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• 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
Node structure
Local filter
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
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:
IF vs. KF
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.
ExperimentsTracking a target:
Experiments