The proceedings contain 23 papers. The topics discussed include: capturing dynamics on multiple time scales: a multilevel fusion approach for cluttered electromagnetic data;classification of terrain using multi-resolu...
ISBN:
(纸本)9780819481740
The proceedings contain 23 papers. The topics discussed include: capturing dynamics on multiple time scales: a multilevel fusion approach for cluttered electromagnetic data;classification of terrain using multi-resolution satellite/aerial imagery and lidar data;data fusion and classification using a hybrid intrinsic cellular inference network;fusion of ESM reports through Dempster-Shafer and Dezert-Smarandache theories;level 0-2 fusion model for ATR using fuzzy logic;multi-layered context impact modulation for enhanced focus of attention of situational awareness in persistent surveillance systems;global evaluation of focused Bayesian fusion;multiple hypothesis tracking of two persons using a network of lidar sensors with stationary and directional beams;a copula-based semi-parametric approach for footstep detection using seismic sensor networks;and integrating perception and problem solving to predict complex object behaviors.
In this paper, a new approach to logic-based knowledge fusion is proposed. It is based on the use of (a form of) semaphores to solve conflicting information. It is shown that a traditional use of semaphores is not rel...
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ISBN:
(纸本)0819449598
In this paper, a new approach to logic-based knowledge fusion is proposed. It is based on the use of (a form of) semaphores to solve conflicting information. It is shown that a traditional use of semaphores is not relevant in the case of an iterated fusion process. Accordingly, an adequate technique is thus proposed that allows multiple fusion steps to be performed. Technical properties of this new technique are then investigated.
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...
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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.
This paper describes a series of experiments in data fusion. of remotely sensed multispectral satellite imagery, in-situ physical measurement data (temperature, pH, salinity), and implicitly encoded knowledge (contain...
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ISBN:
(纸本)0819449598
This paper describes a series of experiments in data fusion. of remotely sensed multispectral satellite imagery, in-situ physical measurement data (temperature, pH, salinity), and implicitly encoded knowledge (contained in location and season) to predict values and classified levels of chlorophyll-a using an artificial. neural net (ANN). ANNs inherently fuse data inputs and discover relationships to provide a fused interpretation of the inputs. The experiments investigated the effects of fusing data and knowledge from the three different types of sources: non-contact, physical contact, and implicit. The results indicate that fusing the three source types improved prediction of chlorophyll-a values and classification levels, and that the multisource ANN fusion approach might improve or augment present periodic sample point monitoring methods for chlorophyll-a.
Various fusion system architectures postulated and studied previously for environments with two and three data sources are further explored in this study to bring out the expanding scope for delineating the architectu...
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ISBN:
(纸本)0819449598
Various fusion system architectures postulated and studied previously for environments with two and three data sources are further explored in this study to bring out the expanding scope for delineating the architecture options for multiple data source environments. A spectrum of single and multi-stage fusion architecture options are defined. The potential for such expansion of choices is illustrated using the scenario with four data sources as an example. Potential problem environments corresponding to this range of two to four data sources are identified. Various fusion logic strategies that can be brought to bear for the analysis of these fusion architecture options, when these fusionarchitectures are employed for Decisions In - Decision Out (DEI-DEO) fusion, are also discussed.
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.
In many instances, sensing tasks are best addressed with multiple sensing modalities. However, fusion of the outputs of disparate sensor systems presents a significant challenge to forming a cohesive sensing system. A...
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ISBN:
(纸本)9780819481740
In many instances, sensing tasks are best addressed with multiple sensing modalities. However, fusion of the outputs of disparate sensor systems presents a significant challenge to forming a cohesive sensing system. A discussion of strategies for fusion of disparate sensor data is presented and illustrated with examples of real time and retrospective data fusion for multisensor systems. The first example discussed is a real-time system for situational awareness and the detection of damage control events in ship compartments. The second example is a retrospective data fusion framework for a multisensor system for the detection of buried unexploded ordnance at former bomb and target ranges.
Adaptive image fusion system based on neural network principle was realized. It works with digitalized video sequences of visible and infrared band sensors, and is able to produce the optimal fused image for a wide ra...
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ISBN:
(纸本)9780819490858
Adaptive image fusion system based on neural network principle was realized. It works with digitalized video sequences of visible and infrared band sensors, and is able to produce the optimal fused image for a wide range of lighting conditions through an adaptive change of a fusion algorithm. The change is driven by a change in the measured statistic of the input images. The best algorithm for a particular input is found with the help of an objective measurement of the fusion process quality.
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.
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