Decentralized Data Fusion and Control in Active Sensor Networks

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) L(zk | xk) Observation likelihood: Find the posterior probability of: P (xk|Zk , x0 ) Prediction of the motion Fuse the information

9. Local Filter III Fusing of information held by two different nodes: Local belief and the new belief in an external node Information can be computed as:

10. IF vs. KF

11. 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:

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