ADAPTIVE DIGITAL FILTERS USING THE WALSH-HADAMARD TRANSFORM.

William Kenneth Jenkins, J. R. Kreidle

    Research output: Contribution to journalConference article

    5 Citations (Scopus)

    Abstract

    The least-mean-square (LMS) adaptive algorithm is probably the best known and most widely used real-time adaptive filtering algorithm due to its simple computational requirements. However, as VLSI digital processors become cheaper and more readily available, the question arises as to whether more effective real-time algorithms can be found that take advantage of increased computational resources as they become available. It has been shown in the literature that a real time decomposition of the incoming signal into a set of orthogonal components, and a subsequent adaptation on these individual components, leads to improved performance. The authors discuss the role of orthogonal transformation in adaptive noise canceling and demonstrate the use of the Walsh-Hadamard transform (WHT) for improving performance. The results show that the WHT is capable of decomposing the input signal into orthogonal channels so that the transform domain can be whitened, although the effects of leakage prevent the orthogonalization from being complete.

    Original languageEnglish (US)
    Pages (from-to)875-878
    Number of pages4
    JournalProceedings - IEEE International Symposium on Circuits and Systems
    StatePublished - Jan 1 1986

    Fingerprint

    Walsh transforms
    Hadamard transforms
    Adaptive filters
    Digital filters
    Adaptive filtering
    Adaptive algorithms
    Decomposition

    All Science Journal Classification (ASJC) codes

    • Electrical and Electronic Engineering

    Cite this

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    title = "ADAPTIVE DIGITAL FILTERS USING THE WALSH-HADAMARD TRANSFORM.",
    abstract = "The least-mean-square (LMS) adaptive algorithm is probably the best known and most widely used real-time adaptive filtering algorithm due to its simple computational requirements. However, as VLSI digital processors become cheaper and more readily available, the question arises as to whether more effective real-time algorithms can be found that take advantage of increased computational resources as they become available. It has been shown in the literature that a real time decomposition of the incoming signal into a set of orthogonal components, and a subsequent adaptation on these individual components, leads to improved performance. The authors discuss the role of orthogonal transformation in adaptive noise canceling and demonstrate the use of the Walsh-Hadamard transform (WHT) for improving performance. The results show that the WHT is capable of decomposing the input signal into orthogonal channels so that the transform domain can be whitened, although the effects of leakage prevent the orthogonalization from being complete.",
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    ADAPTIVE DIGITAL FILTERS USING THE WALSH-HADAMARD TRANSFORM. / Jenkins, William Kenneth; Kreidle, J. R.

    In: Proceedings - IEEE International Symposium on Circuits and Systems, 01.01.1986, p. 875-878.

    Research output: Contribution to journalConference article

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    AU - Jenkins, William Kenneth

    AU - Kreidle, J. R.

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    N2 - The least-mean-square (LMS) adaptive algorithm is probably the best known and most widely used real-time adaptive filtering algorithm due to its simple computational requirements. However, as VLSI digital processors become cheaper and more readily available, the question arises as to whether more effective real-time algorithms can be found that take advantage of increased computational resources as they become available. It has been shown in the literature that a real time decomposition of the incoming signal into a set of orthogonal components, and a subsequent adaptation on these individual components, leads to improved performance. The authors discuss the role of orthogonal transformation in adaptive noise canceling and demonstrate the use of the Walsh-Hadamard transform (WHT) for improving performance. The results show that the WHT is capable of decomposing the input signal into orthogonal channels so that the transform domain can be whitened, although the effects of leakage prevent the orthogonalization from being complete.

    AB - The least-mean-square (LMS) adaptive algorithm is probably the best known and most widely used real-time adaptive filtering algorithm due to its simple computational requirements. However, as VLSI digital processors become cheaper and more readily available, the question arises as to whether more effective real-time algorithms can be found that take advantage of increased computational resources as they become available. It has been shown in the literature that a real time decomposition of the incoming signal into a set of orthogonal components, and a subsequent adaptation on these individual components, leads to improved performance. The authors discuss the role of orthogonal transformation in adaptive noise canceling and demonstrate the use of the Walsh-Hadamard transform (WHT) for improving performance. The results show that the WHT is capable of decomposing the input signal into orthogonal channels so that the transform domain can be whitened, although the effects of leakage prevent the orthogonalization from being complete.

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