IMU Tabanlı Dinamik Eğim Sensörü için Hibrit Filtreleme Yönteminin Geliştirilmesi ve Performans Analizi

Abstract

MEMS (Mikro-Elektromekanik Sistemler) teknolojisindeki gelişmeler, çeşitli uygulamalarda IMU (Atalet Ölçüm Birimi) tabanlı eğim sensörlerinin doğruluğunu ve entegrasyon potansiyelini önemli ölçüde artırmıştır. Ancak dinamik ortamlarda, dış ivmeler ve titreşimler sensör performansını olumsuz etkilemektedir ve bu da gürültüye yol açmaktadır. Bu çalışma, 6 eksenli bir IMU'yu entegre eden ve açı ölçüm hatalarını en aza indirmeyi amaçlayan Kalman Filtresi, Hareketli Ortalama Filtresi ve Alçak Geçiren Filtre olmak üzere birden fazla filtreleme stratejisi uygulayan dinamik bir eğim sensörü cihazı önermektedir. Dinamik koşullar altında filtre performansını değerlendirmek için özel bir test platformu geliştirilmiştir. RMSE (Kök Ortalama Karekök Hatası) ölçümleri kullanılarak değerlendirilen sonuçlar, üç filtreyi birleştiren özel olarak tasarlanmış bir hibrit filtreleme yönteminin statik koşullarda 0,017° ve dinamik koşullarda 1,178° ortalama hata elde ettiğini göstermektedir. Bulgular, önerilen hibrit yaklaşımın literatürdeki mevcut çözümlere kıyasla güvenilir bir alternatif sunduğunu, endüstriyel uygulamalarda ölçüm kararlılığını ve doğruluğunu artırdığını göstermektedir.

Keywords

• Dijital Filtre
• Dinamik Eğim Sensörü
• Eğimölçer
• Eğim Sensörü
• IMU
• MEMS

Development and Performance Analysis of a Hybrid Filtering Method for IMU-Based Dynamic Tilt Sensor

Abstract

Advancements in MEMS (micro-electromechanical systems) technology have significantly improved the accuracy and integration potential of IMU (inertial measurement unit) based tilt sensors across various applications. However, in dynamic environments, external accelerations and vibrations adversely affect sensor performance and this leads to noise. This study proposes a dynamic tilt sensor device integrating a 6-axis IMU and implements multiple filtering strategies, that are Kalman Filter, Moving Average Filter, and Low-Pass Filter aimed at minimizing angle measurement errors. A custom test platform was developed to evaluate filter performance under dynamic conditions. The results, evaluated using RMSE (root mean square error) metrics, show that a specially designed hybrid filtering method combining the three filters achieves an average error of 0.017° in static and 1.178° in dynamic conditions. The findings demonstrate that the proposed hybrid approach offers a reliable alternative comparable to existing solutions in literature, enhancing measurement stability and accuracy in industrial applications.

Keywords

• Digital Filter
• Dynamic Tilt Sensor
• IMU
• Inclinometer
• MEMS
• Tilt Sensor

References

1. Barshan, B., & Durrant-Whyte, H. F. (1995). Inertial navigation systems for mobile robots. IEEE Transactions on Robotics and Automation, 11(3), 328–342. https://doi.org/10.1109/70.388821
2. Beauregard, S. (2007). Wearable navigation system featuring an integrated inertial navigation system and GPS receiver. In Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks (pp. 87–90).
3. Caruso, M., Sabatini, A. M., Laidig, D., Seel, T., Knaflitz, M., Della Croce, U., & Cereatti, A. (2021). Analysis of the Accuracy of Ten Algorithms for Orientation Estimation Using Inertial and Magnetic Sensing under Optimal Conditions: One Size Does Not Fit All. Sensors, 21(7), 2543. https://doi.org/10.3390/s21072543
4. Hoang, Q., Liu, X., & Chen, H. (2021). An adaptive Kalman-based fusion filter for low-cost IMUs in highly dynamic environments. Sensors, 21(15), 5034. https://doi.org/10.3390/s21155034
5. Hong, S., & Lee, H. (2003). The development of an attitude and heading reference system using low-cost MEMS sensors. Proceedings of the IEEE Aerospace Conference, 3, 3_1163–3_1170.
6. Ligorio, G., & Sabatini, A. M. (2015). A novel Kalman filter for human motion tracking with an inertial-based dynamic inclinometer. IEEE Transactions on Biomedical Engineering, 62(8), 2033–2043. https://doi.org/10.1109/TBME.2015.2400015
7. Madgwick, S. O. H. (2011). An efficient orientation filter for inertial and inertial/magnetic sensor arrays. Report x-io and University of Bristol (UK), 25, 113–118.
8. Nobili, G., Nannini, R., & Pianigiani, S. (2017). MEMS accelerometer-based tilt measurement: Comparison of different filtering techniques. Measurement, 103, 265–272.

9. Pazar, A., & Aydın, K. (2023). Low-cost gyroscopic stabilization using MEMS sensors in mobile robotic applications. Journal of Mechatronic Systems, 9(2), 112–121.
10. Qinglei, H., Hu, W., & Shen, Y. (2007). Adaptive Kalman filter for attitude estimation using low-cost MEMS sensors. International Conference on Mechatronics and Automation, 2007, 1410–1415.
11. Quan, W., & Zhang, Z. (2020). CANopen protocol implementation in modular robotics. Journal of Industrial Electronics and Applications, 5(1), 23–30.
12. Rasulov, S., Karadeniz, A. M., & Bakucz, P. (2025). Robust IMU Sensor Fusion via Schreiber’s Nonlinear Filtering Approach. Engineering Proceedings, 118(1), 26. https://doi.org/10.3390/ECSA-12-26586
13. Sonmezocak, O., & Kurt, E. (2022). A MEMS-based wearable diagnostic system for bruxism detection using artificial neural networks. Biomedical Signal Processing and Control, 71, 102853. https://doi.org/10.1016/j.bspc.2021.102853
14. Tao, W., Liu, T., Zheng, R., & Feng, H. (2012). Gait analysis using wearable sensors. Sensors, 12(2), 2255–2283. https://doi.org/10.3390/s120202255
15. Valzasina, F., & Rizzi, C. (2022). Integration of miniaturized IMU systems in wearable devices for industrial applications. Procedia Computer Science, 200, 1129–1136.
16. Xiaoping, C., & Song, Z. (2005). An application of Kalman filter and quaternion estimator in real-time motion tracking. IEEE Sensors Journal, 5(6), 1233–1241.
17. Yazdkhasti, S., & Ghoreishi, S. F. (2018). A robust sensor fusion technique for attitude estimation of autonomous ground vehicles. IEEE Transactions on Industrial Electronics, 65(9), 7288–7298.
18. Yun, X., & Bachmann, E. R. (2005). Design, implementation, and experimental results of a quaternion-based Kalman filter for human body motion tracking. IEEE Transactions on Robotics, 22(6), 1216–1227.
19. Yu, B. (2025). Dynamic multidimensional sensor data acquisition with adaptive Kalman filtering. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4/W14-2025, 369–375, https://doi.org/10.5194/isprs-archives-XLVIII-4-W14-2025-369-2025, 2025.
20. Zhou, H., Hu, H., & Tao, Y. (2008). Integration of MEMS sensors with wireless sensor networks. IEEE Sensors Journal, 8(10), 1640–1648.