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Summary
This summary is machine-generated.

This study introduces an event-triggered particle filter for remote state estimation in nonlinear systems, reducing data transmission and handling packet drops effectively. The method ensures accurate estimation despite non-Gaussian conditions caused by data loss.

Keywords:
Cramér–Rao lower boundEvent-based state estimationNonlinear filteringPacket lossParticle filtering

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Area of Science:

  • Control Systems Engineering
  • Signal Processing
  • Information Theory

Background:

  • Remote state estimation is crucial for monitoring complex systems.
  • Nonlinear discrete systems present unique challenges for estimation.
  • Event-triggered communication and packet drops degrade estimation accuracy.

Purpose of the Study:

  • To develop an event-triggered particle filter for nonlinear discrete systems.
  • To address challenges posed by packet drops and reduce communication load.
  • To achieve accurate state estimation under non-Gaussian conditions.

Main Methods:

  • Sensor scheduling using the SOD mechanism to minimize data transmission.
  • Modeling packet drops using Bernoulli-distributed random variables.
  • Developing an event-trigger particle filter algorithm incorporating non-linearity and non-Gaussianity.

Main Results:

  • Derived an explicit likelihood function considering event triggers and packet drops.
  • Approximated the posterior distribution using a sequential Monte-Carlo algorithm.
  • Assessed estimator performance by comparing error covariance with the posterior Cramér-Rao lower bound.

Conclusions:

  • The proposed event-trigger particle filter effectively reduces communication burden.
  • The algorithm achieves appropriate estimation accuracy in nonlinear systems with packet drops.
  • Numerical examples validate the effectiveness of the developed estimation strategy.