Boston University Libraries OpenBU
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    •   OpenBU
    • BU Open Access Articles
    • BU Open Access Articles
    • View Item
    •   OpenBU
    • BU Open Access Articles
    • BU Open Access Articles
    • View Item

    Estimation from quantized Gaussian measurements: when and how to use dither

    Thumbnail
    Publisher Version
    10.1109/TSP.2019.2916046
    Author(s)
    Rapp, Joshua
    Dawson, Robin M. A.
    Goyal, Vivek
    Share to FacebookShare to TwitterShare by Email
    Export Citation
    Download to BibTex
    Download to EndNote/RefMan (RIS)
    Metadata
    Show full item record
    Permanent Link
    https://hdl.handle.net/2144/39076
    OA Version
    Accepted manuscript
    Citation (published version)
    Joshua Rapp, Robin MA Dawson, Vivek Goyal. "Estimation from Quantized Gaussian Measurements: When and How to Use Dither." IEEE Transactions on Signal Processing, https://doi.org/10.1109/TSP.2019.2916046
    Abstract
    Subtractive dither is a powerful method for removing the signal dependence of quantization noise for coarsely quantized signals. However, estimation from dithered measurements often naively applies the sample mean or midrange, even when the total noise is not well described with a Gaussian or uniform distribution. We show that the generalized Gaussian distribution approximately describes subtractively dithered, quantized samples of a Gaussian signal. Furthermore, a generalized Gaussian fit leads to simple estimators based on order statistics that match the performance of more complicated maximum likelihood estimators requiring iterative solvers. The order statistics-based estimators outperform both the sample mean and midrange for nontrivial sums of Gaussian and uniform noise. Additional analysis of the generalized Gaussian approximation yields rules of thumb for determining when and how to apply dither to quantized measurements. Specifically, we find subtractive dither to be beneficial when the ratio between the Gaussian standard deviation and quantization interval length is roughly less than one-third. When that ratio is also greater than 0.822/K^0.930 for the number of measurements K > 20, estimators we present are more efficient than the midrange.
    Collections
    • ENG: Electrical and Computer Engineering: Scholarly Papers [257]
    • BU Open Access Articles [3730]


    Boston University
    Contact Us | Send Feedback | Help
     

     

    Browse

    All of OpenBUCommunities & CollectionsIssue DateAuthorsTitlesSubjectsThis CollectionIssue DateAuthorsTitlesSubjects

    Deposit Materials

    LoginNon-BU Registration

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Boston University
    Contact Us | Send Feedback | Help