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

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.