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检索条件"任意字段=Sensor Fusion: Architectures, Algorithms and Applications V"

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Research on range observability of 2-dimensional motion target based-on bearing measurements of IR sensor

Research on range observability of 2-dimensional motion targ...

引用

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

作者： Yang, GS Chen, J Dou, LH Hou, CZ Beijing Inst Technol Dept Automat Control Beijing 100081 Peoples R China

ISBN: (纸本)0819444812

Considering the target traveling with a constant acceleration in the 2-dimentional space, a mathematical observability criterion of the target states is established in this paper. In the middle of obtaining the criterion, first, the non-linear bearing measurement equation is changed into linear framework by subtly making use of some mathematical transformations. Next, a high-order non-linear differential equation is converted into a set of one-order linear differential equations by utilizing some characteristics of the full-rank matrix. At the end of this paper, the observability criterion given by some previous researchers for the target traveling in a 2-dimentional space with a constant velocity is directly deduced from the criterion presented by this paper.

关键词： observability criterion single station passive locating and tracking bearing-only measurement differential equation full-rank matrix

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Data fusion approach to the threat assessment for the radar resources management

Data fusion approach to the threat assessment for the radar ...

引用

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

作者： Komorniczak, W Pietrasiniski, J Solaiman, B Mil Univ Technol Fac Elect PL-00908 Warsaw Poland

ISBN: (纸本)0819444812

The paper deals with the problem of the multifunction radar resources management. The problem consists of target/tasks ranking and tasks scheduling. The paper is focused on the target ranking, with the data fusion approach. The data from the radar (object's velocity, range, altitude, direction etc.), IFF system (Identification Friend or Foe) and ESM system (Electronic Support Measures - information concerning threat's electro-magnetic activities) is used to decide of the importance assignment for each detected target. The main problem consists of the multiplicity of various types of the input information. The information from the radar is of the probabilistic or ambiguous imperfection type and the IFF information is of evidential type. To take the advantage of these information sources the advanced data fusion system is necessary. The system should deal with the following situations: fusion of the evidential and fuzzy information, fusion of the evidential information and a priori information. The paper describes the system which fuses the fuzzy and the evidential information without previous change to the same type of information. It is also described the proposal of using of the dynamic fuzzy qualifiers. The paper shows the results of the preliminary system's tests.

关键词： multifunction radar resources management priority assignment

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Ontology-based multi-agent approach to data fusion

Ontology-based multi-agent approach to data fusion

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

作者： Obitko, M Smid, J Czech Tech Univ Fac Elect Engn Dept Cybernet CR-16635 Prague Czech Republic

ISBN: (纸本)0819444812

In this paper, we describe an ontology based multi-agent approach to data fusion from heterogeneous data sources. We assume that the data can come from various sensors or databases. In our approach, each data source is handled by one agent. The agent is able to deliver the data and the description of the data. The data description is provided in the form of specialized ontology. This description is the basis for fusion and integration of data from different sources or agents. A specialized agent fuses the data by evaluating these descriptions under the given query. Since data source agents themselves maintain the description of the data, the whole system of data sources is extensible. This means that a new data source agent may enter the system of data sources at any time. After proper registration with the fusion agent, a new agent can contribute to the overall process. The fusion agent is requested to provide data from different sources with type and location specification. The possible process outcomes are fused data, transformation rules or a failure message. After finding fusion rules, the agent is able to provide data continuously. In our examples of ontologies, we describe primarily numerical data sources and their relations, however symbolic data fusion is also considered.

关键词： data fusion data integration ontology multi-agent system multiple sources

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Factoring in a priori classifier performance into decision fusion

Factoring in a priori classifier performance into decision f...

