Connexions

You are here: Home » Content » Summary of Adaptive Filtering Methods
Content Actions
Lenses

What is a lens?

Lenses

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

What is in a lens?

Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual Connexions member, a community, or a respected organization.

This content is ...
In these lenses
  • This module is included inLens: richb's DSP resources
    By: Richard BaraniukAs a part of collection:"Adaptive Filters"

    Comments:

    "A good introduction in adaptive filters, a major DSP application."

    Click the "richb's DSP" link to see all content selected in this lens.

    richb's DSP
Tags

(?)

These tags come from the endorsement, affiliation, and other lenses that include this content.

Summary of Adaptive Filtering Methods

Module by: Douglas L. Jones

  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.

Comments, questions, feedback, criticisms?

Send feedback