Distributed estimation over an adaptive incremental network based on the affine projection algorithm

Authors:
Leilei Li;Jonathon A. Chambers;Cassio G. Lopes;Ali H. Sayed
Affiliations:
Academy of Broadcast Planning, SARFT, Beijing, China;Department of Electronic and Electrical Engineering Loughborough University, Loughborough, Leics, UK;Department of Electronic Systems, Escola Politecnica University of Sao Paulo;Adaptive Systems Laboratory, Department of the Electrical Engineering, University of California, Los Angeles
Venue:
IEEE Transactions on Signal Processing
Year:
2010

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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.