This is a list of literature that is somehow mentioned in the code or the documentation. It is not a complete literature list of the master thesis.

Becker, A. (2023). Kalman Filter from de ground up (2nd Edition). Alex Becker.

Bell, B. M., & Cathey, F. W. (1993). The iterated Kalman filter update as a Gauss-Newton method. IEEE Transactions on Automatic Control, 38(2), 294–297. https://doi.org/10.1109/9.250476

Bell, B. M. (1994). The Iterated Kalman Smoother as a Gauss–Newton Method. SIAM Journal on Optimization, 4(3), 626–636. https://doi.org/10.1137/0804035

Diebel, J. (2006). Representing Attitude: Euler Angles, Unit Quaternions, and Rotation Vectors. Matrix, 58.

Goslinski, J., Nowicki, M., & Skrzypczynski, P. (2015). Performance Comparison of EKF-Based Algorithms for Orientation Estimation on Android Platform. IEEE Sensors Journal, 15(7), 3781–3792. https://doi.org/10.1109/JSEN.2015.2397397

Guang-Lin, H., Si-Qian, T., Qiang, S., & Pian, Z. (2012). Research on Calibration and Parameter Compensation of MEMS Inertial Sensors Based on Error Analysis. 2012 Fifth International Symposium on Computational Intelligence and Design, 1, 325–329. https://doi.org/10.1109/ISCID.2012.89

IEEE Standard Specification Format Guide and Test Procedure for Linear Single-Axis, Nongyroscopic Accelerometers. (2019). IEEE Std 1293-2018 (Revision of IEEE Std 1293-1998), 1–271. https://doi.org/10.1109/IEEESTD.2019.8653544

Julier, S., Uhlmann, J., & Durrant-Whyte, H. F. (2000). A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Transactions on Automatic Control, 45(3), 477–482. https://doi.org/10.1109/9.847726

Kok, M., Hol, J. D., & Schön, T. B. (2017). Using Inertial Sensors for Position and Orientation Estimation. Foundations and Trends® in Signal Processing, 11(1–2), 1–153. https://doi.org/10.1561/2000000094

Laidig, D., & Seel, T. (2023). VQF: Highly accurate IMU orientation estimation with bias estimation and magnetic disturbance rejection. Information Fusion, 91, 187–204. https://doi.org/10.1016/j.inffus.2022.10.014

Ma, W., Qiu, J., Liang, J., & Chen, B. (2019). Linear Kalman Filtering Algorithm With Noisy Control Input Variable. IEEE Transactions on Circuits and Systems II: Express Briefs, 66(7), 1282–1286. https://doi.org/10.1109/TCSII.2018.2878951

Rauch, H. E., Tung, F., & Striebel, C. T. (1965). Maximum likelihood estimates of linear dynamic systems. AIAA Journal, 3(8), 1445–1450. https://doi.org/10.2514/3.3166

Rutishauser, C. (2014). DSV von IMU-Signalen für die Gang-Analyse [Master’s Thesis]. Zurich University of Applied Sciences.

Sabatini, A. M. (2011). Kalman-filter-based orientation determination using inertial/magnetic sensors: Observability analysis and performance evaluation. Sensors (Basel, Switzerland), 11(10), 9182–9206. https://doi.org/10.3390/s111009182

Simon, D. (2006). Optimal state estimation. Wiley-Interscience.

Sola, J. (2017). Quaternion kinematics for the error-state Kalman filter (arXiv:1711.02508). arXiv. http://arxiv.org/abs/1711.02508

Taylor, B. (1717). Methodus incrementorum directa & inversa. Impensis Gulielmi Innys. https://books.google.ch/books?id=r-Gq9YyZYXYC

Wertz, J. R. (Ed.). (1978). Spacecraft Attitude Determination and Control (Vol. 73). Springer Netherlands. https://doi.org/10.1007/978-94-009-9907-7