A Proportionate Recursive Least Squares Algorithm and Its Performance Analysis

The proportionate updating (PU) mechanism has been widely adopted in least mean squares (LMS) adaptive filtering algorithms to exploit the system sparsity. In this brief, we propose a proportionate recursive least squares (PRLS) algorithm for the sparse system estimation, in which, an independent we...

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Vydané v:IEEE transactions on circuits and systems. II, Express briefs Ročník 68; číslo 1; s. 506 - 510
Hlavní autori: Qin, Zhen, Tao, Jun, Xia, Yili
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Adaptive algorithms
Adaptive filters
Adaptive systems
Circuits and systems
Convergence
Covariance matrices
Energy conservation
energy conservation principle
Least mean squares
Least mean squares algorithm
proportionate matrix
Recursive least squares (RLS)
Sparse matrices
sparse systems
Steady state
System identification
ISSN:1549-7747, 1558-3791
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Abstract The proportionate updating (PU) mechanism has been widely adopted in least mean squares (LMS) adaptive filtering algorithms to exploit the system sparsity. In this brief, we propose a proportionate recursive least squares (PRLS) algorithm for the sparse system estimation, in which, an independent weight update is assigned to each tap according to the magnitude of that estimated filter coefficient. Its mean square performance is analyzed via the energy conservation principle in both the transient and steady-state stages. In this way, an explicit condition on the control parameter of the proportionate matrix of PRLS can be obtained to ensure a better steady-state performance than that of RLS. Simulation results in a system identification setting support the analysis.
AbstractList The proportionate updating (PU) mechanism has been widely adopted in least mean squares (LMS) adaptive filtering algorithms to exploit the system sparsity. In this brief, we propose a proportionate recursive least squares (PRLS) algorithm for the sparse system estimation, in which, an independent weight update is assigned to each tap according to the magnitude of that estimated filter coefficient. Its mean square performance is analyzed via the energy conservation principle in both the transient and steady-state stages. In this way, an explicit condition on the control parameter of the proportionate matrix of PRLS can be obtained to ensure a better steady-state performance than that of RLS. Simulation results in a system identification setting support the analysis.
Author Xia, Yili
Tao, Jun
Qin, Zhen
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SubjectTerms Adaptive algorithms
Adaptive filters
Adaptive systems
Circuits and systems
Convergence
Covariance matrices
Energy conservation
energy conservation principle
Least mean squares
Least mean squares algorithm
proportionate matrix
Recursive least squares (RLS)
Sparse matrices
sparse systems
Steady state
System identification
Title A Proportionate Recursive Least Squares Algorithm and Its Performance Analysis
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