TAILIEUCHUNG - Báo cáo hóa học: " Research Article Performance Analysis of the Consensus-Based Distributed LMS Algorithm"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Performance Analysis of the Consensus-Based Distributed LMS Algorithm | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2009 Article ID 981030 19 pages doi 2009 981030 Research Article Performance Analysis of the Consensus-Based Distributed LMS Algorithm Gonzalo Mateos Ioannis D. Schizas and Georgios B. Giannakis Department of Electrical and Computer Engineering University of Minnesota 200 Union Street SE Minneapolis MN 55455 USA Correspondence should be addressed to Georgios B. Giannakis georgios@ Received 15 May 2009 Accepted 8 October 2009 Recommended by Husheng Li Low-cost estimation of stationary signals and reduced-complexity tracking of nonstationary processes are well motivated tasks than can be accomplished using ad hoc wireless sensor networks WSNs . To this end a fully distributed least mean-square D-LMS algorithm is developed in this paper in which sensors exchange messages with single-hop neighbors to consent on the network-wide estimates adaptively. The novel approach does not require a Hamiltonian cycle or a special bridge subset of sensors while communications among sensors are allowed to be noisy. A mean-square error MSE performance analysis of D-LMS is conducted in the presence of a time-varying parameter vector which adheres to a first-order autoregressive model. For sensor observations that are related to the parameter vector of interest via a linear Gaussian model and after adopting simplifying independence assumptions exact closed-form expressions are derived for the global and sensor-level MSE evolution as well as its steady-state . values. Mean and MSE-sense stability of D-LMS are also established. Interestingly extensive numerical tests demonstrate that for small step-sizes the results accurately extend to the pragmatic setting whereby sensors acquire temporally correlated not necessarily Gaussian data. Copyright 2009 Gonzalo Mateos et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted

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