0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessSince the proposal of moving horizon (MH) estimation in 1960s, the MH estimation approach has drawn ever-increasing research interests due mainly to its inherent capability of handling complex nonlinear systems and constrained systems. Recent years have witnessed considerable progress on the theoretical and practical research of MH estimation. In this work, a bibliographical review is provided on the moving horizon estimation problem and its applications. The basic idea of MH estimation is first introduced in detail. Then recent advances of MH estimation according to the underlying systems are summarized. Furthermore, some applications of MH estimation are presented. Finally, some research challenges of MH estimation problem are outlined for the further research.
Lei Zou, Zidong Wang, Jun Hu, Qinglong Qinglong Han (2020). Moving horizon estimation meets multi-sensor information fusion: Development, opportunities and challenges. Information Fusion, 60, pp. 1-10, DOI: 10.1016/j.inffus.2020.01.009.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Article
Year
2020
Authors
4
Datasets
0
Total Files
0
Language
English
Journal
Information Fusion
DOI
10.1016/j.inffus.2020.01.009
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access