Details
Original language | English |
---|---|
Title of host publication | 2025 IEEE/ION Position, Location and Navigation Symposium, PLANS 2025 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 68-79 |
Number of pages | 12 |
ISBN (electronic) | 9798331523176 |
ISBN (print) | 979-8-3315-2318-3 |
Publication status | Published - 28 Apr 2025 |
Event | 2025 IEEE/ION Position, Location and Navigation Symposium: PLANS 2025 - Marriott Salt Lake Downtown City Creek, Salt Lake City, United States Duration: 28 Apr 2025 → 1 May 2025 |
Abstract
An observability analysis of a Quantum Inertial Navigation System (QINS) is presented for multiple realistic dynamic scenarios. It is performed on an Error State Extended Kalman Filter (ESEKF), which contains loosely coupled position and velocity measurements and 3-axis differential Cold Atom Interferometer (CAI) sensor measurements. The CAI-based measurements are hybridized with conventional IMU measurements, which results, in combination with position and velocity estimates, in a filter structure that contains position, velocity, acceleration and angular-rate based observations of the system at the same time. This in turn results in increased estimability and observability of the system, as well as lower position, velocity and attitude drift. As CAI-based measurements are only available for low measurement frequencies (i.e. 1-10 Hz), and are also only valid for low dynamics, the improvement in estimability has to be evaluated in realistic scenarios.To this end, realistic trajectories (low frequency deterministic movement) and realistic vibrations (high frequency correlated deterministic movements) are generated and combined for this analysis. With this data, a numerical observability analysis is performed for different combinations of GNSS-based and CAI-based measurements. Furthermore, differences in estimability and observability between vehicle types (cars, aircrafts, trains or ships) are shown.The results demonstrate that, as in a conventional GNSS-IMU sensor fusion, dynamics improve the observability of e.g. scale factors, lever arm components, or misalignment terms. The inclusion of misalignments in the ESEKF, orientation difference between the CAI and IMU, and the introduction of larger lever arms between the CAI and IMU leads to increased dependencies between different bias terms of the IMU, but also between components of the lever arm and misalignments at the CAI-IMU level. They are accentuated when larger vehicle-dependent oscillations are introduced in the system, which is demonstrated by an analysis of singular vectors of the Fisher Information Matrix (FIM).The article provides relevant information about tradeoffs between CAI-IMU model complexity and occurring dynamics, and it gives insights which components of the system need to be pre-calibrated, as their on the fly estimation may lead to an insufficiently resolved state, due to increased dependencies.
Keywords
- ESEKF, Kalman Filtering, Numerical analysis, Observability, Quantum inertial navigation, QUINS, Strapdown Navigation
ASJC Scopus subject areas
- Engineering(all)
- Aerospace Engineering
- Engineering(all)
- Automotive Engineering
- Engineering(all)
- Electrical and Electronic Engineering
- Materials Science(all)
- Electronic, Optical and Magnetic Materials
- Physics and Astronomy(all)
- Instrumentation
- Mathematics(all)
- Control and Optimization
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2025 IEEE/ION Position, Location and Navigation Symposium, PLANS 2025. Institute of Electrical and Electronics Engineers Inc., 2025. p. 68-79.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Observability and Estimability Analysis of a Hybrid Error State CAI-IMU Filter for Different Dynamic Environments
AU - Weddig, Nicolai Ben
AU - Tennstedt, Benjamin
AU - Schon, Steffen
N1 - Publisher Copyright: © 2025 IEEE.
