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|Title: ||Distributed estimation over an adaptive incremental network based on the affine projection algorithm|
|Authors: ||Li, Leilei|
Lopes, Cassio G.
Sayed, Ali H.
|Keywords: ||Adaptive filters|
Affine projection algorithm
|Issue Date: ||2010|
|Publisher: ||© IEEE|
|Citation: ||LI, L. ... et al, 2010. Distributed estimation over an adaptive incremental network based on the affine projection algorithm. IEEE Transactions on Signal Processing, 58 (1), pp. 151-164|
|Abstract: ||We study the problem of distributed estimation based on the affine projection algorithm (APA), which is developed from Newton's method for minimizing a cost function. The proposed solution is formulated to ameliorate the limited convergence properties of least-mean-square (LMS) type distributed adaptive filters with colored inputs. The analysis of transient and steady-state performances at each individual node within the network is developed by using a weighted spatial-temporal energy conservation relation and confirmed by computer simulations. The simulation results also verify that the proposed algorithm provides not only a faster convergence rate but also an improved steady-state performance as compared to an LMS-based scheme. In addition, the new approach attains an acceptable misadjustment performance with lower computational and memory cost, provided the number of regressor vectors and filter length parameters are appropriately chosen, as compared to a distributed recursive-least-squares (RLS) based method.|
|Description: ||This is an article from the journal, IEEE Transactions on Signal Processing [© IEEE]. It is also available at: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Appears in Collections:||Published Articles (Mechanical, Electrical and Manufacturing Engineering)|
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