Robust Attitude Estimation Using IMU-Only Measurements

Candan B., SÖKEN H. E.

IEEE Transactions on Instrumentation and Measurement, vol.70, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 70
  • Publication Date: 2021
  • Doi Number: 10.1109/tim.2021.3104042
  • Journal Name: IEEE Transactions on Instrumentation and Measurement
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Keywords: Attitude estimation, covariance tuning, inertial measurement unit (IMU)-only, robust Kalman filter (RKF), ADAPTIVE KALMAN-FILTER, ORIENTATION, FUSION
  • Middle East Technical University Affiliated: Yes


© 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.