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      CRLB based optimal noise enhanced parameter estimation using quantized observations

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      Author(s)
      Balkan, G. O.
      Gezici, Sinan
      Date
      2010-02-22
      Source Title
      IEEE Signal Processing Letters
      Print ISSN
      1070-9908
      Publisher
      IEEE
      Volume
      17
      Issue
      5
      Pages
      477 - 480
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      In this letter, optimal additive noise is characterized for parameter estimation based on quantized observations. First, optimal probability distribution of noise that should be added to observations is formulated in terms of a CramerRao lower bound (CRLB) minimization problem. Then, it is proven that optimal additive noise can be represented by a constant signal level, which means that randomization of additive signal levels is not needed for CRLB minimization. In addition, the results are extended to the cases in which there exists prior information about the unknown parameter and the aim is to minimize the Bayesian CRLB (BCRLB). Finally, a numerical example is presented to explain the theoretical results.
      Keywords
      Cramer-Rao lower bound
      Estimation
      Noise enhanced estimation
      Quantization
      Permalink
      http://hdl.handle.net/11693/22353
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/LSP.2010.2043787
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      • Department of Electrical and Electronics Engineering 3863
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