Nonparametric One-Bit quantizers for distributed estimation

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21 Scopus citations


In this paper, we consider the nonparametric distributed parameter estimation problem using one-bit quantized data from peripheral sensors. Assuming that the sensor observations are bounded, nonparametric distributed estimators are proposed based on the knowledge of the first N moments of sensor noises. These estimators are shown to be either unbiased or asymptotically unbiased with bounded and known estimation variance. Further, the uniformly optimal quantizer based only on the first moment information and the optimal minimax quantizer with the knowledge of the first two moments are determined. The proposed estimators are shown to be consistent even when local sensor noises are not independent but m-dependent. The relationship between the proposed approaches and dithering in quantization is also investigated. The superiority of the proposed quantization/estimation schemes is illustrated via illustrative examples.

Original languageEnglish (US)
Article number5438809
Pages (from-to)3777-3787
Number of pages11
JournalIEEE Transactions on Signal Processing
Issue number7
StatePublished - Jul 2010


  • Data fusion
  • Dependent observations
  • Distributed parameter estimation
  • Nonparametric quantization
  • Nonsubtractive dithering
  • Quantization

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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