decentralized data fusion and control in active sensor networks
DESCRIPTION
Decentralized Data Fusion and Control in Active Sensor Networks. Alexei Makarenko , Hugh Durrant -Whyte. Christian Potthast. Motivation. Example I. Example II. Decentralization. Scalable Computational and communication load at each node is independent of the size of the network - PowerPoint PPT PresentationTRANSCRIPT
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Decentralized Data Fusion and Control in Active Sensor Networks
Alexei Makarenko, Hugh Durrant-Whyte
Christian Potthast
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Motivation
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Example I
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Example II
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Decentralization
• Scalable– Computational and communication load at each node is independent
of the size of the network• Robustness
– No element of the system is mission critical, system is survivable in the event of run-time loss of components
• Modularity– Components can be implemented and deployed independently from
each other
Characterized by:
• No component is central to the successful operation of the network• No central service or facilities
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Node structure
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Local filter
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Local Filter IIEnvironment feature: xk = x(tk)
Observation of feature: zk = z(tk)
Observation likelihood: L(zk | xk)
Find the posterior probability of: P (xk|Zk , x0 )
Prediction of the motion
Fuse the information
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Local Filter III
Local belief and the new belief in an external node
Information can be computed as:
Fusing of information held by two different nodes:
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IF vs. KF
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IF vs. KF
• IF and KF update both in two steps– Prediction and measurement step
• Update steps can vastly differ in complexity– KF prediction step: – IF prediction step:– KF measurement update: – IF measurement update:
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Control
• Coordinated Control– Chose action purely on local observations– Propagate observed information to sensing platform
• Cooperative Control through Negotiation– Propagate expected information through negotiation
channels.
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ExperimentsTracking a target:
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Experiments