Data-reuse recursive least-squares algorithm with Riemannian manifold constraint

Actual signals often contain nonlinear manifold structures, but traditional filtering algorithms assume data are embedded in Euclidean space, which makes them less effective when handling complicated noise and manifold data. To address these challenges, Riemannian geometry constraints to the traditi...

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Veröffentlicht in:	Signal processing Jg. 234; S. 109982
Hauptverfasser:	Zhao, Haiquan, Wang, Haolin, Peng, Yi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.09.2025
Schlagworte:	Adaptive filtering Convergence speed DR-RLS Riemannian manifold constraints Steady-state error Tracking performance Steady-state error Adaptive filtering DR-RLS Riemannian manifold constraints Tracking performance Convergence speed
ISSN:	0165-1684
Online-Zugang:	Volltext
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