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

共 306 条记录，以下是81-90 订阅

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ELIPS: Toward a sensor fusion processor on a chip

ELIPS: Toward a sensor fusion processor on a chip

引用

conference on sensor fusion - architectures, algorithms, and applications III

作者： Daud, T Stoica, A Thomas, T Li, WT Fabunmi, J CALTECH Jet Prop Lab Pasadena CA 91109 USA

ISBN: (纸本)0819431931

The paper presents the concept and initial tests from the hardware implementation of a low-power, high-speed reconfigurable sensor fusion processor. The Extended Logic Intelligent Processing System (ELIPS) processor is developed to seamlessly combine rule-based systems, fuzzy logic, and neural networks to achieve parallel fusion of sensor in compact low power VLSI. The first demonstration of the FLIPS concept targets interceptor functionality;other applications, mainly in robotics and autonomous systems are considered for the future. The main assumption behind ELIPS is that fuzzy, rule-based and neural forms of computation can serve as the main primitives of an "intelligent" processor. Thus, in the same way classic processors are designed to optimize the hardware implementation of a set of fundamental operations, ELIPS is developed as an efficient implementation of computational intelligence primitives, and relies on a set of fuzzy set, fuzzy inference and neural modules, built in programmable analog hardware. The hardware programmability allows the processor to reconfigure into different machines, taking the most efficient hardware implementation during each phase of information processing. Following software demonstrations on several interceptor data three important ELIPS building blocks (a fuzzy set preprocessor, a rule-based fuzzy system and a neural network) have been fabricated in analog VLSI hardware and demonstrated microsecond-processing times.

关键词： sensor fusion hardware sensor fusion processor fuzzy expert system neural networks reconfigurable hardware

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Multi-Layered Context Impact Modulation for Enhanced Focus of Attention of Situational Awareness in Persistent Surveillance Systems

Multi-Layered Context Impact Modulation for Enhanced Focus o...

引用

conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2010

作者： Shirkhodaie, Amir Rababaah, Haroun Tennessee State Univ Dept Mech & Mfg Engn Ctr Excellence Battlefield Sensor Fus Nashville TN 37209 USA

ISBN: (纸本)9780819481740

This paper presents a Multi-Layered Context Impact Modulation (MCIM) technique for persistent surveillance systems (PSS) and discusses its layered architecture for different context modulations including: spatial, temporal, sensor reliability, human presence, and environmental modulations. This paper also presents a fusion model for enhancement of focus of attention at the common operation picture (COP). The fusion model combines all the impacts from the different MCIM layers onto one unified modulated map. To test and evaluate the performance of MCIM, several experiments were conducted to modulate interaction of humans and vehicles which exhibit various normal and suspicious behaviors. The experimental results show strength of this approach in correctly modulating different suspicious situations with higher degree of certainty.

关键词： Persistent Surveillance Systems Situation Assessment Context Impact Layered Modulation Energy Logic fusion Engine Large-scale sensor Networks Soft fusion

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To fuse or not to fuse: Fuser versus best classifier

To fuse or not to fuse: Fuser versus best classifier

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conference on sensor fusion - architectures, algorithms, and applications II

作者： Rao, NSV Oak Ridge Natl Lab Oak Ridge TN 37831 USA

ISBN: (纸本)0819428256

A sample from a class defined on a finite-dimensional Euclidean space and distributed according to an unknown distribution is given. We are given a set of classifiers each of which chooses a hypothesis with least misclassification error from a family of hypotheses. We address the question of choosing the classifier with the best performance guarantee versus combining the classifiers using a fuser. We first describe a fusion method based on isolation property such that the performance guarantee of the fused system is at least as good as the best of the classifiers. For a more restricted case of deterministic classes, we present a method based on error set estimation such that the performance guarantee of fusing all classifiers is at least as good as that of fusing any subset of classifiers.

关键词： classification finite sample analysis distributed detection fusion of classifiers

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Combination of fuzzy sets and Dempster-Shafer theories in forest map updating using multispectral data

Combination of fuzzy sets and Dempster-Shafer theories in fo...

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conference on sensor fusion - architectures, algorithms, and applications V

作者： Leduc, F Solaiman, B Cavayas, F ENST Bretagne Dept ITI F-29285 Brest France

ISBN: (纸本)0819440809

This paper explains a new approach to change detection and interpretation in a context of forest map updating. In this temporal change analysis we use a data set composed of map at time To and a satellite image at time T-1 and we refer to this as a mixed fusion approach. The analysis of remotely sensed data always necessitates the use of approximate reasoning. For this purpose, we use fuzzy logic to evaluate the objects' membership values to the considered classes and the Dempster-Shafer theory to analyse the confusion between classes and to find the more evident class to which an object belong.

关键词： map updating forestry change detection fuzzy logic Dempster-Shafer theory data fusion

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Information fusion for onboard and offboard avionics

Information fusion for onboard and offboard avionics

引用

conference on sensor fusion - architectures, algorithms, and applications II

作者： Kurien, T Chao, A Gully, SW Wood, A ALPHATECH Inc Burlington MA 01803 USA

ISBN: (纸本)0819428256

This paper defines and demonstrates an all-source information fusion system for combining onboard and offboard data and maintain continuous track on targets. We provide an architecture containing an offboard data processor to extract data relevant to the attack aircraft mission, and a set of fusion modules for recursively associating sensor reports, tracking targets, and classifying targets. These modules are derived from a well-posed mathematical formulation which enables us to define precise interfaces among the fusion modules. This approach provides three benefits. First, it enables us to construct a fusion algorithm with close-to-optimal target tracking and classification performance. Second, it allows us to study new fusion algorithms by implementing alternate algorithms for each module. Third, it allows us to process data from any combination of sensors making the architecture applicable to a variety of attack aircraft and missions. We show that the proposed system can increase a pilot's situational awareness by providing him with a clearer battlefield picture consistent with attack aircraft mission objectives. Results for a simple but realistic air-to-ground scenario simulation demonstrate the benefits of fusing data from offboard and onboard sensors.

