Havacılık ve Otonom Araç Uygulamaları İçin Gömülü Sistemlerde Euler ve Dörtgen Tabanlı AHRS Modellerinin Performans Değerlendirmesi

Abstract

Bu çalışma, farklı Kalman Filtre modellerinin AHRS sistemi performansı üzerindeki etkisini incelemeyi ve mikroişlemci bağımsız hesaplama hızını değerlendirmeyi amaçlamaktadır; özellikle havacılık ve otonom araç uygulamalarındaki kritik rolüne vurgu yapmaktadır. AHRS, doğru duruş tahmini sağlayarak hava aracı stabilitesi, navigasyonu ve kontrolü için hayati öneme sahiptir. Araştırma, MPU-9255 sensörü ve ATmega 2560 mikroişlemcisini kullanarak, sensörden alınan verileri işleyerek farklı matematiksel modellerle Kalman Filtreleri uygulamıştır. Euler açıları ve dörtgenler (quaternions) tabanlı iki model test edilip, ölçüm doğruluğu ve yürütme hızı açısından karşılaştırılmıştır. Modeller arasındaki hesaplama süresi farkı 10 ms olarak bulunmuştur. Bu modellerin gömülü bir sistemdeki performansı değerlendirilerek, çalışma, havacılık ve diğer gerçek zamanlı yönelim izleme sistemlerinde AHRS uygulamalarını optimize etmek için bir yenilikçi çerçeve sunmaktadır.

Keywords

• Kalmman Filtersi
• Gömülü Sistemler
• Hesaplama verimliliği
• Sensör Füzyonu;
• IMU
• AHRS

Evaluating the Performance of Euler and Quaternion-Based AHRS Models in Embedded Systems for Aviation and Autonomous Vehicle Applications

Abstract

This study investigates the impact of different Kalman Filter models on the performance of the AHRS system and evaluates its microprocessor-independent computation speed, with particular emphasis on its critical role in aviation and autonomous vehicle applications. AHRS is vital for aircraft stability, navigation, and control by providing accurate attitude estimation. The research employed an MPU-9255 sensor and an ATmega2560 microprocessor, processing data from the sensor to implement Kalman Filters using different mathematical models. Two models, based on Euler angles and quaternions, were tested and compared in terms of measurement accuracy and execution speed. The computation time difference between the models was found to be 10 millisecond (ms). By assessing the performance of these models within an embedded system, the study introduces a novel framework that serves as a reference for optimizing AHRS applications in aviation and other real-time orientation tracking systems.

Keywords

• Kalman Filter
• Embedded Systems
• Computational Efficiency
• Sensor Fusion
• IMU
• AHRS

References

1. Couturier, A., & Akhloufi, M. A. (2021). A review on absolute visual localization for UAV. Robotics and Autonomous Systems, 135, 103666.
2. Diaz, E. M., Müller, F. d. P., Jiménez, A. R., & Zampella, F. (2015). Evaluation of AHRS algorithms for inertial personal localization in industrial environments. Paper presented at the 2015 IEEE International Conference on Industrial Technology (ICIT).
3. Emimi, M., Khaleel, M., & Alkrash, A. (2023). The Current Opportunities and Challenges in Drone Technology. Int. J. Electr. Eng. and Sustain., 1(3), 74-89.
4. Fan, W., Zhu, W. D., & Ren, H. (2016). A New Singularity-Free Formulation of a Three-Dimensional Euler–Bernoulli Beam Using Euler Parameters. Journal of Computational and Nonlinear Dynamics, 11(4).
5. Ferdinando, H., Khoswanto, H., & Purwanto, D. (2012, 2-4 July 2012). Embedded Kalman Filter for Inertial Measurement Unit (IMU) on the ATMega8535. Paper presented at the 2012 International Symposium on Innovations in Intelligent Systems and Applications.
6. Gelsinger, P. P. (2001). Microprocessors for the new millennium: Challenges, opportunities, and new frontiers. Paper presented at the 2001 IEEE International Solid-State Circuits Conference. Digest of Technical Papers. ISSCC (Cat. No.01CH37177).
7. Hanafi, M. E., Abozied, M. A., Elhalwagy, Y. Z., & Elfarouk, A. O. (2019). Multi-sensory data fusion for high performance attitude estimation. Paper presented at the International Conference on Aerospace Sciences and Aviation Technology.
8. Hasan, M., Abdullah, A., Ahmed, A., Hamzah, N., Said, M., Yaacob, S., Engineering, C. (2018). Analysis on Euler angles rotation of a rigid body in three-axis attitude based on RazakSAT data. 10(1-14), 73-76.

