#
Optimally Distributed Kalman Filtering with Data-Driven Communication^{ †}

^{1}

^{2}

^{*}

^{†}

^{‡}

## Abstract

**:**

## 1. Introduction

- All nodes have to send their data at the same time, and
- the central cannot infer any information about the state between the sending times.

## 2. Centralized and Optimally Distributed Kalman Filtering

## 3. Distributed Kalman Filtering with Omitted Estimates

## 4. Data-Driven Distributed Kalman Filtering with Omitted Estimates over Multiple Time Steps–Version 1

## 5. Data-Driven Distributed Kalman Filtering with Omitted Estimates over Multiple Time Steps–Version 2

## 6. Simulations and Evaluation

## 7. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Appendix A

## Appendix B

**Proof.**

## Appendix C

**Proof.**

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**Figure 1.**MSEs and traces of the error covariance matrices are plotted relative to the communication rate. Each communication rate corresponds to one Monte Carlo simulation with 500 runs over 100 time steps. MSEs are shown as solid lines, traces are shown as dashed lines.

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**MDPI and ACS Style**

Dormann, K.; Noack, B.; Hanebeck, U.D. Optimally Distributed Kalman Filtering with Data-Driven Communication. *Sensors* **2018**, *18*, 1034.
https://doi.org/10.3390/s18041034

**AMA Style**

Dormann K, Noack B, Hanebeck UD. Optimally Distributed Kalman Filtering with Data-Driven Communication. *Sensors*. 2018; 18(4):1034.
https://doi.org/10.3390/s18041034

**Chicago/Turabian Style**

Dormann, Katharina, Benjamin Noack, and Uwe D. Hanebeck. 2018. "Optimally Distributed Kalman Filtering with Data-Driven Communication" *Sensors* 18, no. 4: 1034.
https://doi.org/10.3390/s18041034