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

Yıl 2026, Cilt: 38 Sayı: 1, 52 - 70, 20.03.2026
https://doi.org/10.7240/jeps.1736397
https://izlik.org/JA23DB36BP

Öz

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.

Kaynakça

  • 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
  • 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).
  • 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
  • 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
  • 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.
  • 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
  • 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.
  • Nobili, G., Nannini, R., & Pianigiani, S. (2017). MEMS accelerometer-based tilt measurement: Comparison of different filtering techniques. Measurement, 103, 265–272.
  • Pazar, A., & Aydın, K. (2023). Low-cost gyroscopic stabilization using MEMS sensors in mobile robotic applications. Journal of Mechatronic Systems, 9(2), 112–121.
  • 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.
  • Quan, W., & Zhang, Z. (2020). CANopen protocol implementation in modular robotics. Journal of Industrial Electronics and Applications, 5(1), 23–30.
  • 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
  • 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
  • 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
  • Valzasina, F., & Rizzi, C. (2022). Integration of miniaturized IMU systems in wearable devices for industrial applications. Procedia Computer Science, 200, 1129–1136.
  • 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.
  • 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.
  • 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.
  • 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.
  • Zhou, H., Hu, H., & Tao, Y. (2008). Integration of MEMS sensors with wireless sensor networks. IEEE Sensors Journal, 8(10), 1640–1648.

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

Yıl 2026, Cilt: 38 Sayı: 1, 52 - 70, 20.03.2026
https://doi.org/10.7240/jeps.1736397
https://izlik.org/JA23DB36BP

Öz

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.

Etik Beyan

No approval from the Board of Ethics is required.

Destekleyen Kurum

Elfatek A.Ş. and Graduate Education Institute of Konya Technical University

Teşekkür

Authors are thankful to Elfatek A.Ş. for their support as they provide testing and developing tools. Authors are also thankful to Graduate Education Institute of Konya Technical University.

Kaynakça

  • 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
  • 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).
  • 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
  • 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
  • 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.
  • 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
  • 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.
  • Nobili, G., Nannini, R., & Pianigiani, S. (2017). MEMS accelerometer-based tilt measurement: Comparison of different filtering techniques. Measurement, 103, 265–272.
  • Pazar, A., & Aydın, K. (2023). Low-cost gyroscopic stabilization using MEMS sensors in mobile robotic applications. Journal of Mechatronic Systems, 9(2), 112–121.
  • 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.
  • Quan, W., & Zhang, Z. (2020). CANopen protocol implementation in modular robotics. Journal of Industrial Electronics and Applications, 5(1), 23–30.
  • 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
  • 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
  • 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
  • Valzasina, F., & Rizzi, C. (2022). Integration of miniaturized IMU systems in wearable devices for industrial applications. Procedia Computer Science, 200, 1129–1136.
  • 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.
  • 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.
  • 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.
  • 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.
  • Zhou, H., Hu, H., & Tao, Y. (2008). Integration of MEMS sensors with wireless sensor networks. IEEE Sensors Journal, 8(10), 1640–1648.
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektronik, Sensörler ve Dijital Donanım (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

İsmail Dereli 0000-0003-2481-1521

Kemal Erdoğan 0000-0001-7433-2516

Gönderilme Tarihi 7 Temmuz 2025
Kabul Tarihi 28 Ocak 2026
Yayımlanma Tarihi 20 Mart 2026
DOI https://doi.org/10.7240/jeps.1736397
IZ https://izlik.org/JA23DB36BP
Yayımlandığı Sayı Yıl 2026 Cilt: 38 Sayı: 1

Kaynak Göster

APA Dereli, İ., & Erdoğan, K. (2026). Development and Performance Analysis of a Hybrid Filtering Method for IMU-Based Dynamic Tilt Sensor. International Journal of Advances in Engineering and Pure Sciences, 38(1), 52-70. https://doi.org/10.7240/jeps.1736397
AMA 1.Dereli İ, Erdoğan K. Development and Performance Analysis of a Hybrid Filtering Method for IMU-Based Dynamic Tilt Sensor. JEPS. 2026;38(1):52-70. doi:10.7240/jeps.1736397
Chicago Dereli, İsmail, ve Kemal Erdoğan. 2026. “Development and Performance Analysis of a Hybrid Filtering Method for IMU-Based Dynamic Tilt Sensor”. International Journal of Advances in Engineering and Pure Sciences 38 (1): 52-70. https://doi.org/10.7240/jeps.1736397.
EndNote Dereli İ, Erdoğan K (01 Mart 2026) Development and Performance Analysis of a Hybrid Filtering Method for IMU-Based Dynamic Tilt Sensor. International Journal of Advances in Engineering and Pure Sciences 38 1 52–70.
IEEE [1]İ. Dereli ve K. Erdoğan, “Development and Performance Analysis of a Hybrid Filtering Method for IMU-Based Dynamic Tilt Sensor”, JEPS, c. 38, sy 1, ss. 52–70, Mar. 2026, doi: 10.7240/jeps.1736397.
ISNAD Dereli, İsmail - Erdoğan, Kemal. “Development and Performance Analysis of a Hybrid Filtering Method for IMU-Based Dynamic Tilt Sensor”. International Journal of Advances in Engineering and Pure Sciences 38/1 (01 Mart 2026): 52-70. https://doi.org/10.7240/jeps.1736397.
JAMA 1.Dereli İ, Erdoğan K. Development and Performance Analysis of a Hybrid Filtering Method for IMU-Based Dynamic Tilt Sensor. JEPS. 2026;38:52–70.
MLA Dereli, İsmail, ve Kemal Erdoğan. “Development and Performance Analysis of a Hybrid Filtering Method for IMU-Based Dynamic Tilt Sensor”. International Journal of Advances in Engineering and Pure Sciences, c. 38, sy 1, Mart 2026, ss. 52-70, doi:10.7240/jeps.1736397.
Vancouver 1.İsmail Dereli, Kemal Erdoğan. Development and Performance Analysis of a Hybrid Filtering Method for IMU-Based Dynamic Tilt Sensor. JEPS. 01 Mart 2026;38(1):52-70. doi:10.7240/jeps.1736397