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Conference on sensor fusion - architectures, algorithms, and applications iiI

作者： Malhotra, RP Yufik, YM USAF Res Lab Sensors Directorate Wright Patterson AFB OH 45433 USA

ISBN: (纸本)0819431931

We discuss Virtual Associative Networks (VANs) emd their relevance for addressing computationally prohibitive sensor fusion problems (with results in Dynamic sensor Management). To our knowledge, this discussion of VAN technology for sensor fusion is unique and our current result involving VANs for Dynamic sensor Management is the first of its kind. The following provides methodology, results, and extensions.

关键词： neurocomputation cognitive science sensor fusion sensor Management neural networks

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Feature level sensor fusion

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3719卷 332-338页

作者： Peli, Tamar Young, Mon Knox, Robert Ellis, Ken Bennett, Fredrick Atlantic Aerospace Electronics Corp Waltham United States

This paper describes two practical fusion techniques (hybrid fusion and cued fusion) for automatic target cueing that combine features derived from each sensor data at the object-level. In the hybrid fusion method each of the input sensor data is prescreened (i.e. Automatic Target Cueing (ATC) is performed) before the fusion stage. The cued fusion method assumes that one of the sensors is designated as a primary sensor, and thus ATC is only applied to its input data. If one of the sensors exhibits a higher Pd and/or a lower false alarm rate, it can be selected as the primary sensor. However, if the ground coverage can be segmented to regions in which one of the sensors is known to exhibit better performance, then the cued fusion can be applied locally/adaptively by switching the choice of a primary sensor. Otherwise, the cued fusion is applied both ways (each sensor as primary) and the outputs of each cued mode are combined. Both fusion approaches use a back-end discrimination stage that is applied to a combined feature vector to reduce false alarms. The two fusion processes were applied to spectral and radar sensor data and were shown to provide substantial false alarm reduction. The approaches are easily extendable to more than two sensors.

关键词： sensor data fusion

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Implementing Real-Time Imaging Systems Using the Sarnoff Acadia ii Vision Processor

Implementing Real-Time Imaging Systems Using the Sarnoff Aca...

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

作者： Berends, David van der Wal, Gooitzen Sarnoff Corp Princeton NJ 08543 USA

ISBN: (纸本)9780819481740

Vision system designers often face the daunting challenge of implementing powerful image processing capabilities in severely size, weight and power constrained systems. Multi-sensor fusion, image stabilization, image enhancement, target detection and object tracking are fundamental processing techniques required by UAVs (Unmanned Aerial Vehicles), smart cameras, weapon sights, and vehicle situational awareness systems. All of these systems also process non-vision data while communicating large amounts of information elsewhere. To meet their demanding requirements, Sarnoff developed the Acadia (R) ii System-on-a- Chip, combining dedicated image processing cores, four ARM (R) 11 processors and an abundance of peripherals in a single Integrated Circuit. This paper will describe how to best use the power of the Acadia (R) ii as both an all-in-one image processor and as a general purpose computer for performing other critical non-vision tasks, such as flight control and system-to-system communication.*

关键词： fusion stabilization image enhancement size weight and power constrained systems target detection object tracking system-on-a-chip situational awareness

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Objective pixel-level image fusion performance measure

Objective pixel-level image fusion performance measure

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Conference on sensor fusion: architectures, algorithms, and applications IV

作者： Xydeas, C Petrovic, V Univ Lancaster Dept Commun Syst Lancaster LA1 4YR England

ISBN: (纸本)0819436771

This paper addresses the issue of objectively measuring the performance of pixel level image fusion systems. The proposed fusion performance metric models the accuracy with which visual information is transferred from the input images to the fused image. Experimental results clearly indicate that the metric is perceptually meaningful.

关键词： Image fusion Image processing Information visualization Visualization Image quality Wavelets Coherence (optics) Computing systems Image analysis Image filtering

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Data level fusion using common symmetry set

Data level fusion using common symmetry set

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Conference on sensor fusion - architectures, algorithms, and applications iiI

作者： Tari, S Middle E Tech Univ Dept Engn Sci TR-06531 Ankara Turkey

ISBN: (纸本)0819431931

Availability of different imaging modalities requires techniques to process and combine information from different images of the same phenomena. We present a symmetry based approach for combining information from multiple images. fusion is performed at data level. Actual object boundaries and shape descriptors are recovered directly from raw sensor output(s). Method is applicable to arbitrary number of images in arbitrary dimension.

关键词： Image fusion Data fusion Image segmentation sensors Image processing Information fusion Data processing Shape analysis 3D image processing Image analysis

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architectures, algorithms, and applications Using Bayesian Networks

Architectures, Algorithms, and Applications Using Bayesian N...

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Conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Kingsbury, Todd Gen Dynam Adv Informat Syst San Diego CA 92108 USA

ISBN: (纸本)9780819486387

A Bayesian network is a tree structure where each branch represents a classification candidate. The leaves of the tree represent observable target features such as frequency or length. An optimized tree groups similar features together, e.g. frequency and pulse width, while collecting dissimilar or disparate information, e. g. spectral and kinematics, all within the same unifying structure. A vehicular track then is a subset of the a priori candidate library and contains only feasible branches. The algorithm for updating the confidence of each feasible candidate according to Bayes' rule is embedded in each track, as is the ability of a track to learn, apply a priori probability distributions, switch modes, switch among kinematics models, apply tracking history to classification and apply classification history to tracking, and support multisensor correlation and sensor fusion.

