Details
| Original language | English |
|---|---|
| Title of host publication | 2018 21st International Conference on Information Fusion |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 889-896 |
| Number of pages | 8 |
| ISBN (electronic) | 978-0-9964527-6-2 |
| ISBN (print) | 978-1-5386-4330-3 |
| Publication status | Published - 5 Sept 2018 |
| Event | 21st International Conference on Information Fusion, FUSION 2018 - Cambridge, United Kingdom (UK) Duration: 10 Jul 2018 → 13 Jul 2018 |
Abstract
In this paper, a novel set-membership Kalman filter is applied on a data set which is obtained from a real world experiment. In this experiment, taken from the scope of georeferencing of terrestrial laser scanner, a multi-sensor system has captured the trajectory of two GNSS antennas. The dynamical system contains the random uncertainty and set-membership uncertainty simultaneously. Both estimated results from classic extended Kalman filter and novel set-membership Kalman filter are shown and compared. Detailed analysis of the set-membership Kalman filter is given in the end.
Keywords
- multi-sensor system, set-membership Kalman filter, state estimation
ASJC Scopus subject areas
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Signal Processing
- Decision Sciences(all)
- Statistics, Probability and Uncertainty
- Physics and Astronomy(all)
- Instrumentation
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2018 21st International Conference on Information Fusion. Institute of Electrical and Electronics Engineers Inc., 2018. p. 889-896 8455763.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Geo-Referencing of a Multi-Sensor System Based on Set-Membership Kalman Filter
AU - Sun, Ligang
AU - Alkhatib, Hamza
AU - Paffenholz, Jens André
AU - Neumann, Ingo
N1 - Funding Information: This work was supported by the German Research Foundation (DFG) as part of the Research Training Group i.c.sens (RTG 2159). The authors greatly acknowledge Dr. Yi Zhang in Leibniz Universität Hannover for his helpful suggestion on the computation of the experiment. The authors also thank the five anonymous reviewers for their valuable comments. Publisher Copyright: © 2018 ISIF Copyright: Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/9/5
Y1 - 2018/9/5
N2 - In this paper, a novel set-membership Kalman filter is applied on a data set which is obtained from a real world experiment. In this experiment, taken from the scope of georeferencing of terrestrial laser scanner, a multi-sensor system has captured the trajectory of two GNSS antennas. The dynamical system contains the random uncertainty and set-membership uncertainty simultaneously. Both estimated results from classic extended Kalman filter and novel set-membership Kalman filter are shown and compared. Detailed analysis of the set-membership Kalman filter is given in the end.
AB - In this paper, a novel set-membership Kalman filter is applied on a data set which is obtained from a real world experiment. In this experiment, taken from the scope of georeferencing of terrestrial laser scanner, a multi-sensor system has captured the trajectory of two GNSS antennas. The dynamical system contains the random uncertainty and set-membership uncertainty simultaneously. Both estimated results from classic extended Kalman filter and novel set-membership Kalman filter are shown and compared. Detailed analysis of the set-membership Kalman filter is given in the end.
KW - multi-sensor system
KW - set-membership Kalman filter
KW - state estimation
UR - http://www.scopus.com/inward/record.url?scp=85050639812&partnerID=8YFLogxK
U2 - 10.23919/ICIF.2018.8455763
DO - 10.23919/ICIF.2018.8455763
M3 - Conference contribution
AN - SCOPUS:85050639812
SN - 978-1-5386-4330-3
SP - 889
EP - 896
BT - 2018 21st International Conference on Information Fusion
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st International Conference on Information Fusion, FUSION 2018
Y2 - 10 July 2018 through 13 July 2018
ER -