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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Reinhardt, M. Noack, B. Hanebeck, U.D. |
| Copyright Year | 2013 |
| Description | Author affiliation: Intell. Sensor-Actuator-Syst. Lab. (ISAS), Karlsruhe Inst. of Technol. (KIT), Karlsruhe, Germany (Reinhardt, M.; Noack, B.; Hanebeck, U.D.) |
| Abstract | In this paper, linear distributed estimation is revisited on the basis of the hypothesizing distributed Kalman filter and equations for a flexible application of the algorithm are derived. We propose a new approximation for the mean-squared-error matrix and present techniques for automatically improving the hypothesis about the global measurement model. Utilizing these extensions, the precision of the filter is improved so that it asymptotically yields optimal results for time-invariant models. Pseudo-code for the implementation of the algorithm is provided and the lossless inclusion of out-of-sequence measurements is discussed. An evaluation demonstrates the effect of the new extensions and compares the results to state-of-the-art methods. |
| Starting Page | 77 |
| Ending Page | 84 |
| File Size | 493108 |
| Page Count | 8 |
| File Format | |
| ISBN | 9786058631113 |
| e-ISBN | 9781479902842 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-07-09 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | ISIF ( Intl Society of Information Fusi |
| Subject Keyword | Approximation methods Mathematical model Estimation Time measurement Gain measurement Kalman filters Noise Track-to-Track Fusion (T2TF) Kalman Filtering Distributed Estimation Sensor-networks |
| Content Type | Text |
| Resource Type | Article |
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