A Space-Time Diffusion Scheme for Peer-to-Peer Least-Squares Estimation
- Creators
- Xiao, Lin
- Boyd, Stephen
- Lall, Sanjay
Abstract
We consider a sensor network in which each sensor takes measurements, at various times, of some unknown parameters, corrupted by independent Gaussian noises. Each node can take a finite or infinite number of measurements, at arbitrary times (ie, asynchronously). We propose a space-time diffusion scheme, that relies only on peer-to-peer communication, and allows every node to asymptotically compute the global maximum-likelihood estimate of the unknown parameters. At each iteration, information is diffused across the network by a temporal update step and a spatial update step. Both steps update each node's state by a weighted average of its current value and locally available data: new measurements for the time update, and neighbors' data for the spatial update. At any time, any node can compute a local weighted least-squares estimate of the unknown parameters, which converges to the global maximum-likelihood solution. With an infinite number of measurements, these estimates converge to the true parameter values in the sense of mean-square convergence. We show that this scheme is robust to unreliable communication links, and works in a network with dynamically changing topology.
Additional Information
© 2006 ACM.Additional details
- Eprint ID
- 24767
- DOI
- 10.1145/1127777.1127806
- Resolver ID
- CaltechAUTHORS:20110809-115212015
- Created
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2011-08-09Created from EPrint's datestamp field
- Updated
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2021-11-09Created from EPrint's last_modified field