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Decentralized estimation representes an important aspect of decentralized systems. Because various nodes within the system may not have access to all measurements within the system, the individual estimates will be suboptimal.
Decentralized estimation representes an important aspect of decentralized systems. Because various nodes within the system may not have access to all measurements within the system, the individual estimates will be suboptimal. In a perfect world there would exist a means to combine the estimates from all of the nodes into a single estimate which makes use of all system measurements and is optimal. Difficulty arises because some measurements may be used by more than one node leading the estimates from those nodes to be correlated. An example of such a system is the spacecraft attitude estimator shown. Because each extended Kalman filter (EKF) makes use of a common gyro measurement the two estimates will be correlated. The problem to be solved is ''how can we efficiently and consistently fuse the individual estimates from each EKF to obtain the best possible estimate?''
Revision as of 15:35, 26 November 2012
Systems comprised of a number of computing nodes all supporting a common mission. All nodes can be given the same task or asked to support the mission through a subset of tasks. Decentalized systems offer several advantages over their centralized counterparts including:
- Each node does not require intrinsic knowledge of the system topology
- Topology is variable. Nodes can be added or subtracted without significantly impacting the overall system
- Less vulnerable to single-point failures
- Computational load can be spread amongst nodes for efficiency
Decentralized systems also suffer from some disadvantages:
- All nodes may not have access to all information contained within the system
- Multiple nodes have access to the same information
Examples of decentralized systems include robotic swarms, cooperative UAVs, formation flying spacecraft and formation of synthetic apertures using multiple smaller sensors.