Towards a novel approach for generating realistic IMU sensor measurements, based on real recorded body frame sensor data

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Authors

  • N. B. Weddig
  • S. Schon

Research Organisations

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Details

Original languageEnglish
Title of host publication2023 DGON Inertial Sensors and Systems, ISS 2023
Subtitle of host publicationProceedings
EditorsPeter Hecker
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages20
ISBN (electronic)9798350347241
Publication statusPublished - 2023
Event2023 DGON Inertial Sensors and Systems, ISS 2023 - Braunschweig, Germany
Duration: 24 Oct 202325 Oct 2023

Publication series

Name2023 DGON Inertial Sensors and Systems, ISS 2023 - Proceedings

Abstract

This article shows a novel approach to generate body frame IMU data with realistic noise characteristics, based on real measured sensor data. Realistic noise characteristics as defined here are based not only on the sensor noise itself, but also on the high frequency deterministic components of the vehicle system, which are often ignored in ground-based navigation simulations. Realistic noise processes of the system are derived, based on a spectral analysis of segmented maneuvers of real IMU data, gathered from previous experiments. The measurements were recorded over the last decade at the lnstitut für Erdmessung (1FE) for a variety of experiments and projects. Here, the results from a single experiment are highlighted including spectral analysis for both amplitude and phase data. A potential noise generator is outlined based on these results. As the noise processes are non-normal and nonstationary, a histogram-based distribution of the spectrum amplitudes is used as a basis for the simulation. Phase dependencies between frequency bins of different axes can also be simulated via Markov chain Monte Carlo (MCMC) methods, by fitting a multivariate von-Mises distribution per frequency bin between sensor axes. In this contribution, the analysis process as a whole is described on the basis of one exemplary car trajectory, to give some guidelines as to how simulated measurements for such an IMU generator can be obtained with a given dataset. The results of the analysis are presented and discussed. It is shown that phase dependencies between accelerations and angular rates exist for multiple frequencies, even after gravity-related oscillations of the accelerations are corrected by a computed attitude solution. Furthermore, an explanation for the origin of these sinusoidal correlations between accelerations and angular rates is given. Finally, an outlook is presented, to separate still remaining systematic effects of the car system from the obtained simulation parameters.

ASJC Scopus subject areas

Cite this

Towards a novel approach for generating realistic IMU sensor measurements, based on real recorded body frame sensor data. / Weddig, N. B.; Schon, S.
2023 DGON Inertial Sensors and Systems, ISS 2023 : Proceedings. ed. / Peter Hecker. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 DGON Inertial Sensors and Systems, ISS 2023 - Proceedings).

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Weddig, NB & Schon, S 2023, Towards a novel approach for generating realistic IMU sensor measurements, based on real recorded body frame sensor data. in P Hecker (ed.), 2023 DGON Inertial Sensors and Systems, ISS 2023 : Proceedings. 2023 DGON Inertial Sensors and Systems, ISS 2023 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2023 DGON Inertial Sensors and Systems, ISS 2023, Braunschweig, Germany, 24 Oct 2023. https://doi.org/10.1109/ISS58390.2023.10361937
Weddig, N. B., & Schon, S. (2023). Towards a novel approach for generating realistic IMU sensor measurements, based on real recorded body frame sensor data. In P. Hecker (Ed.), 2023 DGON Inertial Sensors and Systems, ISS 2023 : Proceedings (2023 DGON Inertial Sensors and Systems, ISS 2023 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISS58390.2023.10361937
Weddig NB, Schon S. Towards a novel approach for generating realistic IMU sensor measurements, based on real recorded body frame sensor data. In Hecker P, editor, 2023 DGON Inertial Sensors and Systems, ISS 2023 : Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 DGON Inertial Sensors and Systems, ISS 2023 - Proceedings). doi: 10.1109/ISS58390.2023.10361937
Weddig, N. B. ; Schon, S. / Towards a novel approach for generating realistic IMU sensor measurements, based on real recorded body frame sensor data. 2023 DGON Inertial Sensors and Systems, ISS 2023 : Proceedings. editor / Peter Hecker. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 DGON Inertial Sensors and Systems, ISS 2023 - Proceedings).
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abstract = "This article shows a novel approach to generate body frame IMU data with realistic noise characteristics, based on real measured sensor data. Realistic noise characteristics as defined here are based not only on the sensor noise itself, but also on the high frequency deterministic components of the vehicle system, which are often ignored in ground-based navigation simulations. Realistic noise processes of the system are derived, based on a spectral analysis of segmented maneuvers of real IMU data, gathered from previous experiments. The measurements were recorded over the last decade at the lnstitut f{\"u}r Erdmessung (1FE) for a variety of experiments and projects. Here, the results from a single experiment are highlighted including spectral analysis for both amplitude and phase data. A potential noise generator is outlined based on these results. As the noise processes are non-normal and nonstationary, a histogram-based distribution of the spectrum amplitudes is used as a basis for the simulation. Phase dependencies between frequency bins of different axes can also be simulated via Markov chain Monte Carlo (MCMC) methods, by fitting a multivariate von-Mises distribution per frequency bin between sensor axes. In this contribution, the analysis process as a whole is described on the basis of one exemplary car trajectory, to give some guidelines as to how simulated measurements for such an IMU generator can be obtained with a given dataset. The results of the analysis are presented and discussed. It is shown that phase dependencies between accelerations and angular rates exist for multiple frequencies, even after gravity-related oscillations of the accelerations are corrected by a computed attitude solution. Furthermore, an explanation for the origin of these sinusoidal correlations between accelerations and angular rates is given. Finally, an outlook is presented, to separate still remaining systematic effects of the car system from the obtained simulation parameters.",
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AU - Weddig, N. B.

