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White noise estimators for networked systems with packet dropouts

  • Chunyan Han
  • Wei Wang
  • Yuan Zhang
Control Theory

Abstract

This paper studies the optimal and suboptimal deconvolution problems over a network subject to random packet losses, which are modeled by an independent identically distributed Bernoulli process. By the projection formula, an optimal input white noise estimator is first presented with a stochastic Kalman filter. We show that this obtained deconvolution estimator is time-varying, stochastic, and it does not converge to a steady value. Then an alternative suboptimal input white-noise estimator with deterministic gains is developed under a new criterion. The estimator gain and its respective error covariance-matrix information are derived based on a new suboptimal state estimator. It can be shown that the suboptimal input white-noise estimator converges to a steady-state one under appropriate assumptions.

Keywords

Convergence analysis networked system packet dropout white noise estimation 

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Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Electrical EngineeringUniversity of JinanJinanP. R. China
  2. 2.School of Control Science and EngineeringShandong UniversityJinanP. R. China
  3. 3.Shandong Provincial Key Laboratory of Network Based Intelligent ComputingJinanP. R. China

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