Distributed estimation in diffusion networks using affine least-squares combiners

Fernandez-Bes, Jesus ; Azpicueta-Ruiz, Luis A. ; Arenas-García, Jerónimo ; Silva, Magno T.M.

Resumen: We propose a diffusion scheme for adaptive networks, where each node obtains an estimate of a common unknown parameter vector by combining a local estimate with the combined estimates received from neighboring nodes. The combination weights are adapted in order to minimize the mean-square error of the network employing a local least-squares (LS) cost function. This adaptive diffusion network with LS combiners (ADN-LS) is analyzed, deriving expressions for its network mean-square deviation that characterize the convergence and steady-state performance of the algorithm. Experiments carried out in stationary and tracking scenarios show that our proposal outperforms a state-of-art scheme for adapting the weights of diffusion networks (ACW algorithm from [10]), both during convergence and in tracking situations. Despite its good convergence behavior, our proposal may present a slightly worse steady-state performance in stationary or slowly-changing scenarios with respect to ACW due to the error inherent to the least-squares adaptation with sliding window. Therefore, to take advantage of these different behaviors, we also propose a hybrid scheme based on a convex combination of the ADN-LS and ACW algorithms.

Idioma: Inglés

DOI: 10.1016/j.dsp.2014.09.004

Año: 2015

Publicado en: DIGITAL SIGNAL PROCESSING 36 (2015), 1-14
ISSN: 1051-2004

Factor impacto JCR: 1.444 (2015)

Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 112 / 255 = 0.439 (2015) - Q2 - T2

Factor impacto SCIMAGO: 0.566 - Signal Processing (Q2) - Electrical and Electronic Engineering (Q2)

Tipo y forma: Artículo (PostPrint)