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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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 identity through a set-theory approach by determining a plausible set of targets being tracked.
A decentralized technique in which kinematic information from a radar and infrared imager are fused has been developed. To validate its effectiveness, the technique has been compared to a sequentially based Kalman fil...
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A decentralized technique in which kinematic information from a radar and infrared imager are fused has been developed. To validate its effectiveness, the technique has been compared to a sequentially based Kalman filter via Monte Carlo simulation. The comparison showed that the decentralized technique is a potentially viable alternative.
Intelligent Transportation Systems (ITS), implemented all over the world, has become an important and practical traffic management technique. Among all ITS subsystems, the detection system plays an integral element th...
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ISBN:
(纸本)0819431931
Intelligent Transportation Systems (ITS), implemented all over the world, has become an important and practical traffic management technique. Among all ITS subsystems, the detection system plays an integral element that provides all the necessary environmental information to the ITS infrastructure. This paper describes the ITS Detector testbed design, currently being implemented with these potential ITS applications on the State Highway 6 in College Station, Texas to provide a multi-sensor, multi-source fusion environment that utilizes both multi-sensor and distributed sensor system testing environment.
We consider the problem of identify fusion for a multi-sensor target tracking system whereby sensors generate reports on the target identities. Since the sensor reports are typically fuzzy, 'incomplete' and in...
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We consider the problem of identify fusion for a multi-sensor target tracking system whereby sensors generate reports on the target identities. Since the sensor reports are typically fuzzy, 'incomplete' and inconsistent, the fusion of such sensor reports becomes a major challenge. In this paper, we introduce a new identify fusion approach based on the minimization of inconsistencies between the sensor reports by using a convex Quadratic Programming (QP) and linear programming (LP) formulation. In contrast to the Dempster-Shafer's evidential reasoning approach which suffers from exponentially growing complexity, our approach is highly efficient (polynomial time solvable). Moreover, our approach is capable of fusing 'Ratio type' sensor reports, thus it is more general than the evidential reasoning theory. When the sensor reports are consistent, the solution generated by the new fusion method can be shown to converge to the true probability distribution. Simulation work shows that our method generates reasonable fusion results, and when only 'Subset type' sensor reports are present, it produces fusion results similar to that obtained via the evidential reasoning theory.
We discuss virtual Associative Networks (vANs) emd their relevance for addressing computationally prohibitive sensorfusion problems (with results in Dynamic sensor Management). To our knowledge, this discussion of vA...
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ISBN:
(纸本)0819431931
We discuss virtual Associative Networks (vANs) emd their relevance for addressing computationally prohibitive sensorfusion problems (with results in Dynamic sensor Management). To our knowledge, this discussion of vAN technology for sensorfusion is unique and our current result involving vANs for Dynamic sensor Management is the first of its kind. The following provides methodology, results, and extensions.
This paper describes two practical fusion techniques (hybrid fusion and cued fusion) for automatic target cueing that combine features derived from each sensor data at the object-level. In the hybrid fusion method eac...
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This paper describes two practical fusion techniques (hybrid fusion and cued fusion) for automatic target cueing that combine features derived from each sensor data at the object-level. In the hybrid fusion method each of the input sensor data is prescreened (i.e. Automatic Target Cueing (ATC) is performed) before the fusion stage. The cued fusion method assumes that one of the sensors is designated as a primary sensor, and thus ATC is only applied to its input data. If one of the sensors exhibits a higher Pd and/or a lower false alarm rate, it can be selected as the primary sensor. However, if the ground coverage can be segmented to regions in which one of the sensors is known to exhibit better performance, then the cued fusion can be applied locally/adaptively by switching the choice of a primary sensor. Otherwise, the cued fusion is applied both ways (each sensor as primary) and the outputs of each cued mode are combined. Both fusion approaches use a back-end discrimination stage that is applied to a combined feature vector to reduce false alarms. The two fusion processes were applied to spectral and radar sensor data and were shown to provide substantial false alarm reduction. The approaches are easily extendable to more than two sensors.
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...
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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.
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.
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 ...
详细信息
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 this paper we present a methodology for fuzzy sensorfusion. We then apply this methodology to sensor data from a gas turbine power plant. The developed fusion algorithm tackles several problems: 1.) It aggregates ...
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In this paper we present a methodology for fuzzy sensorfusion. We then apply this methodology to sensor data from a gas turbine power plant. The developed fusion algorithm tackles several problems: 1.) It aggregates redundant (but uncertain) sensor information; this allows making decisions which sensors (and to what degree) should be considered for propagation of sensor information. 2.) It filters out noise and sensor failure from measurements; this allows a system to operate despite temporary or permanent failure of one or more sensors. For the fusion, we use a combination of direct and functional redundancy. The fusion algorithm uses confidence values obtained for each sensor reading from validation curves and performs a weighted average fusion. With increasing distance from the predicted value, readings are discounted through a non-linear validation function. They are assigned a confidence value accordingly. The predicted value in the described algorithm is obtained through application of a fuzzy exponential weighted moving average time series predictor with adaptive coefficients. Experiments on real data from a gas turbine power plant show the robustness of the fusion algorithm which leads to smooth controller input values.
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