In this paper we propose a new approach for distributed multiclass classification using a hierarchical fusionarchitecture. 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 fusionarchitecture. 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.
The revised JDL fusion model Level 4 "process refinement" covers a broad spectrum of actions such as sensor management and control. A limitation of Level 4 is the purpose of control - whether it be for user ...
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ISBN:
(纸本)0819449598
The revised JDL fusion model Level 4 "process refinement" covers a broad spectrum of actions such as sensor management and control. A limitation of Level 4 is the purpose of control - whether it be for user needs or system operation. Level 5, User Refinement, is a modification to the Revised JDL model that distinguishes between machine process refinement and user refinement. User refinement can either be human control actions or refinement of the user's cognitive model. In many cases, fusion research concentrates on the machine and does not take full advantage of the human as not only a qualified expert to refine the fusion process, but also as customer for whom the fusion system is designed. Without user refinement, sensor fusion is incomplete, inadequate, and the user neglects its worthiness. To capture user capabilities, we. explore the concept of user refinement through decision and action based on situational leadership models. We develop a Fuse-Act Situational User Refinement (FASUR) model that details four refinement behaviors: Neglect, Consult, Rely, and Interact and five refinement functions: Planning, Organizing, Coordinating, Directing, and Controlling. Process refinement varies for different systems and different user information needs. By designing a fusion system with a specific user in mind, vis 'Level 5', a fusionarchitecture can meet user's information needs for varying situations, extend user sensing capabilities for action, and increase the human-machine interaction.
Tracking of moving objects based on multisensor input is a topic that has received considerable attention, and has been the subject of many papers. As Web-type capabilities have emerged, along with ever-improving wire...
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Tracking of moving objects based on multisensor input is a topic that has received considerable attention, and has been the subject of many papers. As Web-type capabilities have emerged, along with ever-improving wireless communications capabilities, coupled with the needs of various applications to include "network-centric warfare" on the part of the military, research on distributed multi-object, multi-sensor tracking has received some attention in recent years. However, the study of these distributed problems at the complete-architecture level, accounting for (among yet other factors) organizational information-sharing protocols, dynamic network topologies, mixes of stationary and moving sensors, complex object dynamics, equipment failure modes, node-specific tracking algorithms and data fusion algorithms, does not seem to have been addressed in any "holistic" way. This paper addresses these issues and describes an integrated testbed being developed at the University at Buffalo's Center for multisourceinformationfusion for the empirical study of these complex architectures involving, in essence, the interconnection of multiple systems.
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