Calibration and Signal Processing of MPU6050 Sensor to Improve Navigation Stability of Autonomous Underwater Vehicle (AUV)

Authors

  • Permono Adi Putro Program Studi Fisika, Fakultas Sains, Universitas Mandiri, Subang, Jawa Barat, Indonesia
  • Kuntjoro Pinard PT PAL Indonesia, Surabaya, Jawa Timur, Indonesia
  • Aditia Ramadhan Program Studi Fisika, Fakultas Sains, Universitas Mandiri, Subang, Jawa Barat, Indonesia
  • Aulia Sultan Rafael Zein Program Studi Fisika, Fakultas Sains, Universitas Mandiri, Subang, Jawa Barat, Indonesia
  • Yokeu Nurfalah Program Studi Fisika, Fakultas Sains, Universitas Mandiri, Subang, Jawa Barat, Indonesia

Keywords:

MPU6050, sensor calibration, AUV navigation, fast Fourier transform, digital ilter

Abstract

A stable and accurate navigation system is crucial for the operation of Autonomous Underwater Vehicles (AUVs), especially in underwater environments where GPS signals are unavailable. This study aims to enhance AUV navigation performance by optimizing the use of a low-cost inertial sensor, the MPU6050, through calibration and signal processing techniques. The methodology includes reading raw data from accelerometer and gyroscope sensors, performing static calibration to reduce bias and noise, applying Fast Fourier Transform (FFT) for signal analysis, and implementing digital filters such as low-pass and high-pass filters. The raw data reveal significant deviations due to gyroscope bias and accelerometer noise. Static calibration effectively reduces systematic errors, although residual biases remain. FFT analysis identifies dominant frequency components in each axis, while digital filtering helps eliminate irrelevant frequency noise. Precision testing demonstrates that the sensor produces relatively stable data post-calibration, with the Z-axis showing higher deviation compared to the X and Y axes. The combination of calibration and signal processing methods significantly improves the data quality and reliability of the MPU6050 sensor. This research contributes to the development of more efficient and low-cost AUV navigation systems and supports the broader application of simple IMU sensors in underwater exploration and maritime technologies.

References

De Agostino, M. (2008). A multi-frequency filtering procedure for inertial navigation. 2008 IEEE/ION Position, Location and Navigation Symposium, 1115–1121. https://doi.org/10.1109/PLANS.2008.4569998

Fan, N., Balan, R. V., & Rosca, J. (2004). Comparison of wavelet- and FFT-based single-channel speech signal noise reduction techniques. In F. Truchetet & O. Laligant (Eds.), Proc. SPIE 5607, Wavelet Applications in Industrial Processing II (pp. 127–138). https://doi.org/10.1117/12.574050

Han, S., Meng, Z., Omisore, O., Akinyemi, T., & Yan, Y. (2020). Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement—A Review. Micromachines, 11(11), 1021. https://doi.org/10.3390/mi11111021

Liu, J., Yu, T., Wu, C., Zhou, C., Lu, D., & Zeng, Q. (2024). A Low-Cost and High-Precision Underwater Integrated Navigation System. Journal of Marine Science and Engineering, 12(2), 200. https://doi.org/10.3390/jmse12020200

Ma’arif, A., Iswanto, I., Nuryono, A. A., & Alfian, R. I. (2019). Kalman Filter for Noise Reducer on Sensor Readings. Signal and Image Processing Letters, 1(2), 11–22. https://doi.org/10.31763/simple.v1i2.2

Magsi, H., Sodhro, A. H., Chachar, F. A., & Abro, S. A. K. (2018). Analysis of signal noise reduction by using filters. 2018 International Conference on Computing, Mathematics and Engineering Technologies (ICoMET), 1–6. https://doi.org/10.1109/ICOMET.2018.8346412

Pangestu, R. D., Erfianto, B. B., & Pahlevi, R. R. (2021). Analisis kenyamanan berkendara kereta api menggunakan sensor IMU pada smartphone. EProceedings of Engineering, 9898–9922.

Prasetyo, R. T., Wahyudi, W., & Setiyono, B. (2014). Karakteristik dan kalibrasi Untuk sensor inertial measurement unit. Transient: Jurnal Ilmiah Teknik Elektro, 3(4), 479–486. https://doi.org/10.14710/transient.v3i4.479-486

Shiau, J.-K., Huang, C.-X., & Chang, M.-Y. (2012). Noise characteristics of MEMS gyro’s null drift and temperature compensation. Journal of Applied Science and Engineering, 15(3), 239–246. https://doi.org/10.6180/jase.2012.15.3.04

Tjhai, C. (2019). Integration of multiple low-cost wearable inertial/magnetic sensors and kinematics of lower limbs for improving pedestrian navigation systems. University of Calgary.

Wahyudi, D. (2022). Pengukuran gelombang air laut menggunakan accelerometer pada PSoC untuk deteksi parameter tsunami. Universitas Sultan Ageng Tirtayasa.

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Published

2025-03-10

How to Cite

Putro, P. A., Kuntjoro Pinard, Aditia Ramadhan, Aulia Sultan Rafael Zein, & Yokeu Nurfalah. (2025). Calibration and Signal Processing of MPU6050 Sensor to Improve Navigation Stability of Autonomous Underwater Vehicle (AUV). TIME in Physics, 3(1), 31–44. Retrieved from https://ejournal.universitasmandiri.ac.id/index.php/timeinphys/article/view/225

Issue

Vol. 3 No. 1 (2025): March

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Articles

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Copyright (c) 2025 Permono Adi Putro, Kuntjoro Pinard, Aditia Ramadhan, Aulia Sultan Rafael Zein, Yokeu Nurfalah

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This work is licensed under a Creative Commons Attribution 4.0 International License.