关键词： Bayesian tracking classification sensor fusion

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Collaborative Wireless sensor Networks: architectures, algorithms and applications

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INFORMATION fusion 2015年 22卷 1-2页

作者： Fortino, Giancarlo Bal, Mert Li, Wenfeng Shen, Weiming Univ Calabria DIMES I-87036 Arcavacata Di Rende CS Italy Miami Univ Dept Engn Technol Hamilton OH 45011 USA Wuhan Univ Technol Sch Logist Engn Wuhan 430063 Peoples R China Natl Res Council Canada London ON N6G 4X8 Canada

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Optimal features in feature out (FEI-FEO) fusion for decisions in multisensor environments

Optimal features in feature out (FEI-FEO) fusion for decisio...

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Conference on sensor fusion - architectures, algorithms, and applications ii

作者： Belur, SV Dasarathy, BV Comp Sci Corp Dept Appl Res Syst Sci Div Lanham MD 20706 USA

ISBN: (纸本)0819428256

The study presents a formal methodology to the problem of feature level fusion, that had been previously addressed in the literature mostly in an ad hoc manner on a case by case basis only. The input set of features extracted from multiple sensors (data sources) are optimally fused to derive a synthetic feature so as to enhance the effective discrimination potential among the defined set of decision classes. This features in - feature out (FEI-FEO)' fusion process, unlike most other fusion schemes reported in the literature, is designed through a formal learning phase in which an optimal mapping from the multisensor derived feature space to a single unified feature is developed. This learning, accomplished through a new composite random and deterministic search based optimization teal, defines the transformation for the FEI-FEO process. This transformation is applied to the multi-sensor generated feature sets in the operational phase to derive the fused feature values corresponding to the objects under observation. The corresponding classification decisions are made on the basis of relative closeness of these feature values to the different class mean values in the transformed single dimensional feature space. The new methodology has been implemented in MATLAB which, being a vector/matrix oriented language, is an ideal candidate for solving problems in patter recognition and learning. The method is applied to well-known data sets available on the web for testing pattern recognition algorithms to assess its effectiveness relative to the traditional classification methods from both conceptual as well as computational view points.

关键词： feature level fusion feature optimization features in - feature out fusion feature synthesis

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Bayesian Fault Detection and Localization Through Wireless sensor Networks in Industrial Plants

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IEEE INTERNET OF THINGS JOURNAL 2024年第8期11卷 13231-13246页

作者： Tabella, Gianluca Ciuonzo, Domenico Paltrinieri, Nicola Rossi, Pierluigi Salvo SINTEF Energy Res Dept Gas Technol N-7034 Trondheim Norway Norwegian Univ Sci & Technol Dept Elect Syst N-7034 Trondheim Norway Univ Naples Federico II Dept Elect Engn andInformat Technol I-80138 Naples Italy Norwegian Univ Sci & Technol Dept Mech & Ind Engn N-7034 Trondheim Norway

This work proposes a data fusion approach for quickest fault detection and localization within industrial plants via wireless sensor networks. Two approaches are proposed, each exploiting different network architectures. In the first approach, multiple sensors monitor a plant section and individually report their local decisions to a fusion center (FC). The FC provides a global decision after spatial aggregation of the local decisions. A post-processing center subsequently processes these global decisions in time, which performs quick detection and localization. Alternatively, the FC directly performs a spatiotemporal aggregation directed at quickest detection, together with a possible estimation of the faulty item. Both architectures are provided with a feedback system where the network's highest hierarchical level transmits parameters to the lower levels. The two proposed approaches model the faults according to a Bayesian criterion and exploit the knowledge of the reliability model of the plant under monitoring. Moreover, adaptations of the well-known Shewhart and CUSUM charts are provided to fit the different architectures and are used for comparison purposes. Finally, the algorithms are tested via simulation on an active Oil and Gas subsea production system, and performances are provided.

关键词： Data fusion fault detection Industry 4.0 localization monitoring quickest detection reliability wireless sensor network (WSNs)

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Data fusion and processing for airborne multichannel system of radar remote sensing: methodology, stages, and algorithms

Data fusion and processing for airborne multichannel system ...

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Conference on sensor fusion: architectures, algorithms, and applications IV

作者： Lukin, VV Astola, JT Melnik, VP Kurekin, AA Zelensky, AA Kulemin, GP Ponomarenko, NN Dolia, AN Parkkinen, J State Aerosp Univ UA-61070 Kharkov Ukraine

ISBN: (纸本)0819436771

Methodology and stages of data processing in multichannel airborne radar imaging systems are considered. It is shown that data fusion in such systems requires special techniques, algorithms, and software for image processing and information retrieval. Some approaches and methods are proposed. The results are demonstrated for simulated and real images.

关键词： Image filtering Remote sensing Digital filtering Image processing Radar Data processing Image classification Optical filters Ka band Multichannel imaging systems

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