Stability and convergence analysis of transform-domain LMS adaptive filters with second-order autoregressive process

  • Authors:

  • Zhihong Man;Suiyang Khoo;Hong Ren Wu;Shengkui Zhao

  • Affiliations:

  • School of Engineering, Monash University Malaysia, Petaling Jaya, Malaysia;School of Engineering, Monash University Malaysia, Petaling Jaya, Malaysia;School of Electrical and Computer Engineering, RMIT University, VIC, Australia;School of Computer Engineering, Nanyang Technological University, Singapore

  • Venue:
  • IEEE Transactions on Signal Processing
  • Year:
  • 2009

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Abstract

In this paper, the stability and convergence properties of the class of transform-domain least mean square (LMS) adaptive filters with second-order autoregressive (AR) process are investigated. It is well known that this class of adaptive filters improve convergence property of the standard LMS adaptive filters by applying the fixed data-independent orthogonal transforms and power normalization. However, the convergence performance of this class of adaptive filters can be quite different for various input processes, and it has not been fully explored. In this paper, we first discuss the mean-square stability and steady-state performance of this class of adaptive filters. We then analyze the effects of the transforms and power normalization performed in the various adaptive filters for both first-order and second-order AR processes. We derive the input asymptotic eigenvalue distributions and make comparisons on their convergence performance. Finally, computer simulations on AR process as well as moving-average (MA) process and autoregressive-moving-average (ARMA) process are demonstrated for the support of the analytical results.