Optimally Distributed Kalman Filtering with Data-Driven Communication

• Published in: Sensors (Basel, Switzerland) Vol. 18; no. 4; p. 1034
• Main Authors: Dormann, Katharina, Noack, Benjamin, Hanebeck, Uwe D
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.03.2018; MDPI
• Subjects: Communication; Computer simulation; Data integration; data-driven communication; distributed data fusion; distributed Kalman Filtering; Filtration; Kalman filters; Multisensor fusion; sensor networks; State estimation; distributed data fusion; data-driven communication; distributed Kalman Filtering; sensor networks
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s18041034

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.