Details
| Original language | English |
|---|---|
| Title of host publication | 2022 DGON Inertial Sensors and Systems, ISS 2022 - Proceedings |
| Editors | Peter Hecker |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (electronic) | 9781665490214 |
| ISBN (print) | 978-1-6654-9022-1 |
| Publication status | Published - 2022 |
| Event | 2022 DGON Inertial Sensors and Systems, ISS 2022 - Braunschweig, Germany Duration: 13 Sept 2022 → 14 Sept 2022 |
Publication series
| Name | International Symposium on Inertial Sensors and Systems |
|---|---|
| ISSN (Print) | 2377-3464 |
| ISSN (electronic) | 2377-3480 |
Abstract
The results of an observability analysis of a CAI-based IMU sensor fusion model, based on findings from an extensive analysis of inertial datasets collected over the last 10 years at the Institut für Erdmessung of the Leibniz University of Hannover, are presented. Datasets are analysed with respect to characteristic peaks occurring in the body frame during acceleration, deceleration, and turn maneuvers. This is done for IMU datasets recorded on board trains and cars. Based on these findings, 'characteristic' maneuvers are derived for the forward (x) and right (y) axis of the accelerometer, and the z axis of the gyroscope in the body frame. Maneuvers are derived by ranking multiple possible function fits on a RMSE-based evaluation method. This results in best fitting functions which are used to confirm the observability of different systematic IMU error terms with respect to a CAI-based reference sensor. Turn maneuvers result in dynamics across both accelerometer and the gyroscope axes, which in turn leads to observability of misalignments. For acceleration and deceleration maneuvers, only the longitudinal axis of the vehicle exhibits changes in acceleration, which should also be sufficient to estimate the misalignment terms between the conventional IMU and the CAI-based sensor. Meanwhile, the lever arm (displacement) between the CAI and IMU cannot be reliably estimated by maneuvers considered here, as it requires significant angular rates along two axes. A solution to this problem could be the oscillation due to suspension visible in the car-based datasets, which have a frequency of 0.2-1 Hz, and an amplitude of up to 0.1 rad/s. Based on these results, a follow-up study is suggested with real CAI sensor measurements to estimate the impact of such slow oscillations on the sensor solution.
ASJC Scopus subject areas
- Mathematics(all)
- Control and Optimization
- Physics and Astronomy(all)
- Instrumentation
Sustainable Development Goals
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
2022 DGON Inertial Sensors and Systems, ISS 2022 - Proceedings. ed. / Peter Hecker. Institute of Electrical and Electronics Engineers Inc., 2022. (International Symposium on Inertial Sensors and Systems).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Challenges for a hybrid CAI-based INS due to trajectory dynamics derived from real inertial datasets
AU - Weddig, Nikolai Ben
AU - Tennstedt, Benjamin
AU - Schön, Steffen
N1 - Funding Information: This work was sponsored by BMWi, projects 50RK1957 (QGyro) and 50NA2106
PY - 2022
Y1 - 2022
N2 - The results of an observability analysis of a CAI-based IMU sensor fusion model, based on findings from an extensive analysis of inertial datasets collected over the last 10 years at the Institut für Erdmessung of the Leibniz University of Hannover, are presented. Datasets are analysed with respect to characteristic peaks occurring in the body frame during acceleration, deceleration, and turn maneuvers. This is done for IMU datasets recorded on board trains and cars. Based on these findings, 'characteristic' maneuvers are derived for the forward (x) and right (y) axis of the accelerometer, and the z axis of the gyroscope in the body frame. Maneuvers are derived by ranking multiple possible function fits on a RMSE-based evaluation method. This results in best fitting functions which are used to confirm the observability of different systematic IMU error terms with respect to a CAI-based reference sensor. Turn maneuvers result in dynamics across both accelerometer and the gyroscope axes, which in turn leads to observability of misalignments. For acceleration and deceleration maneuvers, only the longitudinal axis of the vehicle exhibits changes in acceleration, which should also be sufficient to estimate the misalignment terms between the conventional IMU and the CAI-based sensor. Meanwhile, the lever arm (displacement) between the CAI and IMU cannot be reliably estimated by maneuvers considered here, as it requires significant angular rates along two axes. A solution to this problem could be the oscillation due to suspension visible in the car-based datasets, which have a frequency of 0.2-1 Hz, and an amplitude of up to 0.1 rad/s. Based on these results, a follow-up study is suggested with real CAI sensor measurements to estimate the impact of such slow oscillations on the sensor solution.
AB - The results of an observability analysis of a CAI-based IMU sensor fusion model, based on findings from an extensive analysis of inertial datasets collected over the last 10 years at the Institut für Erdmessung of the Leibniz University of Hannover, are presented. Datasets are analysed with respect to characteristic peaks occurring in the body frame during acceleration, deceleration, and turn maneuvers. This is done for IMU datasets recorded on board trains and cars. Based on these findings, 'characteristic' maneuvers are derived for the forward (x) and right (y) axis of the accelerometer, and the z axis of the gyroscope in the body frame. Maneuvers are derived by ranking multiple possible function fits on a RMSE-based evaluation method. This results in best fitting functions which are used to confirm the observability of different systematic IMU error terms with respect to a CAI-based reference sensor. Turn maneuvers result in dynamics across both accelerometer and the gyroscope axes, which in turn leads to observability of misalignments. For acceleration and deceleration maneuvers, only the longitudinal axis of the vehicle exhibits changes in acceleration, which should also be sufficient to estimate the misalignment terms between the conventional IMU and the CAI-based sensor. Meanwhile, the lever arm (displacement) between the CAI and IMU cannot be reliably estimated by maneuvers considered here, as it requires significant angular rates along two axes. A solution to this problem could be the oscillation due to suspension visible in the car-based datasets, which have a frequency of 0.2-1 Hz, and an amplitude of up to 0.1 rad/s. Based on these results, a follow-up study is suggested with real CAI sensor measurements to estimate the impact of such slow oscillations on the sensor solution.
UR - http://www.scopus.com/inward/record.url?scp=85142255377&partnerID=8YFLogxK
U2 - 10.1109/ISS55898.2022.9926311
DO - 10.1109/ISS55898.2022.9926311
M3 - Conference contribution
AN - SCOPUS:85142255377
SN - 978-1-6654-9022-1
T3 - International Symposium on Inertial Sensors and Systems
BT - 2022 DGON Inertial Sensors and Systems, ISS 2022 - Proceedings
A2 - Hecker, Peter
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 DGON Inertial Sensors and Systems, ISS 2022
Y2 - 13 September 2022 through 14 September 2022
ER -