Diffusion LMS for source and process estimation in sensor networks

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.


Published in:
IEEE Statistical Signal Processing Workshop (SSP), 165-168
Presented at:
Statistical Signal Processing Workshop (SSP), Ann Arbor, MI, USA, August 5-8, 2012
Year:
2012
Publisher:
IEEE
Laboratories:




 Record created 2017-12-19, last modified 2018-03-17


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