Performance analysis of the deficient length LMS adaptive algorithm

Authors:
K. Mayyas
Affiliations:
Dept. of Electr. Eng., Jordan Univ. of Sci. & Technol., Irbid, Jordan
Venue:
IEEE Transactions on Signal Processing - Part I
Year:
2005

Citing 0
Cited 9

An improved variable tap-length LMS algorithm

Signal Processing
Adaptive nonlinear system identification in the short-time fourier transform domain

IEEE Transactions on Signal Processing
A VLMS based pseudo-fractional optimum order estimation algorithm

Proceedings of the 2011 International Conference on Communication, Computing & Security
Dynamically Partitioned Hierarchical Constant Modulus Algorithm for Variable Delay Spread Channels

Wireless Personal Communications: An International Journal
A variable step-size selective partial update LMS algorithm

Digital Signal Processing
Performance analysis of under-modelling stereophonic acoustic echo cancellation by adaptive filtering LMS algorithm

Computers and Electrical Engineering
Performance analysis of the selective coefficient update NLMS algorithm in an undermodeling situation

Digital Signal Processing
Modified partial update EDS algorithms for adaptive filtering

Analog Integrated Circuits and Signal Processing
A minimised complexity dynamic structure adaptive filter design for improved steady state performance analysis

International Journal of Computational Vision and Robotics

Quantified Score

Hi-index	0.00

Visualization

Abstract

In almost all analyzes of the least mean-square (LMS) finite impulse response (FIR) adaptive algorithm, it is assumed that the length of the adaptive filter is equal to that of the unknown system impulse response. However, in many practical situations, a deficient length adaptive filter, whose length is less than that of the unknown system, is employed, and analysis results for the sufficient length LMS algorithm are not necessarily applicable to the deficient length case. Therefore, there is an essential need to accurately quantify the behavior of the LMS algorithm for realistic situations where the length of the adaptive filter is deficient. In this paper, we present a performance analysis for the deficient length LMS adaptive algorithm for correlated Gaussian input data and using the common independence assumption. Exact expressions that completely characterize the transient and steady-state mean-square performances of the algorithm are developed, which lead to new insights into the statistical behavior of the deficient length LMS algorithm. Simulation experiments illustrate the accuracy of the theoretical results in predicting the convergence behavior of the algorithm.