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Summary of Adaptive Filtering Methods

Module by: Douglas L. Jones. E-mail the author

  1. LMS: remains the simplest and best algorithm when slow convergence is not a serious issue (typically used) ON O N
  2. NLMS: simple extension of the LMS with much faster convergence in many cases (very commonly used) ON O N
  3. Frequency-domain methods: offer computational savings ( OlogN O N ) for long filters and usually offer faster convergence, too (sometimes used; very commonly used when there are already FFTs in the system)
  4. Lattice methods: are stable and converge quickly, but cost substantially more than LMS and have higher residual EMSE than many methods (very occasionally used) ON O N
  5. RLS: algorithms that converge quickly and are stable exist. However, they are considerably more expensive than LMS. (almost never used) ON O N
  6. Block RLS: (least squares) methods exist and can be pretty efficient in some cases. (occasionally used) OlogN O N , ON O N , ON2 O N 2
  7. IIR: methods are difficult to implement successfully and pose certain difficulties, but are sometimes used in some applications, for example noise cancellation of low frequency noise (very occasionally used)
  8. CMA: very useful when applicable (blind equalization); CMA is the method for blind equalizer initialization (commonly used in a few specific equalization applications) ON O N

Note:

In general, getting adaptive filters to work well in an application is much more challenging than, say, FFTs or IIR filters; they generally require lots of tweaking!

References

  1. B. Widrow and S.D. Stearns. (1985). Adaptive Signal Processing. [Good on applications, LMS]. Prentice-Hall.
  2. C.F.N. Cowan and P.M. Grant. (1985). Adaptive Filters. [Good overview of lots of topics]. Prentice-Hall.
  3. J.R. Treichler, C.R. Johnson and M.G. Larimore. (1987). Theory and Design of Adaptive Filters. [Good introduction to adaptive filtering, CMA; nice coverage of hardware]. Wiley-Interscience.
  4. M.L. Honig and D.G. Messerschmidt. (1984). Adaptive Filters: Structures, Algorithms, and Applications. [Good coverage of lattice algorithms]. Kluwer.
  5. S. Haykin. (1986). Adaptive Filters Theory. [Nice coverage of adaptive filter theory; Good reference]. Prentice-Hall.

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A lens is a custom view of the content in the repository. You can think of it as a fancy kind of list that will let you see content through the eyes of organizations and people you trust.

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