@inproceedings{621a815a66614c3f8a4d3ee0332e921b,

title = "Two-dimensional transform domain adaptive filters based on one-dimensional orthogonal transforms",

abstract = "Recently it has been shown that two-dimensional (2-D) orthogonal transforms can be incorporated into 2-D FIR adaptive filters to improve the conditioning of the input auto correlation matrix eigenvalue spread, thereby improving the convergence rates for 2-D adaptive filters operating in colored noise. This paper considers two approaches to incorporating the transform. The first involves mapping the 2-D input data into a long 1-D vector, and then performing the orthogonalization with a 1-D sliding window orthogonal transform (the FFT is considered in this paper). The second approach performs the transform directly with a 2-D transform algorithm. Experiments demonstrate that similar reductions in eigenvalue spread result with both 1-D and 2-D transformations, both of which can greatly speed convergence in 2-D adaptive filters that have inherently slow convergence rates due to the large number of coefficients required in two dimensions.",

author = "Howard, {M. N.} and Soni, {R. A.} and Jenkins, {William Kenneth}",

year = "1993",

month = dec,

day = "1",

language = "English (US)",

isbn = "0818641207",

series = "Conference Record of the Asilomar Conference of Signals, Systems & Computers",

publisher = "Publ by IEEE",

pages = "1589--1593",

booktitle = "Conference Record of the Asilomar Conference of Signals, Systems & Computers",

note = "Proceedings of the 27th Asilomar Conference on Signals, Systems & Computers ; Conference date: 01-11-1993 Through 03-11-1993",

}