Robust Attitude Estimation Using IMU-Only Measurements

Date

2021-01-01

Author

Candan, Batu
Söken, Halil Ersin

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

277
views

0
downloads

© 1963-2012 IEEE.This article proposes two novel covariance-tuning methods to form a robust Kalman filter (RKF) algorithm for attitude (i.e., roll and pitch) estimation using the measurements of only an inertial measurement unit (IMU). KF-based and complementary filtering (CF)-based approaches are the two common methods for solving the attitude estimation problem. Efficiency and optimality of the KF-based attitude filters are correlated with appropriate tuning of the covariance matrices. Manual tuning process is a difficult and time-consuming task. Specifically, the IMU-only attitude estimation filters are prone to the external accelerations unless their covariances are adapted to gain robustness. The proposed algorithms provide an adaptive method for tuning the measurement noise covariance such that they can accurately estimate the attitude in the two axes. The first method relies on a single tuning factor, whereas the second one tunes the covariance with different (multiple) factors for each measurement axis. The proposed methodologies are tested and compared with other existing filtering algorithms in the literature under different dynamical conditions and using real-world experimental datasets in order to validate their effectiveness. Results show that highly dynamic scenarios, especially the multiple tuning factor strategy, can increase the attitude estimation accuracy more than two-times compared to the competitive algorithms.

Subject Keywords

Attitude estimation, covariance tuning, inertial measurement unit (IMU)-only, robust Kalman filter (RKF)

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85113340028&origin=inward
https://hdl.handle.net/11511/91973

Journal

IEEE Transactions on Instrumentation and Measurement

DOI

https://doi.org/10.1109/tim.2021.3104042

Collections

Department of Aerospace Engineering, Article

Suggestions

OpenMETU
Core

Design of attitude estimation algorithms for inertial sensors only measurement scenarios Candan, Batu; Söken, Halil Ersin; Department of Aerospace Engineering (2022-3-24) This thesis proposes four novel robust Kalman filter algorithms for attitude estimation using only the measurements of an inertial measurement unit. Efficiency and optimality of the Kalman filter based attitude filters are correlated with appropriate tuning of the covariance matrices. Manual tuning process is a difficult and time-consuming task. Specifically, the inertial measurement unit-only attitude estimation filters are prone to the external accelerations unless their covariances are adapted to gain ro...
Robust Kalman filtering with single and multiple scale factors for small satellite attitude estimation Söken, Halil Ersin; Sakai, Shin-ichiro (Springer, 2015-11-01) In case of normal operational conditions for a satellite, a conventional Kalman Filter gives sufficiently good attitude estimation results. On the other hand, when there is a fault in the measurements then the Kalman filter fails about providing the required accuracy and may even collapse by time. In this paper, a Robust Kalman filtering method is proposed for the attitude estimation problem. By using the proposed method both the Extended Kalman Filter and Unscented Kalman Filter are modified ...
Exact kalman filtering of respiratory motion Çetinkaya, Mehmet; Erkmen, Aydan Müşerref (2018-10-01) In this paper we propose a novel Exact Kalman Filter for state estimation of quasi-periodic signals such as respiratory motion. Nonlinear functions of interest are approximations as truncated Fourier series. Instead of relying on approximations provided by Extended Kalman Filter or Unscented Kalman Filter, our filter performs exact calculation of the mean and covariances of interest. We then compare, through simulations, the performance of our filter to the two. Our results show that the theoretically deriv...
GPS-Based Real-Time Orbit Determination of Low Earth Orbit Satellites Using Robust Unscented Kalman Filter Karslıoğlu, Mahmut Onur; Erdogan, Eren; Pamuk, Onur (2017-11-01) In this research, a novel algorithm for real-time orbit determination (RTOD) is presented using the robust unscented Kalman filter (RUKF) with global positioning system (GPS) group and phase ionospheric correction (GRAPHIC) observables. To increase the reliability of the solution, a robust approach is included in the UKF to cope with the bad, invalid, or degraded measurements leading to the divergence or inaccurate output of the filter. Robustness is provided by making the filter less sensitive to faulty me...
Compressed Representation of High Dimensional Channels using Deep Generative Networks Doshi, Akash; Balevi, Eren; Andrews, Jeffrey G. (2020-05-01) © 2020 IEEE.This paper proposes a novel compressed representation for high dimensional channel matrices obtained by optimization of the input to a deep generative network. Channel estimation using generative networks constrains the reconstructed channel to lie in the range of the generative model, which allows it to outperform conventional channel estimation techniques in the presence of limited number of pilots. It also eliminates the need for explicit knowledge of the sparsifying basis for mmWave multiple...

Citation Formats

B. Candan and H. E. Söken, “Robust Attitude Estimation Using IMU-Only Measurements,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 0–0, 2021, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85113340028&origin=inward.