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      Average fisher information optimization for quantized measurements using additive independent noise

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      Author
      Balkan, Gokce Osman
      Gezici, Sinan
      Date
      2010
      Source Title
      2010 IEEE 18th Signal Processing and Communications Applications Conference
      Publisher
      IEEE
      Pages
      177 - 180
      Language
      Turkish
      Type
      Conference Paper
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      Abstract
      Adding noise to nonlinear systems can enhance their performance. Additive noise benefits are observed also in parameter estimation problems based on quantized observations. In this study, the purpose is to find the optimal probability density function of additive noise, which is applied to observations before quantization, in those problems. First, optimal probability density function of noise is formulated in terms of an average Fisher information maximization problem. Then, it is proven that optimal additive "noise" can be represented by a constant signal level. This result, which means that randomization of additive signal levels is not needed for average Fisher information maximization, is supported with two numerical examples. ©2010 IEEE.
      Keywords
      Fisher information
      Independent noise
      Numerical example
      Quantized measurements
      Quantized observations
      Signal level
      Additive noise
      Fisher information matrix
      Optimization
      Parameter estimation
      Signal processing
      Probability density function
      Permalink
      http://hdl.handle.net/11693/28469
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/SIU.2010.5653626
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      • Department of Electrical and Electronics Engineering 3524
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