Quantized Identification and Asymptotic Efficiency

  • Le Yi Wang
  • G. George Yin
  • Ji-Feng Zhang
  • Yanlong Zhao
Part of the Systems & Control: Foundations & Applications book series (SCFA)


Up to this point, we have been treating binary-valued observations. The fundamental principles and basic algorithms for binary-valued observations can be modified to handle quantized observations as well. One way to understand the connection is to view a quantized observation as a vector-valued binary observation in which each vector component represents the output of one threshold, which is a binary-valued sensor. The dimension of the vector is the number of the thresholds in the quantized sensor.


Unbiased Estimator Quantization Error Uniform Quantization Minimum Variance Unbiased Estimator Rational Transfer Function 


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Le Yi Wang
    • 1
  • G. George Yin
    • 2
  • Ji-Feng Zhang
    • 3
  • Yanlong Zhao
    • 3
  1. 1.Department of Electrical and Computer EngineeringWayne State UniversityDetroitUSA
  2. 2.Department of MathematicsWayne State UniversityDetroitUSA
  3. 3.Key Laboratory of Systems and Control, Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina

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