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

作者： Goebel, K Mysore, SP GE Co Corp Res & Dev Niskayuna NY USA

ISBN: (纸本)0819444812

In this paper we present methods to enhance the classification rate in decision fusion with partially redundant information by manipulating the input to the fusion scheme using a priori performance information. Intuitively, it seems to make sense to trust a more reliable tool more than a less reliable one without discounting the less reliable one completely. For a multi-class classifier, the reliability per class must be considered. In addition, complete ignorance for any given class must also be factored into the fusion process to ensure that all faults are equally well represented. However, overly trusting the best classifier will not permit the fusion tool to achieve results that rate beyond the best classifiers performance. We assume that the performance of classifiers to be fused is known, and show how to take advantage of this information. In particular, we glean pertinent performance information from the classifier confusion matrices and their cousin, the relevance matrix. We further demonstrate how to integrate a priori performance information within an hierarchical fusion architecture. We investigate several schemes for these operations and discuss the advantages and disadvantages of each. We then apply the concepts introduced to the diagnostic realm where we aggregate the output of several different diagnostic tools. We present results motivated from diagnosing on-board faults in aircraft engines.

关键词： classification diagnostics information fusion decision fusion a priori information confusion matrix

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Generation and fusion of multiple image representations in automatic corrosion detection algorithms used with shipboard tank video inspection systems

Generation and fusion of multiple image representations in a...

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

作者： Nelson, BN Slebodnick, P Lemieux, EJ Krupa, M Preisser, R Lucas, K GEO CTRS INc Newton Ctr MA 02459 USA

ISBN: (纸本)0819444812

Coatings damage in shipboard tanks is presently assessed using "Certified Coatings Inspectors". Prior to a coatings inspector entering a tank, the tank must be emptied and certified "gas free". These requirements combined with the limited number of certified coatings inspectors available at shipyards and Naval Bases significantly increases the cost and the logistical requirements associated with performing shipboard tank inspections. There is additionally significant variation in damage assessments made by different inspectors. To overcome these difficulties, the Naval Research Laboratory has developed two video inspection systems that obviate requirements for both certifying tanks gas free and for emptying the tank prior to performing an inspection. These systems also obviate requirements for inspector presence during tank inspections. The Naval Research Laboratory has also developed an automatic corrosion detection algorithm. The corrosion detection algorithm currently employs two independent algorithms that individually assess the tank coatings damage. The independent damage assessments are than fused to attain a single coatings damage value. In testing performed to date, it has been shown that the corrosion detection algorithm significantly reduces the effect of inspector-to-inspector variability and provides an accurate assessment of tank coatings damage. This in turn makes it significantly easier to prioritize ship maintenance.

关键词： information fusion image processing automatic corrosion detection

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Electro-optical and SAR image fusion for improvements on target feature estimation

Electro-optical and SAR image fusion for improvements on tar...

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5th International Conference on applications of Photonic Technology (ICAPT 2002)

作者： Lampropoulos, GA Li, YF Secker, J Sévigny, L Beaudoin, A AUG Signals Ltd Toronto ON M4V 1K7 Canada

ISBN: (纸本)0819446122

In this paper, tradeoff studies on several pixel level fusion algorithms and on their performance evaluation criteria are presented. Electro-optical (EO) and Synthetic Aperture Radar (SAR) sensors are dissimilar and produce images with very low degree of correlation. These images are initially registered at subpixel level accuracy. The fusion is performed using the following pixel level fusion algorithms: Principal Component Analysis (PCA), Averaging (Ave), Laplacian Pyramid, Filter Subtract Decimate (FSD), Ratio Pyramid, Contrast Pyramid, Gradient Pyramid, Discrete Wavelet Transform (DWT), Shift Invariant DWT (SIDWT) with Haar, Morphological Pyramid, and the recent image fusion method developed by A.U.G. Signals Ltd. A MATLAB based dedicated image fusion toolbox, that includes several pixel level fusion, restoration and registration algorithms, has been recently developed by A.U.G. Signals. This toolbox is used for the tradeoff studies.

关键词： multi-sensor pixel level fusion feature extraction detection

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Neural networks estimation of truck static weights by fusing weight-in-motion data

Neural networks estimation of truck static weights by fusing...

