1. Decentralized Data Fusion and Control in Active Sensor Networks Alexei Makarenko, Hugh Durrant-Whyte Christian Potthast

2. Motivation

3. Example I

4. Example II

5. 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

6. Node structure

7. Local filter

8. Local Filter II Environment 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

9. 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:

10. IF vs. KF

11. 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:

12. 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.

13. Experiments Tracking a target:

15. Experiments