A number of sensors are being developed for the Concealed Weapon Detection (CWD), and use of the appropriate sensor or combination of sensors will be: very important to the success of such technologies. Assuming that ...
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ISBN:
(纸本)0819431931
A number of sensors are being developed for the Concealed Weapon Detection (CWD), and use of the appropriate sensor or combination of sensors will be: very important to the success of such technologies. Assuming that two identical sensors are used to collect data on a target from different angular views, this paper addresses the problem of registration associated with the collected scenes. Theory and application to real data are presented.
In this paper, a series of knowledge fusion operators are motivated and analyzed. They are defined in a semantic way, although syntactical facets of knowledge are taken into account. More precisely, they rely on a ran...
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ISBN:
(纸本)0819444812
In this paper, a series of knowledge fusion operators are motivated and analyzed. They are defined in a semantic way, although syntactical facets of knowledge are taken into account. More precisely, they rely on a rank-ordering of interpretations that is based on the number of formulas that the interpretations falsify. It is briefly discussed how these operators could be refined, by taking into account various distribution policies of the falsified information among the knowledge sources, syntactical properties of formulas to be fused and forms of integrity constraints preference among literals.
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 sensorfusion.
The aim of this paper is to explore the problem of fusing identity and attribute information emanating from different sources, and to offer the decision maker a quantitative analysis based on statistical methodology t...
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ISBN:
(纸本)081942482X
The aim of this paper is to explore the problem of fusing identity and attribute information emanating from different sources, and to offer the decision maker a quantitative analysis based on statistical methodology that can enhance his/her decision making process regarding the identity of detected objects. Two identity information fusionarchitectures are discussed here. The first is concerned with the fusion of identity declarations where the sources are expected to provide only useful and complete results such as an identity declaration. The second is concerned with the fusion of attribute information using a modified version of the Dempster-Shafer evidential combination algorithm.
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.
Situational Awareness is one of the keys to realizing the full potential of a weapons platform. It encompasses mission planning and rehearsal, the optimum use of offensive and defensive resources, and post mission ana...
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Situational Awareness is one of the keys to realizing the full potential of a weapons platform. It encompasses mission planning and rehearsal, the optimum use of offensive and defensive resources, and post mission analysis. In this report, the Situation Awareness process is described as it relates to the integration of Electronic Warfare (EW) with targeting and intelligence and information warfare systems. Discussion is focused on the sensorfusion, Situation Assessment, and Response Management Strategies.
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)0819444812
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. This principle has been successfully applied in the monitoring of an ultra-energy efficient house in Canada and other applications.
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...
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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 sensorfusion 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.
Humans exhibit remarkable abilities to estimate, filter, predict, and fuse information in target tracking tasks. To improve track quality, we extend previous tracking approaches by investigating human cognitive-level ...
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ISBN:
(纸本)0819431931
Humans exhibit remarkable abilities to estimate, filter, predict, and fuse information in target tracking tasks. To improve track quality, we extend previous tracking approaches by investigating human cognitive-level fusion for constraining the set of plausible targets where the number of targets is not known a priori. The target track algorithm predicts a belief in the position and pose for a set of targets and an automatic target recognition algorithm uses the pose estimate to calculate an accumulated target-belief classification confidence measure. The human integrates the target track information and classification confidence measures to determine the number and identification of targets. This paper implements the cognitive belief filtering approach for sensorfusion and resolves target identify through a set-theory approach by determining a plausible set of targets being tracked.
In both military and civilian applications, increasing interest is being shown in fusing infrared and vision images for improved situational awareness. In previous work, the authors have developed a fusion method for ...
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In both military and civilian applications, increasing interest is being shown in fusing infrared and vision images for improved situational awareness. In previous work, the authors have developed a fusion method for combining the thermal and vision images into a single image emphasizing the most salient features of the surrounding environment. This approach is based on the assumption that although the thermal and vision data are uncorrelated, they are complementary and can be fused using a suitable disjunctive function. This paper, as a continuation of that work, will describe the development of an information based real-time data level fusion method. In addition, applicability of the algorithms that we developed for data level fusion to feature level techniques (e.g., shapes, lines, and edges) will be investigated.
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