000233422 001__ 233422
000233422 005__ 20180317095127.0
000233422 0247_ $$2doi$$a10.1109/SSP.2012.6319649
000233422 037__ $$aCONF
000233422 245__ $$aDiffusion LMS for source and process estimation in sensor networks
000233422 269__ $$a2012
000233422 260__ $$bIEEE$$c2012
000233422 336__ $$aConference Papers
000233422 520__ $$aWe develop a least mean-squares (LMS) diffusion strategy for sensor network applications where it is desired to estimate parameters of physical phenomena that vary over space. In particular, we consider a regression model with space-varying parameters that captures the system dynamics over time and space. We use a set of basis functions such as sinusoids or B-spline functions to replace the space-variant (local) parameters with space-invariant (global) parameters, and then apply diffusion adaptation to estimate the global representation. We illustrate the performance of the algorithm via simulations.
000233422 700__ $$aAbdolee, Reza
000233422 700__ $$aChampagne, Benoit
000233422 700__ $$0251037$$aSayed, Ali H.$$g283344
000233422 7112_ $$aStatistical Signal Processing Workshop (SSP)$$cAnn Arbor, MI, USA$$dAugust 5-8, 2012
000233422 773__ $$q165-168$$tIEEE Statistical Signal Processing Workshop (SSP)
000233422 909CO $$ooai:infoscience.tind.io:233422$$pSTI$$pconf
000233422 909C0 $$0252608$$pASL$$xU13470
000233422 917Z8 $$x144315
000233422 937__ $$aEPFL-CONF-233422
000233422 970__ $$aabdolee2012diffusion/ASL
000233422 973__ $$aOTHER$$rREVIEWED$$sPUBLISHED
000233422 980__ $$aCONF