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Updated: Feb 12, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
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Optimally Distributed Kalman Filtering with Data-Driven Communication.

Katharina Dormann1, Benjamin Noack2, Uwe D Hanebeck3

  • 1Robert Bosch GmbH, 71636 Ludwigsburg, Germany. katharina.dormann@gmx.de.

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|March 30, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces data-driven transmission schemes for distributed Kalman filtering, reducing communication costs in multisensor data fusion. These methods maintain estimate consistency while significantly lowering data transmission needs.

Keywords:
data-driven communicationdistributed Kalman Filteringdistributed data fusionsensor networks

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Area of Science:

  • Engineering
  • Computer Science
  • Signal Processing

Background:

  • Distributed state estimation minimizes storage and communication costs in multisensor data fusion.
  • A distributed Kalman filter offers optimal state estimation but requires full communication.
  • High communication overhead limits the practical application of existing distributed Kalman filters.

Purpose of the Study:

  • To propose extensions of the distributed Kalman filter using data-driven transmission schemes.
  • To reduce communication expenses in distributed state estimation without compromising accuracy.
  • To develop algorithms that enable lower transmission rates while ensuring consistent fusion results.

Main Methods:

  • Developing extensions to the optimally distributed Kalman filter.
  • Implementing data-driven transmission schemes for reduced communication.
  • Deriving bounds to guarantee consistent fusion results.
  • Simulating and comparing performance against centralized Kalman filters.

Main Results:

  • Each node can transmit every second time step without losing consistency.
  • Two data-driven algorithms achieve even lower transmission rates.
  • Guaranteed consistent fusion results are achieved with derived bounds.
  • Data-driven schemes outperform centralized Kalman filters in simulations.

Conclusions:

  • Data-driven transmission schemes effectively reduce communication costs in distributed Kalman filtering.
  • The proposed methods offer a practical solution for efficient multisensor data fusion.
  • These advancements enable more scalable and cost-effective distributed estimation systems.