A Reconstruction Filter for Saturated Accelerometer Signals Due to Insufficient FSR in Foot-Mounted Inertial Navigation System
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Abstract
In pedestrian inertial navigation, one possible placement of Inertial Measurement Units (IMUs) is on a footwear. This placement allows to limit the accumulation of navigation errors due to the bias drift of inertial sensors and is generally a preferable placement of sensors to achieve the highest precision of pedestrian inertial navigation. However, inertial sensors mounted on footwear experience significantly higher accelerations and angular velocities during regular pedestrian activities than during more conventional navigation tasks, which could exceed Full Scale Range (FSR) of many commercial-off-the-shelf IMUs, therefore degrading accuracy of pedestrian navigation systems. This paper proposes a reconstruction filter to mitigate localization error in pedestrian navigation due to insufficient FSR of inertial sensors. The proposed reconstruction filter approximates immeasurable accelerometer's signals with a triangular function and estimates the size of the triangles using a Gaussian Process regression. To evaluate performance of the proposed reconstruction filter, we conducted two series of indoor pedestrian navigation experiments with a VectorNav VN-200 IMU and an Analog Device ADIS16497-3 IMU. In the first series of experiments, forces experienced by the IMUs did not exceed the FSRs of the sensors, while in the second series, the forces surpassed the FSR of the VN-200 IMU and saturated the accelerometer's readings. The saturated readings reduced the accuracy of estimated positions using the VN-200 by 1.34× and 3.37× along horizontal and vertical directions. When applying our proposed reconstruction filter to the saturated measurements, the navigation accuracy was increased by 5% horizontally and 50% vertically, as compared to using unreconstructed signals.
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The design and functionality of ACC sensors may lead to sensor noise and other inconsistencies like sensor saturation or displacement.
“The design and functionality of ACC sensors may lead to sensor noise and other inconsistencies like sensor saturation or displacement63–65.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [63]
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Data quality evaluation in wearable monitoring
Böttcher, S., Vieluf, S., Bruno, E., Joseph, B., Epitashvili, N., Biondi, A., Zabler, N., Glasstetter, M., Dümpelmann, M., Van Laerhoven, K., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-25949-x · Cited by 91 · see citing works
- Authors
- Jao, C.-S., & Shkel, A. M.
- Affiliations
- University of California, Irvine (US)
- Venue
- IEEE Sensors Journal
- Published
- 2022-01-01
- Volume
- 22
- Issue
- 1
- Pages
- 695-706
- ISSN
- 1530-437X (ISSN-L)
- 1558-1748
- 2379-9153
- OpenAlex
- W3216701173
- Cited by
- 18
- References
- 28
- Citations by year
- 2025: 4 · 2024: 2 · 2023: 9 · 2022: 3
- FWCI
- 1.81
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- IEEE Sensors Journal (publishedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- U.S. Department of Commerce, National Institute of Standards and Technology (70NANB17H192)
- Federal Program Officer Joseph Grasso
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Keywords
- pedestrian inertial navigation, foot-mounted IMU, reconstruction filter, Gaussian process regression, indoor positioning, navigation accuracy, inertial measurement unit
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