Evolution of new technologies has lead to the development of new devices, exchanging data over a diverse network area. Diversity in the propagation model has given diversity in the interference and has made the estimation algorithm limited to certain interference only. However, the increase in the propagation model, the co-channel interferences are increasing rapidly. In the estimation of such interference, recursive filters were proposed with the objective of lower complexity and faster convergence. Recursive B-spline approximation (RBA) is used as an optimal solution in this approach. An time shift kalman filtration (KF) approach to optimize the RBA using weighted least square (WLS) develops the objective of state stabilization in this approach. However, dynamic interference scenario in the propagation model, leads to a large convergence overhead and the bounded cost function constraint the estimation to bounded limits. To develop a unbounded estimation approach, in this paper, a new 2-Level unconstraint estimation approach for Kalman filter stabilization for RBA is proposed. The method defines a open bound to the signal estimation for faster convergence with higher estimation accuracy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.