@article{Abdolee:233422,
title = {Diffusion LMS for source and process estimation in sensor networks},
author = {Abdolee, Reza and Champagne, Benoit and Sayed, Ali H.},
publisher = {IEEE},
journal = {IEEE Statistical Signal Processing Workshop (SSP)},
pages = {165-168},
year = {2012},
abstract = {We develop a least mean-squares (LMS) diffusion strategy for sensor network applications where it is desired to estimate parameters of physical phenomena that vary over space. In particular, we consider a regression model with space-varying parameters that captures the system dynamics over time and space. We use a set of basis functions such as sinusoids or B-spline functions to replace the space-variant (local) parameters with space-invariant (global) parameters, and then apply diffusion adaptation to estimate the global representation. We illustrate the performance of the algorithm via simulations.},
url = {http://infoscience.epfl.ch/record/233422},
doi = {10.1109/SSP.2012.6319649},
}