AU - Schon, S.

N1 - Funding Information: This work is financed based on a resolution by the German Bundestag, Project-ID 50NA2106 (QGyroPlus), and supported by “Niedersächsisches Vorab”, initial funding by the DLR-SI institute.

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N2 - This article shows a novel approach to generate body frame IMU data with realistic noise characteristics, based on real measured sensor data. Realistic noise characteristics as defined here are based not only on the sensor noise itself, but also on the high frequency deterministic components of the vehicle system, which are often ignored in ground-based navigation simulations. Realistic noise processes of the system are derived, based on a spectral analysis of segmented maneuvers of real IMU data, gathered from previous experiments. The measurements were recorded over the last decade at the lnstitut für Erdmessung (1FE) for a variety of experiments and projects. Here, the results from a single experiment are highlighted including spectral analysis for both amplitude and phase data. A potential noise generator is outlined based on these results. As the noise processes are non-normal and nonstationary, a histogram-based distribution of the spectrum amplitudes is used as a basis for the simulation. Phase dependencies between frequency bins of different axes can also be simulated via Markov chain Monte Carlo (MCMC) methods, by fitting a multivariate von-Mises distribution per frequency bin between sensor axes. In this contribution, the analysis process as a whole is described on the basis of one exemplary car trajectory, to give some guidelines as to how simulated measurements for such an IMU generator can be obtained with a given dataset. The results of the analysis are presented and discussed. It is shown that phase dependencies between accelerations and angular rates exist for multiple frequencies, even after gravity-related oscillations of the accelerations are corrected by a computed attitude solution. Furthermore, an explanation for the origin of these sinusoidal correlations between accelerations and angular rates is given. Finally, an outlook is presented, to separate still remaining systematic effects of the car system from the obtained simulation parameters.

AB - This article shows a novel approach to generate body frame IMU data with realistic noise characteristics, based on real measured sensor data. Realistic noise characteristics as defined here are based not only on the sensor noise itself, but also on the high frequency deterministic components of the vehicle system, which are often ignored in ground-based navigation simulations. Realistic noise processes of the system are derived, based on a spectral analysis of segmented maneuvers of real IMU data, gathered from previous experiments. The measurements were recorded over the last decade at the lnstitut für Erdmessung (1FE) for a variety of experiments and projects. Here, the results from a single experiment are highlighted including spectral analysis for both amplitude and phase data. A potential noise generator is outlined based on these results. As the noise processes are non-normal and nonstationary, a histogram-based distribution of the spectrum amplitudes is used as a basis for the simulation. Phase dependencies between frequency bins of different axes can also be simulated via Markov chain Monte Carlo (MCMC) methods, by fitting a multivariate von-Mises distribution per frequency bin between sensor axes. In this contribution, the analysis process as a whole is described on the basis of one exemplary car trajectory, to give some guidelines as to how simulated measurements for such an IMU generator can be obtained with a given dataset. The results of the analysis are presented and discussed. It is shown that phase dependencies between accelerations and angular rates exist for multiple frequencies, even after gravity-related oscillations of the accelerations are corrected by a computed attitude solution. Furthermore, an explanation for the origin of these sinusoidal correlations between accelerations and angular rates is given. Finally, an outlook is presented, to separate still remaining systematic effects of the car system from the obtained simulation parameters.

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