Optimally Distributed Kalman Filtering with Data-Driven Communication†
AbstractFor 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
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Dormann, K.; Noack, B.; Hanebeck, U.D. Optimally Distributed Kalman Filtering with Data-Driven Communication. Sensors 2018, 18, 1034.
Dormann K, Noack B, Hanebeck UD. Optimally Distributed Kalman Filtering with Data-Driven Communication. Sensors. 2018; 18(4):1034.Chicago/Turabian Style
Dormann, Katharina; Noack, Benjamin; Hanebeck, Uwe D. 2018. "Optimally Distributed Kalman Filtering with Data-Driven Communication." Sensors 18, no. 4: 1034.
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