Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/s18041034 | SENSORS |
Keywords | Field | DocType |
distributed Kalman Filtering,data-driven communication,distributed data fusion,sensor networks | Distributed filtering,Data-driven,Fusion,Real-time computing,Electronic engineering,Kalman filter,Sensor fusion,Fusion center,Engineering,Wireless sensor network | Journal |
Volume | Issue | ISSN |
18 | 4.0 | 1424-8220 |
Citations | PageRank | References |
2 | 0.43 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Katharina Dormann | 1 | 2 | 0.43 |
| Benjamin Noack | 2 | 168 | 23.73 |
| Uwe D. Hanebeck | 3 | 944 | 133.52 |