Combinations of Adaptive Filters
Jer\'onimo Arenas-Garc\'ia, Luis A. Azpicueta-Ruiz, Magno T.M., Silva, Vitor H. Nascimento, Ali H. Sayed

TL;DR
This paper reviews the use of adaptive filter combinations in signal processing, highlighting their design principles, advantages, and performance in various applications, especially under limited prior knowledge scenarios.
Contribution
It provides a comprehensive overview of combination schemes for adaptive filters, emphasizing design rules and worst-case performance bounds from the machine learning perspective.
Findings
Combination of adaptive filters improves robustness.
Design rules enhance filter performance in uncertain scenarios.
Examples demonstrate effectiveness across applications.
Abstract
Adaptive filters are at the core of many signal processing applications, ranging from acoustic noise supression to echo cancelation, array beamforming, channel equalization, to more recent sensor network applications in surveillance, target localization, and tracking. A trending approach in this direction is to recur to in-network distributed processing in which individual nodes implement adaptation rules and diffuse their estimation to the network. When the a priori knowledge about the filtering scenario is limited or imprecise, selecting the most adequate filter structure and adjusting its parameters becomes a challenging task, and erroneous choices can lead to inadequate performance. To address this difficulty, one useful approach is to rely on combinations of adaptive structures. The combination of adaptive filters exploits to some extent the same divide and conquer principle…
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