9. Immonen, R., & Hämäläinen, T. (2022). Tiny Machine Learning for Resource-Constrained Microcontrollers. Journal of Sensors, 2022, 7437023.
10. Kalman, R. E. (1960). A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82(1), 35-45. doi:10.1115/1.3662552 %J Journal of Basic Engineering
11. Kang, C. W., & Park, C. G. (2011). Euler Angle Based Attitude Estimation Avoiding the Singularity Problem. IFAC Proceedings Volumes, 44(1), 2096-2102.
12. Kim, A., & Golnaraghi, M. (2004). A Quaternion-Based Orientation Estimation Algorithm Using an Inertial
13. Measurement Unit.
14. Kim, P., & Huh, L. J. (2011). Kalman filter for beginners: with MATLAB examples.
15. Konar, M., Hatipoğlu, S. A., & Akpınar, M. (2024). Improvement of UAV thrust using the BSO algorithm-based ANFIS model. The Aeronautical Journal, 1-10.
16. Lam, Q., Stamatakos, N., Woodruff, C., & Ashton, S. (2003). Gyro Modeling and Estimation of Its Random Noise Sources (Vol. 5562).
17. Lei, B., Liu, B., & Wang, C. (2024). Robust Geometric Control for a Quadrotor UAV with Extended Kalman Filter Estimation. 13(6), 205.
18. Menghal, P. M., & Laxmi, A. J. (2010). Real time control of electrical machine drives: A review. Paper presented at the 2010 International Conference on Power, Control and Embedded Systems.
19. Munguia, R., & Grau, A. (2011). Attitude and Heading System based on EKF total state configuration. Paper presented at the 2011 IEEE International Symposium on Industrial Electronics.
20. Nagui, N., Attallah, O., Zaghloul, M. S., & Morsi, I. (2020, 28-31 Oct. 2020). Smart Real-Time Autonomous Navigation System using integration of MEMS-based Low-Cost IMU/GPS. Paper presented at the 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON).
21. Parai, M. K., Das, B., Das, G. J. I. J. o. S. C., & Engineering. (2013). An overview of microcontroller unit: from proper selection to specific application. 2(6), 228-231.
22. Poulose, A., Kim, J., & Han, D. S. (2019). Indoor Localization with Smartphones: Magnetometer Calibration. Paper
23. presented at the 2019 IEEE International Conference on Consumer Electronics (ICCE).
24. Pourtakdoust, S., Ghanbarpour Asl, H. J. A. E., & Technology, A. (2007). An adaptive unscented Kalman filter for quaternion‐based orientation estimation in low‐cost AHRS. 79(5), 485-493.
25. Samiullah, M., Irfan, M. Z., & Rafique, A. Microcontrollers: A Comprehensive Overview and Comparative Analysis of Diverse Types.
26. Saraf, A., Moon, S., & Madotto, A. (2023, 4-10 June 2023). A Survey of Datasets, Applications, and Models for IMU Sensor Signals. Paper presented at the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW).
27. Shuster, M. D. J. N. (1993). A survey of attitude representations. 8(9), 439-517.
28. Tomaszewski, D., Rapiński, J., Pelc-Mieczkowska, R. J. R. o. G., & Geoinformatics. (2017). Concept of AHRS algorithm designed for platform independent IMU attitude alignment. 104(1), 33-47.
29. Vigrahala, J., Ramesh, N. V. K., Devanaboyina, V. R., & Reddy, B. N. K. (2021, 18-19 June 2021). Attitude, Position and Velocity determination using Low-cost Inertial Measurement Unit for Global Navigation Satellite System Outages. Paper presented at the 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT).
30. Wang, L., Zhang, Z., & Sun, P. (2015). Quaternion-Based Kalman Filter for AHRS Using an Adaptive-Step Gradient Descent Algorithm. 12(9), 131.
31. Welch, G. F. J. C. V. A. R. G. (2020). Kalman filter. 1-3.
32. Wen-shu, N., Liao-ni, W., & Qi, L. (2010). AHRS base on MEMS-IMU for aircraft model in wire-driven parallel suspension system. Paper presented at the 2010 International Conference on Mechanic Automation and Control Engineering.
33. Zhi, Y., Li, G., Song, Q., Yu, K., & Zhang, J. (2017). Flight control law of unmanned aerial vehicles based on robust servo linear quadratic regulator and Kalman filtering. 14(1), 1729881416686952.