This paper considers the applicability of algorithms, constraint solving and active structure across the spectrum of complexity of informationfusionapplications. informationfusion is recast as a cognitive applicati...
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ISBN:
(纸本)9780819471659
This paper considers the applicability of algorithms, constraint solving and active structure across the spectrum of complexity of informationfusionapplications. informationfusion is recast as a cognitive application using dynamic structure building and constraint reasoning. The similarity between situation awareness and an undirected structure responding to change is highlighted. The efficiency and speed of operation of cognitive informationfusion are touched on. A tsunami warning system provides an example which involves multiple threat and demonstrates the difference between segmented algorithms making decisions without context, and the active use of knowledge.
The purpose of a tracking algorithm is to associate data measured by one or more (moving) sensors to moving objects in the environment. The state of these objects that can be estimated with the tracking process depend...
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ISBN:
(纸本)0819449598
The purpose of a tracking algorithm is to associate data measured by one or more (moving) sensors to moving objects in the environment. The state of these objects that can be estimated with the tracking process depends on the type of data that is provided by these sensors. It is discussed how the tracking algorithm can adapt itself, depending on the provided data, to improve data association. The core of the tracking algorithm,is an extended Kalman filter using multiple hypotheses for contact to track association. Examples of various sensor suites of radars, electro-optic sensors and acoustic sensors are presented.
A new formalism has been developed that produces detection algorithms for model-based problems, in which one or more parameter values is unknown. Continuum fusion can be used to generate different flavors of algorithm...
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ISBN:
(纸本)9780819486387
A new formalism has been developed that produces detection algorithms for model-based problems, in which one or more parameter values is unknown. Continuum fusion can be used to generate different flavors of algorithm for any composite hypothesis testing problem. The methodology is defined by a fusion logic that can be translated into max/min conditions. Here it is applied to a simple sensor fusion model, but one for which the generalized likelihood ratio test is intractable. By contrast, a fusion-based response to the same problem can be devised that is solvable in closed form and represents a good approximation to the GLR test.
The JDL model for fusion provides a structure for fusion of multispectral data at all levels. Fused data provides improved performance in Automatic Target Recognition (ATR). Critical to the overall fusion performance,...
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ISBN:
(纸本)9780819481740
The JDL model for fusion provides a structure for fusion of multispectral data at all levels. Fused data provides improved performance in Automatic Target Recognition (ATR). Critical to the overall fusion performance, however, is the low level(0-2) fusion of sensory and context information. Loss of information must be avoided at this level, but complexity must be reduced. A model is presented that uses fuzzy sets to form entities and capture the information needed for target recognition. Examples using multi-spectral imagery will be presented.
The objective of this paper is to develop novel classification structures for military targets detection and recognition by employing different fusion techniques. In real applications, the great diversity of materials...
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ISBN:
(纸本)9780819466938
The objective of this paper is to develop novel classification structures for military targets detection and recognition by employing different fusion techniques. In real applications, the great diversity of materials in the background areas and the similarity between the background and target signatures result in high false alarm rates and large miss classification errors. In this paper, three new systems are proposed using different fusion techniques: pixel level fusion, decision fusion, and classification fusion employing confidence vectors. These new developed systems are tested using an experimental data to show its effectiveness.
We address the problem of characterizing uncertainty for multisensor data fusion in a classification problem. To achieve this goal, we model the joint density of given multivariate data using copula functions while al...
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ISBN:
(纸本)9781628416145
We address the problem of characterizing uncertainty for multisensor data fusion in a classification problem. To achieve this goal, we model the joint density of given multivariate data using copula functions while allowing the ability to incorporate any desired marginal distributions, i.e., any desired modalities. The proposed model is data driven in that the corresponding copula functions and their parameters are learned from the data. Our results show that the proposed framework can capture the uncertainties more accurately than current state of the practice, and lead to robust and improved classification performance compared to traditional classifiers.
applications of informationfusion include cases that involve a large number of information sources. Methods developed in the context of few information sources may not, and often do not, scale well to cases involving...
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ISBN:
(纸本)0819449598
applications of informationfusion include cases that involve a large number of information sources. Methods developed in the context of few information sources may not, and often do not, scale well to cases involving a large number of sources. This paper addresses specifically the problem of informationfusion of large number of information sources. Performance of Support Vector Machine (SVM) based approach is investigated in input spaces consisting of thousands of information sources. Microarray pattern recognition, an important bioinformatics task with significant medical diagnostics applications, is considered from the information and sensor data fusion viewpoint, and recognition performance experiments conducted on microarray data are discussed. An approach involving high-dimensional input space partitioning is presented and its efficacy is investigated. The aspects of feature-level and decision-level fusion are discussed as well. The results indicate the feasibility of the SVM based informationfusion with large number of information sources.
Multiple source band image fusion can sometimes be a multi-step process that consists of several intermediate image processing steps. Typically, each of these steps is required to be in a particular arrangement in ord...
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ISBN:
(纸本)9780819486387
Multiple source band image fusion can sometimes be a multi-step process that consists of several intermediate image processing steps. Typically, each of these steps is required to be in a particular arrangement in order to produce a unique output image. GStreamer is an open source, cross platform multimedia framework, and using this framework, engineers at NVESD have produced a software package that allows for real time manipulation of processing steps for rapid prototyping in image fusion.
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 par...
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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.
In this paper we propose a new approach for distributed multiclass classification using a hierarchical fusion architecture. Binary decisions from local sensors, possibly in the presence of faults, axe fused locally. L...
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ISBN:
(纸本)0819449598
In this paper we propose a new approach for distributed multiclass classification using a hierarchical fusion architecture. Binary decisions from local sensors, possibly in the presence of faults, axe fused locally. Locally fused results are forwarded to the global fusion center that determines the final classification result. Classification fusion in our approach is implemented via error correcting codes to incorporate fault-tolerance capability. This new approach not only provides an improved fault-tolerance capability but also reduces bandwidth requirements as well as computation time and memory requirements at the fusion center. Numerical examples axe provided to illustrate the performance of this new approach.
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