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5th International Conference on Information fusion (fusion 2002)

作者： Mangeas, M Glaser, S Dolcemascolo, v LCPC INRETS LIVIC F-78000 Versailles France

ISBN: (纸本)0972184414

This paper presents a complete neural networks approach developed to improve the accuracy of multiple-sensor Weigh-In-Motion system. This system consists in fusing the dynamical measurements of individual sensors installed in the road, into one improved estimate of static gross or axle weight. This task is complex due to the difficulty to inverse the model that describes the dynamical vehicle-pavement interactions. The sensors are also difficult to calibrate and remain sensitive to the environmental conditions. We chose to model the data with neural networks named 'general feedforward neural networks'. This class of neural nets includes in particular the famous Multilayer Perceptron model used in many applications. In order to increase the accuracy and the generalization capacity of this neural model, we also use an automatic method of model selection based on genetic algorithms. Simulated traffic data, computed with a realistic model based on vehicle-pavement interaction, indicated that a neural network can produce results with a higher degree of accuracy than the simple linear regression method. Moreover, our results show that the fusion of several sensors significantly improved the estimation of the static weight, much more in the frame of non linear modeling than in. the frame of linear one. In our simulation, the optimal number of sensors to fuse stands around 7.

关键词： multisensor fusion neural networks genetic algorithms nonlinear estimation real world application

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Neural networks estimation of truck static weights by fusing weight-in-motion data

Neural networks estimation of truck static weights by fusing...

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International Conference on Information fusion

作者： M. Mangeas S. Glaser v. Dolcemascolo LIVIC Satory France DESE Paris France

This paper presents a complete neural networks approach developed to improve the accuracy of multiple-sensor Weigh-In-Motion system. This system consists in fusing the dynamical measurements of individual sensors installed in the road, into one improved estimate of static gross or axle weight. This task is complex due to the difficulty to inverse the model that describes the dynamical vehicle-pavement interactions. The sensors are also difficult to calibrate and remain sensitive to the environmental conditions. We chose to model the data with neural networks named 'general feedforward neural networks'. This class of neural nets includes in particular the famous Multilayer Perceptron model used in many applications. In order to increase the accuracy and the generalization capacity of this neural model, we also use an automatic method of model selection based on genetic algorithms. Simulated traffic data, computed with a realistic model based on vehicle-pavement interaction, indicated that a neural network can produce results with a higher degree of accuracy than the simple linear regression method. Moreover, our results show that the fusion of several sensors significantly improved the estimation of the static weight, much more in the frame of non linear modeling than in the frame of linear one. In our simulation, the optimal number of sensors to fuse stands around 7.

关键词： Neural networks Feedforward neural networks sensor fusion sensor systems Roads Axles Inverse problems Multi-layer neural network Multilayer perceptrons Genetic algorithms

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Information fusion concepts. From information elements definition to the application of fusion approaches

Information fusion concepts. From information elements defin...

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

作者： Solaiman, B Ecole Natl Super Telecommun Bretagne Dept ITI F-29285 Brest France

ISBN: (纸本)0819440809

The aim of this gaper is to give a homogeneous and a simple framework in order to present information fusion concepts. The main concept presented here concerns the definition of the information element concept This concept is then illustrated through the general scheme of pattern recognition systems. Different types of information imperfection are then illustrated. Finally, information fusion concepts and fusion architectures are illustrated

关键词： information element data fusion decision fusion model fusion

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The equivalence of Bayesian multi-sensor information fusion and neural networks

The equivalence of Bayesian multi-sensor information fusion ...

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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

This paper proposes a Bayesian multi-sensor object localization approach that keeps track of the observability of the sensors in order to maximize the accuracy of the final decision. This is accomplished by adaptively monitoring the mean-square-error of the results of the localization system. Knowledge of this error and the distribution of the system's object localization estimates allow the result of each sensor to be scaled and combined in an optimal Bayesian sense. It is shown that under conditions of normality, the Bayesian sensor fusion approach is directly equivalent to a single layer neural network with a sigmoidal non-linearity. Furthermore, spatial and temporal feedback in the neural networks can be used to compensate for practical difficulties such as the spatial dependencies of adjacent positions. Experimental results using 10 binary microphone arrays yield an order of magnitude improvement in localization error for the proposed approach when compared to previous techniques.

关键词： object localization sensor fusion neural networks sound localization

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