Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2021 IEEE 24th International Conference on Information Fusion, FUSION 2021 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Number of pages | 8 |
| ISBN (electronic) | 9781737749714 |
| ISBN (print) | 978-1-6654-1427-2 |
| Publication status | Published - 2021 |
| Event | 24th IEEE International Conference on Information Fusion, FUSION 2021 - Sun City, South Africa Duration: 1 Nov 2021 → 4 Nov 2021 |
Abstract
Keywords
- 6 DoF, Georeferencing, Implicit observation model, Monte Carlo simulation, MSS, Particle filter
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Signal Processing
- Decision Sciences(all)
- Information Systems and Management
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Proceedings of 2021 IEEE 24th International Conference on Information Fusion, FUSION 2021. Institute of Electrical and Electronics Engineers Inc., 2021.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Information-Based Georeferencing of Multi-Sensor-Systems by Particle Filter with Implicit Measurement Equations
AU - Moftizadeh, Rozhin
AU - Vogel, Soren
AU - Dorndorf, Alexander
AU - Jungerink, Jan
AU - Alkhatib, Hamza
N1 - Funding Information: This research was funded by the Deutsche Forschungsge-meinschaft (DFG, German Research Foundation) - NE 1453/5-1 and as part of the Research Training Group i.c.sens [RTG 2159]. The computations were performed by the compute cluster, which is funded by the Leibniz University of Hanover, the Lower Saxony Ministry of Science and Culture (MWK) and DFG.
PY - 2021
Y1 - 2021
N2 - Multi-Sensor-System (MSS) georeferencing is a challenging task in engineering that should be dealt with in the most reliable way possible. The most straight forward way for localizing a MSS is to rely on the Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data. However, these data might not be always reliable enough or even available. Therefore, suitable filtering techniques are required to deal with such problems and to increase the reliability of the estimated states. In global localization and when it comes to real scenarios, particle filters are proven to deliver more realistic results than Kalman filter realizations. However, in MSS georeferencing where multiple sensors are used, different observation models are needed some of which could be of implicit type. In such a case, the likelihood estimation is challenging due to impossibility of estimating the observations by means of the generated samples. Therefore, the current paper offers a new particle filter methodology that can handle both implicit and explicit observation models. Final results of this methodology, which is applied on a simulated environment for georeferencing a MSS, are shown to be satisfactory.
AB - Multi-Sensor-System (MSS) georeferencing is a challenging task in engineering that should be dealt with in the most reliable way possible. The most straight forward way for localizing a MSS is to rely on the Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data. However, these data might not be always reliable enough or even available. Therefore, suitable filtering techniques are required to deal with such problems and to increase the reliability of the estimated states. In global localization and when it comes to real scenarios, particle filters are proven to deliver more realistic results than Kalman filter realizations. However, in MSS georeferencing where multiple sensors are used, different observation models are needed some of which could be of implicit type. In such a case, the likelihood estimation is challenging due to impossibility of estimating the observations by means of the generated samples. Therefore, the current paper offers a new particle filter methodology that can handle both implicit and explicit observation models. Final results of this methodology, which is applied on a simulated environment for georeferencing a MSS, are shown to be satisfactory.
KW - 6 DoF
KW - Georeferencing
KW - Implicit observation model
KW - Monte Carlo simulation
KW - MSS
KW - Particle filter
UR - http://www.scopus.com/inward/record.url?scp=85123400140&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85123400140
SN - 978-1-6654-1427-2
BT - Proceedings of 2021 IEEE 24th International Conference on Information Fusion, FUSION 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th IEEE International Conference on Information Fusion, FUSION 2021
Y2 - 1 November 2021 through 4 November 2021
ER -