PY - 2025/4/28
Y1 - 2025/4/28
N2 - An observability analysis of a Quantum Inertial Navigation System (QINS) is presented for multiple realistic dynamic scenarios. It is performed on an Error State Extended Kalman Filter (ESEKF), which contains loosely coupled position and velocity measurements and 3-axis differential Cold Atom Interferometer (CAI) sensor measurements. The CAI-based measurements are hybridized with conventional IMU measurements, which results, in combination with position and velocity estimates, in a filter structure that contains position, velocity, acceleration and angular-rate based observations of the system at the same time. This in turn results in increased estimability and observability of the system, as well as lower position, velocity and attitude drift. As CAI-based measurements are only available for low measurement frequencies (i.e. 1-10 Hz), and are also only valid for low dynamics, the improvement in estimability has to be evaluated in realistic scenarios.To this end, realistic trajectories (low frequency deterministic movement) and realistic vibrations (high frequency correlated deterministic movements) are generated and combined for this analysis. With this data, a numerical observability analysis is performed for different combinations of GNSS-based and CAI-based measurements. Furthermore, differences in estimability and observability between vehicle types (cars, aircrafts, trains or ships) are shown.The results demonstrate that, as in a conventional GNSS-IMU sensor fusion, dynamics improve the observability of e.g. scale factors, lever arm components, or misalignment terms. The inclusion of misalignments in the ESEKF, orientation difference between the CAI and IMU, and the introduction of larger lever arms between the CAI and IMU leads to increased dependencies between different bias terms of the IMU, but also between components of the lever arm and misalignments at the CAI-IMU level. They are accentuated when larger vehicle-dependent oscillations are introduced in the system, which is demonstrated by an analysis of singular vectors of the Fisher Information Matrix (FIM).The article provides relevant information about tradeoffs between CAI-IMU model complexity and occurring dynamics, and it gives insights which components of the system need to be pre-calibrated, as their on the fly estimation may lead to an insufficiently resolved state, due to increased dependencies.
AB - An observability analysis of a Quantum Inertial Navigation System (QINS) is presented for multiple realistic dynamic scenarios. It is performed on an Error State Extended Kalman Filter (ESEKF), which contains loosely coupled position and velocity measurements and 3-axis differential Cold Atom Interferometer (CAI) sensor measurements. The CAI-based measurements are hybridized with conventional IMU measurements, which results, in combination with position and velocity estimates, in a filter structure that contains position, velocity, acceleration and angular-rate based observations of the system at the same time. This in turn results in increased estimability and observability of the system, as well as lower position, velocity and attitude drift. As CAI-based measurements are only available for low measurement frequencies (i.e. 1-10 Hz), and are also only valid for low dynamics, the improvement in estimability has to be evaluated in realistic scenarios.To this end, realistic trajectories (low frequency deterministic movement) and realistic vibrations (high frequency correlated deterministic movements) are generated and combined for this analysis. With this data, a numerical observability analysis is performed for different combinations of GNSS-based and CAI-based measurements. Furthermore, differences in estimability and observability between vehicle types (cars, aircrafts, trains or ships) are shown.The results demonstrate that, as in a conventional GNSS-IMU sensor fusion, dynamics improve the observability of e.g. scale factors, lever arm components, or misalignment terms. The inclusion of misalignments in the ESEKF, orientation difference between the CAI and IMU, and the introduction of larger lever arms between the CAI and IMU leads to increased dependencies between different bias terms of the IMU, but also between components of the lever arm and misalignments at the CAI-IMU level. They are accentuated when larger vehicle-dependent oscillations are introduced in the system, which is demonstrated by an analysis of singular vectors of the Fisher Information Matrix (FIM).The article provides relevant information about tradeoffs between CAI-IMU model complexity and occurring dynamics, and it gives insights which components of the system need to be pre-calibrated, as their on the fly estimation may lead to an insufficiently resolved state, due to increased dependencies.
KW - ESEKF
KW - Kalman Filtering
KW - Numerical analysis
KW - Observability
KW - Quantum inertial navigation
KW - QUINS
KW - Strapdown Navigation
UR - http://www.scopus.com/inward/record.url?scp=105009232222&partnerID=8YFLogxK
U2 - 10.1109/PLANS61210.2025.11028432
DO - 10.1109/PLANS61210.2025.11028432
M3 - Conference contribution
AN - SCOPUS:105009232222
SN - 979-8-3315-2318-3
SP - 68
EP - 79
BT - 2025 IEEE/ION Position, Location and Navigation Symposium, PLANS 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE/ION Position, Location and Navigation Symposium
Y2 - 28 April 2025 through 1 May 2025
ER -