TY - JOUR
T1 - A Comparative Survey of Convex Combination of Adaptive Filters
AU - Vázquez, Ángel A.
AU - Avalos, J. Gerardo
AU - Sánchez, Giovanny
AU - Sánchez, Juan C.
AU - Pérez, Héctor
N1 - Publisher Copyright:
© 2023 IETE.
PY - 2023
Y1 - 2023
N2 - Over the last twenty years, combination of adaptive filters has emerged to provide a potential solution in the development of advanced applications, such as channel equalization, active noise control, acoustic echo cancelation, adaptive beamforming, among others. Several authors have demonstrated that the combination of two filters with complementary capabilities improves the overall filter performance in comparison when a single filter is used. Commonly, these combinations employ a fast filter and slow filter to guarantee fast convergence speed and low steady-state mean square error (MSE), respectively, at the cost of increasing their computational complexity. However, those combinations have not been well analysed and compared. Currently, this aspect is crucial since most of the current convex combination of adaptive filters could be implemented in advanced embedded digital devices to be used in real-time signal processing applications. In this work, we evaluate the convex combination of adaptive filters, mainly including those based on least mean square (LMS) algorithm, the affine projection (AP) algorithm and the recursive least mean square (RLS) algorithm. We carry out an extensive performance evaluation to demonstrate the advantages and disadvantages of each approach. Specifically, we simulate them in fixed point, which makes feasible the rapid development of advanced prototypes. This aspect could be potentially valuable to the signal processing engineering communities.
AB - Over the last twenty years, combination of adaptive filters has emerged to provide a potential solution in the development of advanced applications, such as channel equalization, active noise control, acoustic echo cancelation, adaptive beamforming, among others. Several authors have demonstrated that the combination of two filters with complementary capabilities improves the overall filter performance in comparison when a single filter is used. Commonly, these combinations employ a fast filter and slow filter to guarantee fast convergence speed and low steady-state mean square error (MSE), respectively, at the cost of increasing their computational complexity. However, those combinations have not been well analysed and compared. Currently, this aspect is crucial since most of the current convex combination of adaptive filters could be implemented in advanced embedded digital devices to be used in real-time signal processing applications. In this work, we evaluate the convex combination of adaptive filters, mainly including those based on least mean square (LMS) algorithm, the affine projection (AP) algorithm and the recursive least mean square (RLS) algorithm. We carry out an extensive performance evaluation to demonstrate the advantages and disadvantages of each approach. Specifically, we simulate them in fixed point, which makes feasible the rapid development of advanced prototypes. This aspect could be potentially valuable to the signal processing engineering communities.
KW - Adaptation term
KW - Adaptive filters
KW - Affine projection algorithm
KW - Convex combination
KW - Least mean square algorithm
KW - Recursive least mean square algorithm
UR - http://www.scopus.com/inward/record.url?scp=85096198198&partnerID=8YFLogxK
U2 - 10.1080/03772063.2020.1844075
DO - 10.1080/03772063.2020.1844075
M3 - Artículo
AN - SCOPUS:85096198198
SN - 0377-2063
VL - 69
SP - 940
EP - 950
JO - IETE Journal of Research
JF - IETE Journal of Research
IS - 2
ER -