Abstract
The unknown system error is widespread, but it is difficult to be verified or corrected. Furthermore, it also will yield to relatively large filtering errors. As an effective solution, the incremental equation is introduced, which can eliminate these unknown system errors. Meanwhile, the accuracy of state estimators will be improved. Then, a kind of distributed fusion incremental Kalman filter is presented in this paper. It can greatly improve the accuracy of state estimation for the multisensor systems under poor observation condition. The proposed algorithm is easy to be applied in engineering practice because of its simple form and small computational burden so. The simulation results show that it is effective and feasible.
| Original language | English |
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| Title of host publication | ICSEng 2018 26th international conference on systems engineering |
| Subtitle of host publication | Conference proceedings |
| Place of Publication | New Jersey, USA |
| Publisher | IEEE |
| Number of pages | 3 |
| ISBN (Electronic) | 9781538678343 |
| ISBN (Print) | 9781538678350 (Print on demand) |
| DOIs | |
| Publication status | Published - 2018 |
| Event | 26th International Conference on Systems Engineering, ICSEng 2018: ICSEng 2018 - University of Technology Sydney, Sydney, Australia Duration: 18 Dec 2018 → 20 Dec 2018 http://www.icseng.com/previous/2018/ (Conference website) http://www.icseng.com/previous/2018/program.php (Program and papers) |
Conference
| Conference | 26th International Conference on Systems Engineering, ICSEng 2018 |
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| Country/Territory | Australia |
| City | Sydney |
| Period | 18/12/18 → 20/12/18 |
| Other | The conference will provide a high-level forum for scholars, researchers and engineers from all over the world to share their views, research achievements, explore the hot issues and exchange the new experiences and technologies in the fields of Systems Engineering. |
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