关键词： all source fusion Multiple Hypothesis Tracking (MHT) image gee-registration continuous tracking onboard-offboard fusion Kalman filtering

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The system parameter fusion principle and its application to fuzzy pattern recognition

The system parameter fusion principle and its application to...

引用

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

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

ISBN: (纸本)0819449598

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 and its application to fuzzy pattern recognition.

关键词： sensor fusion fuzzy pattern recognition computer monitoring systems computer control systems realtime expert systems

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The application of spatial likelihood functions to multi-camera object localization

The application of spatial likelihood functions to multi-cam...

引用

conference on sensor fusion - architectures, algorithms, and applications V

作者： Aarabi, P Stanford Univ Dept Comp Sci Stanford CA 94305 USA

ISBN: (纸本)0819440809

The benefits and problems of a multiple-camera object localization system utilizing Spatial Likelihood Functions (SLF) are explored. This method utilizes the angular extent of objects perceived by different cameras in order to find the region in which they intersect. This region will ideally correspond to the original location of the objects. It is shown that as long as the number of cameras is greater than the number of objects, an efficient camera fusion algorithm utilizing SLFs can be successfully employed to localize the objects. In certain situations, especially with a greater number of objects than cameras, false objects will appear among the correctly localized objects. Several different techniques to identify and remove the false objects are proposed, including a heuristic-based ray tracing approach and other multi-modal techniques. The effectiveness of the camera fusion and false object removal approaches are illustrated in the context of several examples.

关键词： visual object localization multi-camera image fusion

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Proceedings of SPIE: Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2003

Proceedings of SPIE: Multisensor, Multisource Information Fu...

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Multisensor. Multisource Information fusion: architectures, algorithms, and applications 2003

This Volume 5099 of the conference proceedings contains 45 papers. Topics discussed include classification and decision fusion, image level fusion, approximate reasoning methodologies, estimation and tracking, fusion methodologies, evolving concepts and methodologies, architectures and related topics, industrial, medical and speech applications, defense applications, sensor/resource management and related topics.

关键词： sensor data fusion

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Decision fusion in sensor Networks for Spectrum Sensing based on Likelihood Ratio Tests

Decision Fusion in Sensor Networks for Spectrum Sensing base...

引用

conference on Advanced Signal Processing algorithms, architectures, and Implementantions XviII

作者： Chung, Wei-Ho Yao, Kung Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA

ISBN: (纸本)9780819472946

sensor networks have been shown to be useful in diverse applications. One of the important applications is the collaborative detection based on multiple sensors to increase the detection performance. To exploit the spectrum vacancies in cognitive radios, we consider the collaborative spectrum sensing by sensor networks in the likelihood ratio test (LRT) frameworks. In the LRT, the sensors make individual decisions. These individual decisions are then transmitted to the fusion center to make the final decision, which provides better detection accuracy than the individual sensor decisions. We provide the lowered-bounded probability of detection (LBPD) criterion as an alternative criterion to the conventional Neyman-Pearson (NP) criterion. In the LBPD criterion, the detector pursues the minimization of the probability of false alarm while maintaining the probability of detection above the pre-defined value. In cognitive radios, the LBPD criterion limits the probabilities of channel conflicts to the primary users. Under the NP and LBPD criteria, we provide explicit algorithms to solve the LRT fusion rules, the probability of false alarm, and the probability of detection for the fusion center. The fusion rules generated by the algorithms are optimal under the specified criteria. In the spectrum sensing, the fading channels influence the detection accuracies. We investigate the single-sensor detection and collaborative detections of multiple sensors under various fading channels, and derive testing statistics of the LRT with known fading statistics.

关键词： sensor networks cognitive radio lower-bounded probability of detection criterion Neyman-Pearson criterion decision fusion

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fusion of imaging and non-imaging data for surveillance aircraft

Fusion of imaging and non-imaging data for surveillance airc...

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conference on sensor fusion - architectures, algorithms, and applications

作者： Shahbazian, E Gagnon, L Duquet, JR Macieszczak, M Valin, P Lockheed Martin Canada (Canada)

ISBN: (纸本)081942482X

This paper describes a phased incremental integration approach for application of image analysis and data fusion technologies to provide automated intelligent target tracking and identification for Airborne Surveillance on board an Aurora Maritime Patrol Aircraft. The sensor suite of the Aurora consists of a radar, an Identification Friend or Foe (IFF) system, an Electronic Support Measures (ESM) system, a Spotlight Synthetic Aperture Radar (SSAR), a Forward Looking Infra-Red (FLIR) sensor and a Link-11 tactical datalink system. Lockheed Martin Canada (LMCan) is developing a testbed, which will be used to analyze and evaluate approaches for combining the data provided by the existing sensors, which were initially not designed to feed a fusion system. Three concurrent research proof-of-concept activities provide techniques, algorithms and methodology into three sequential phases of integration of this testbed. These activities are: (a) analysis of the fusion architecture (track/contact/hybrid) most appropriate for the type of data available, (b) extraction and fusion of simple features from the imaging data into the fusion system performing automatic target identification, and (c) development of a unique software architecture which will permit integration and independent evolution, enhancement and optimization of various decision aid capabilities, such as Multi-sensor Data fusion (MSDF), Situation and Threat Assessment (STA) and Resource Management (RM).

关键词： intelligent decision aids data fusion image analysis automatic target recognition real-time adaptive planning situation and threat assessment resource management

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