Navigation through the Processing of Android Data with a High-Order Kalman Filter. Campos-Vega, C. J., Watts, T. M., Martin, S. M., Chen, H., & Bevly, D. M. In pages 2957–2973, September, 2021. ![Navigation through the Processing of Android Data with a High-Order Kalman Filter [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex This paper presents a fourth order Extended Kalman Filter (EKF) that processes Android smartphone data for positioning and navigation. The EKF utilizes GNSS, IMU, and magnetometer sensor outputs for correction to its states. Unlike traditional GPS/INS error state recursions, the EKF processes the IMU outputs through the measurement observation matrix. The highorder EKF includes Fault Detection and Exclusion (FDE) to remove erroneous measurements. The navigation algorithm was evaluated with Pixel 4 and Pixel 4XL training data provided by the Google Smartphone Decimeter Challenge. The experimental results indicate the smartphones’ GNSS chip receivers provide positions with accuracies of roughly 1.5 meters at a rate of 1 Hz. The high-order EKF provides positions with accuracies of roughly 3 meters at a rate of 100 Hz. Furthermore, the highorder EKF can accurately navigate under motion with the IMU and magnetometer sensors for 5 to 10 seconds without GNSS. In cases when the GNSS has position faults, the EKF successfully rejects the outliers with FDE and continues to navigate. Future work includes augmenting the EKF with an adaptive process noise tuning algorithm, including sensor bias states for the IMU and magnetometer sensors, and comparing the high-order EKF to a traditional GPS/INS error state filter.
@inproceedings{campos-vega_navigation_2021,
title = {Navigation through the {Processing} of {Android} {Data} with a {High}-{Order} {Kalman} {Filter}},
url = {http://www.ion.org/publications/abstract.cfm?jp=p&articleID=18042},
doi = {10.33012/2021.18042},
abstract = {This paper presents a fourth order Extended Kalman Filter (EKF) that processes Android smartphone data for positioning and navigation. The EKF utilizes GNSS, IMU, and magnetometer sensor outputs for correction to its states. Unlike traditional GPS/INS error state recursions, the EKF processes the IMU outputs through the measurement observation matrix. The highorder EKF includes Fault Detection and Exclusion (FDE) to remove erroneous measurements. The navigation algorithm was evaluated with Pixel 4 and Pixel 4XL training data provided by the Google Smartphone Decimeter Challenge. The experimental results indicate the smartphones’ GNSS chip receivers provide positions with accuracies of roughly 1.5 meters at a rate of 1 Hz. The high-order EKF provides positions with accuracies of roughly 3 meters at a rate of 100 Hz. Furthermore, the highorder EKF can accurately navigate under motion with the IMU and magnetometer sensors for 5 to 10 seconds without GNSS. In cases when the GNSS has position faults, the EKF successfully rejects the outliers with FDE and continues to navigate. Future work includes augmenting the EKF with an adaptive process noise tuning algorithm, including sensor bias states for the IMU and magnetometer sensors, and comparing the high-order EKF to a traditional GPS/INS error state filter.},
language = {en},
urldate = {2024-06-20},
author = {Campos-Vega, Christian J. and Watts, Tanner M. and Martin, Scott M. and Chen, Howard and Bevly, David M.},
month = sep,
year = {2021},
pages = {2957--2973},
}
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The navigation algorithm was evaluated with Pixel 4 and Pixel 4XL training data provided by the Google Smartphone Decimeter Challenge. The experimental results indicate the smartphones’ GNSS chip receivers provide positions with accuracies of roughly 1.5 meters at a rate of 1 Hz. The high-order EKF provides positions with accuracies of roughly 3 meters at a rate of 100 Hz. Furthermore, the highorder EKF can accurately navigate under motion with the IMU and magnetometer sensors for 5 to 10 seconds without GNSS. In cases when the GNSS has position faults, the EKF successfully rejects the outliers with FDE and continues to navigate. 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The EKF utilizes GNSS, IMU, and magnetometer sensor outputs for correction to its states. Unlike traditional GPS/INS error state recursions, the EKF processes the IMU outputs through the measurement observation matrix. The highorder EKF includes Fault Detection and Exclusion (FDE) to remove erroneous measurements. The navigation algorithm was evaluated with Pixel 4 and Pixel 4XL training data provided by the Google Smartphone Decimeter Challenge. The experimental results indicate the smartphones’ GNSS chip receivers provide positions with accuracies of roughly 1.5 meters at a rate of 1 Hz. The high-order EKF provides positions with accuracies of roughly 3 meters at a rate of 100 Hz. Furthermore, the highorder EKF can accurately navigate under motion with the IMU and magnetometer sensors for 5 to 10 seconds without GNSS. In cases when the GNSS has position faults, the EKF successfully rejects the outliers with FDE and continues to navigate. 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