A minimised complexity dynamic structure adaptive filter design for improved steady state performance analysis

The structural complexity and overall performance of the adaptive filter depend on its structure. The number of taps is one of the most important structural parameters of the liner adaptive filter. In practice the system length is not known a priori and has to be estimated from the knowledge of the input and output signals. In a system identification framework the tap-length estimation algorithm automatically adapts the filter order to the suitable optimum value which makes the variable order adaptive filter a best identifier of the unknown plant. In this paper an improved pseudo-fractional tap-length selection algorithm is proposed to find out the optimum tap-length which best balances the complexity and steady state performance. The performance analysis is presented to formulate steady state tap-length in correspondence with the proposed algorithm. Simulations and results are provided to observe the analysis and to make a comparison with the existing tap-length learning methods.

[1]  Huijuan Cui,et al.  Convergence analysis of a deficient-length LMS filter and optimal-length sequence to model exponential decay impulse response , 2003, IEEE Signal Process. Lett..

[2]  Huijuan Cui,et al.  LMS algorithm with gradient descent filter length , 2004, IEEE Signal Processing Letters.

[3]  Haibo Li,et al.  Adaptive filter length selection for acoustic echo cancellation , 2009, Signal Process..

[4]  Corneliu Rusu,et al.  Novel Stochastic Gradient Adaptive Algorithm with Variable Length , 2001 .

[5]  Ali H. Sayed,et al.  Steady-State Performance Analysis of a Variable Tap-Length LMS Algorithm , 2008, IEEE Transactions on Signal Processing.

[6]  Jonathon A. Chambers,et al.  A novel adaptive leakage factor scheme for enhancement of a variable tap-length learning algorithm , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[7]  Arie Feuer,et al.  Variable length stochastic gradient algorithm , 1991, IEEE Trans. Signal Process..

[8]  Bernard Widrow,et al.  Adaptive Signal Processing , 1985 .

[9]  Zhongli Liu,et al.  A VSLMS Style Tap-Length Learning Algorithm for structure adaptation , 2008, 2008 11th IEEE Singapore International Conference on Communication Systems.

[10]  Ravinder Nath,et al.  A VLMS based pseudo-fractional optimum order estimation algorithm , 2011, ICCCS '11.

[11]  Yu Gong,et al.  An LMS style variable tap-length algorithm for structure adaptation , 2005, IEEE Transactions on Signal Processing.

[12]  Khaled A. Mayyas,et al.  Performance analysis of the deficient length LMS adaptive algorithm , 2005, IEEE Transactions on Signal Processing.

[14]  Y. K. Won,et al.  Variable LMS Algorithms Using the Time Constant Concept , 1994, IEEE International Conference on Consumer Electronics.

[15]  J. Shynk.  Frequency-domain and multirate adaptive filtering , 1992, IEEE Signal Processing Magazine.

[16]  Mahesh Chandra,et al.  Dynamic tap-length estimation based low complexity acoustic echo canceller , 2012, 2012 International Conference on Emerging Trends in Science, Engineering and Technology (INCOSET).

[17]  Tyseer Aboulnasr,et al.  A robust variable step-size LMS-type algorithm: analysis and simulations , 1997, IEEE Trans. Signal Process..

[18]  Yu Gong,et al.  Structure adaptation of linear MMSE adaptive filters , 2004 .

[19]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .