Distributed estimation and quantization

Authors:
J. A. Gubner
Affiliations:
Dept. of Electr. & Comput. Eng., Wisconsin Univ., Madison, WI
Venue:
IEEE Transactions on Information Theory
Year:
2006

Citing 0
Cited 5

Joint source-decoding in large scale sensor networks using Markov random field models

Signal Processing
Brief paper: Min-max optimal data encoding and fusion in sensor networks

Automatica (Journal of IFAC)
Brief paper: An efficient sensor quantization algorithm for decentralized estimation fusion

Automatica (Journal of IFAC)
Practical data compression in wireless sensor networks: A survey

Journal of Network and Computer Applications
Decentralized Estimation using distortion sensitive learning vector quantization

Pattern Recognition Letters

Quantified Score

Hi-index	754.84

Visualization

Abstract

An algorithm is developed for the design of a nonlinear, n-sensor, distributed estimation system subject to communication and computation constraints. The algorithm uses only bivariate probability distributions and yields locally optimal estimators that satisfy the required system constraints. It is shown that the algorithm is a generalization of the classical Lloyd-Max results