Title :
Diffusion LMS for source and process estimation in sensor networks
Author :
Abdolee, Reza ; Champagne, Benoit ; Sayed, Ali H.
Author_Institution :
Dept. of Electr. & Comput. Eng., McGill Univ., Montreal, QC, Canada
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.
Keywords :
least mean squares methods; parameter estimation; signal representation; B-spline functions; diffusion LMS; diffusion adaptation; global representation; least mean-squares diffusion strategy; process estimation; regression model; sensor networks; signal processing; source estimation; space-invariant parameter; space-variant parameter; space-varying parameter; system dynamics; Adaptation models; Adaptive systems; Estimation; Least squares approximation; Mathematical model; Signal processing; Vectors; Diffusion adaptation; Distributed adaptive estimation; fluid-flow; population dispersal; sensor networks; space-varying parameters;
Conference_Titel :
Statistical Signal Processing Workshop (SSP), 2012 IEEE
Conference_Location :
Ann Arbor, MI
Print_ISBN :
978-1-4673-0182-4
Electronic_ISBN :
pending
DOI :
10.1109/SSP.2012.6319649