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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Bibliographic Details
Published in:IEEE transactions on circuits and systems. II, Express briefs Vol. 68; no. 1; pp. 506 - 510
Main Authors: Qin, Zhen, Tao, Jun, Xia, Yili
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1549-7747
1558-3791
DOI:10.1109/TCSII.2020.3004466