Abstract
In this paper, we consider the diffusion adaptive filters where a set of sensors is required to collectively estimate time-varying signals (or parameters) from noisy measurements in a way of information diffusion. We will establish the stability of the diffusion least mean square (DLMS) algorithm, without requiring stationarity, independency, and boundedness assumptions of the system signals, which means that our results can be applied to more general and practical class of stochastic systems than those studied in the literature. We will present theoretical results concerning stability and bounds on the mean square error(MSE) of the filtering. We will also show that the network of sensors can cooperate to guarantee the stability of the filtering, even though any single sensor does not have such a capability. This clearly reveals the advantages of the DLMS algorithm vs. standard least mean square (LMS) algorithm. Numerical simulations will also be presented to support the theoretical justifications.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
