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Sensors 2018, 18(4), 1034;

Optimally Distributed Kalman Filtering with Data-Driven Communication

Robert Bosch GmbH, 71636 Ludwigsburg, Germany
Intelligent Sensor-Actuator-Systems Laboratory (ISAS), Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
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.
Author to whom correspondence should be addressed.
Received: 1 March 2018 / Revised: 23 March 2018 / Accepted: 27 March 2018 / Published: 29 March 2018
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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

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Dormann, K.; Noack, B.; Hanebeck, U.D. Optimally Distributed Kalman Filtering with Data-Driven Communication. Sensors 2018, 18, 1034.

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