Abstract
This paper presents a mathematically rigorous analysis of linearly constrained adaptive filtering algorithms based on the adaptive projected subgradient method. We provide the novel concept of constraint-embedding functions that enables to analyze certain classes of linearly constrained adaptive algorithms in a unified manner. Trajectories of the linearly constrained adaptive filters always lie in the affine constraint set, a translation of a closed subspace. Based on this fact, we translate all the points on the constraint set to its underlying subspace . which we regard as a Hilbert space . thereby making the analysis feasible. Derivations of the linearly constrained adaptive filtering algorithms are finally presented in connection with the analysis.
| Original language | English |
|---|---|
| Title of host publication | European Signal Processing Conference |
| Pages | 131-135 |
| Number of pages | 5 |
| Publication status | Published - 2011 |
| Externally published | Yes |
| Event | 19th European Signal Processing Conference, EUSIPCO 2011 - Barcelona, Spain Duration: 2011 Aug 29 → 2011 Sep 2 |
Other
| Other | 19th European Signal Processing Conference, EUSIPCO 2011 |
|---|---|
| Country | Spain |
| City | Barcelona |
| Period | 11/8/29 → 11/9/2 |
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ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering
Cite this
A deterministic analysis of linearly constrained adaptive filtering algorithms. / Yukawa, Masahiro; Yamada, Isao.
European Signal Processing Conference. 2011. p. 131-135.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A deterministic analysis of linearly constrained adaptive filtering algorithms
AU - Yukawa, Masahiro
AU - Yamada, Isao
PY - 2011
Y1 - 2011
N2 - This paper presents a mathematically rigorous analysis of linearly constrained adaptive filtering algorithms based on the adaptive projected subgradient method. We provide the novel concept of constraint-embedding functions that enables to analyze certain classes of linearly constrained adaptive algorithms in a unified manner. Trajectories of the linearly constrained adaptive filters always lie in the affine constraint set, a translation of a closed subspace. Based on this fact, we translate all the points on the constraint set to its underlying subspace . which we regard as a Hilbert space . thereby making the analysis feasible. Derivations of the linearly constrained adaptive filtering algorithms are finally presented in connection with the analysis.
AB - This paper presents a mathematically rigorous analysis of linearly constrained adaptive filtering algorithms based on the adaptive projected subgradient method. We provide the novel concept of constraint-embedding functions that enables to analyze certain classes of linearly constrained adaptive algorithms in a unified manner. Trajectories of the linearly constrained adaptive filters always lie in the affine constraint set, a translation of a closed subspace. Based on this fact, we translate all the points on the constraint set to its underlying subspace . which we regard as a Hilbert space . thereby making the analysis feasible. Derivations of the linearly constrained adaptive filtering algorithms are finally presented in connection with the analysis.
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M3 - Conference contribution
SP - 131
EP - 135
BT - European Signal Processing Conference
ER -