<![CDATA[Land deformation monitoring generally relies on Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) technologies. While accurate, both methods have limitations: GNSS requires relatively expensive permanent infrastructure, while InSAR relies on atmospheric conditions and cannot function optimally in enclosed areas such as caves, tunnels, or areas with dense vegetation. In this context, the Inertial Measurement Unit (IMU) offers a more flexible, portable, and low-cost solution. IMUs can record acceleration data continuously without relying on satellite or radar signals, making them suitable for deformation monitoring in locations difficult to reach by GNSS or InSAR. However, IMU accuracy is often hampered by accumulated noise and multiple integration errors, which cause significant drift in displacement estimates. Previous research has focused on the Zero-Velocity Update (ZUPT) method to reduce errors, but the results still show significant deviations from the true value. The main contribution of this research is proposing the integration of the ZUPT method with a second-order Butterworth low-pass filter to reduce high-frequency noise that triggers error accumulation. Displacement testing results of 10–30 cm show significant accuracy improvements. Without the filter, the displacement estimation yielded an RMSE of 0.4292 m with a Relative Error of Prediction (REP) of –304.8%, indicating a significant deviation. After the application of the Butterworth filter, the accuracy improved dramatically with an RMSE of only 0.002 m and an REP of –0.40%, indicating that the proposed method is capable of reducing the error by more than 99%. Thus, this study not only proves the effectiveness of the integration of ZUPT and the Butterworth filter in improving the accuracy of the IMU, but also confirms the potential of the IMU as a reliable alternative for deformation monitoring in areas with limited access to GNSS or InSAR. These findings open up opportunities for further development towards a real-time, portable, and efficient IMU-based landslide monitoring system.]]>
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