Next Article in Journal
Object-Based Dense Matching Method for Maintaining Structure Characteristics of Linear Buildings
Next Article in Special Issue
Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening
Previous Article in Journal
Screen-Printed Electrodes Modified with “Green” Metals for Electrochemical Stripping Analysis of Toxic Elements
Open AccessArticle

Optimally Distributed Kalman Filtering with Data-Driven Communication

1
Robert Bosch GmbH, 71636 Ludwigsburg, Germany
2
Intelligent Sensor-Actuator-Systems Laboratory (ISAS), Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Dormann, K.; Noack, B.; Hanebeck, U.D. Distributed Kalman Filtering With Reduced Transmission Rate, In Proceedings of the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017), Daegu, South Korea, 16–18 November 2017.
These authors contributed equally to this work.
Sensors 2018, 18(4), 1034; https://doi.org/10.3390/s18041034
Received: 1 March 2018 / Revised: 23 March 2018 / Accepted: 27 March 2018 / Published: 29 March 2018
For multisensor data fusion, distributed state estimation techniques that enable a local processing of sensor data are the means of choice in order to minimize storage and communication costs. In particular, a distributed implementation of the optimal Kalman filter has recently been developed. A significant disadvantage of this algorithm is that the fusion center needs access to each node so as to compute a consistent state estimate, which requires full communication each time an estimate is requested. In this article, different extensions of the optimally distributed Kalman filter are proposed that employ data-driven transmission schemes in order to reduce communication expenses. As a first relaxation of the full-rate communication scheme, it can be shown that each node only has to transmit every second time step without endangering consistency of the fusion result. Also, two data-driven algorithms are introduced that even allow for lower transmission rates, and bounds are derived to guarantee consistent fusion results. Simulations demonstrate that the data-driven distributed filtering schemes can outperform a centralized Kalman filter that requires each measurement to be sent to the center node. View Full-Text
Keywords: distributed Kalman Filtering; data-driven communication; distributed data fusion; sensor networks distributed Kalman Filtering; data-driven communication; distributed data fusion; sensor networks
Show Figures

Figure 1

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

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop