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Related Experiment Video

Updated: Sep 20, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Quantized recursive filtering for networked systems with stochastic transmission delays.

Zhongyi Zhao¹, Xiaojian Yi², Lifeng Ma³

¹College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China.

ISA Transactions

|June 7, 2022

Summary

This summary is machine-generated.

This study addresses networked systems with uniform quantization and transmission delays. A novel Kalman-type filter minimizes filtering error covariance, ensuring robust performance in networked systems.

Keywords:

Networked systems Newest timestamp Recursive filtering Riccati-like equations Stochastic transmission delays Uniform quantization

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Area of Science:

Control Systems Engineering
Signal Processing
Networked Systems

Background:

Networked systems face challenges from data quantization and unpredictable transmission delays.
Accurate state estimation is crucial for effective control and monitoring in these systems.

Purpose of the Study:

To design a robust Kalman-type filter for networked systems with uniform quantization and stochastic transmission delays.
To guarantee and minimize the upper bound of the filtering error covariance.

Main Methods:

Utilizing an indicator function to manage stochastic transmission delays and ensure data integrity.
Employing an augmented system approach to create a delay-free system model.
Applying stochastic analysis and solving Riccati-like difference equations to derive the error covariance bound.

Main Results:

A novel Kalman-type filter design is presented for the augmented system.
The filtering error covariance upper bound is recursively derived and minimized.
The effectiveness of the developed filtering scheme is validated through a numerical example.

Conclusions:

The proposed filtering approach effectively handles uniform quantization and stochastic delays in networked systems.
The method guarantees a minimized upper bound for the filtering error covariance.
The research provides a valuable tool for state estimation in complex networked environments.