In this paper, we proposed a maximum likelihood fusion approach for multisensorfusionapplications. The proposed approach was based on a parametric modeling of the noise covariance and formulated in the transformed n...
详细信息
ISBN:
(纸本)0819428256
In this paper, we proposed a maximum likelihood fusion approach for multisensorfusionapplications. The proposed approach was based on a parametric modeling of the noise covariance and formulated in the transformed noise subspace. It could solve the fusion problems when the sensor noises are correlated and the scaling coefficients unknown. The approach could also deal with nonstationary signals. We showed that in the optimization process, the computation of the noise parameters and the scaling coefficients were separable leading to a reduced optimization dimensionality and computational complexity. Computer simulations were used to demonstrate the effectiveness of the proposed fusion approach.
Using Bayesian statistical methods a formulation is setup for fusing multi band data from LWIR sensors. This formulation is illustrated with applications to synthetic data consisting of 100 signatures in the wavelengt...
详细信息
ISBN:
(纸本)0819428256
Using Bayesian statistical methods a formulation is setup for fusing multi band data from LWIR sensors. This formulation is illustrated with applications to synthetic data consisting of 100 signatures in the wavelength bands 6-10 mu m, 11-16 mu m and 17-21 mu m. Following the works of Jaynes, and Bretthorst, a Bayesian formulation is given for detrending the time seriesdata for the emissive area, followed by estimations of frequencies and their amplitudes. This formulation is illustrated with analysis of the synthetic data.
This paper describes a blackboard system for integrating observations from multiple sensors. Multiple sensors report observations to the blackboard system. The blackboard system correlates the observations to a set of...
详细信息
ISBN:
(纸本)081942482X
This paper describes a blackboard system for integrating observations from multiple sensors. Multiple sensors report observations to the blackboard system. The blackboard system correlates the observations to a set of active models, and the models are both temporally limited and also probabilistic. The design is object-oriented to allow for extensions that accommodate new models and sensors. An example application to a grid of sensors is 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...
详细信息
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...
详细信息
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.
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...
详细信息
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.
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...
详细信息
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...
详细信息
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.
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 mult...
详细信息
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.
The tracking system with Dempster-Shafer attribute association algorithm is studied. The aim of the paper is to study how the different parameters affect to the association accuracy. The results show that the proposed...
详细信息
ISBN:
(纸本)0819436771
The tracking system with Dempster-Shafer attribute association algorithm is studied. The aim of the paper is to study how the different parameters affect to the association accuracy. The results show that the proposed Dempster-Shafer attribute association algorithm is robust for parameter variations and thus for modelling errors. The simulations are done according to synthesized data.
暂无评论