paper

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

作者： Henderson, B Short, B Stewart, C SAIC Sterling VA 20164 USA

ISBN: (纸本)081943194X

A software application for correlating and fusing information products from multiple dissimilar sensors is presented. The Tactical Multi sensor fusion (TMSF) system is a C++ object oriented application implementing data correlation and fusion algorithms which provide decision aids for identifying, locating, and determining the status of processing equipment within suspected weapons of mass destruction (WMD) sites. The TMSF system also provides valuable information for assessing weapon delivery accuracy and effectiveness.

关键词： sensors Data fusion Weapons of mass destruction Data modeling Error analysis Interfaces Weapons Image fusion sensor fusion Data processing

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