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