The Inertial Measurement Unit (IMU) [1] is a cornerstone technology in various fields, ranging from aerospace to consumer electronics, where accurate motion tracking is paramount. Central to the effectiveness of an IMU is the quality of data processing, particularly in the context of filtering techniques. This study compares two filtering methods: Complementary Filters and Kalman Filters, in their application to IMU data processing. Complementary Filters, known for their simplicity and efficiency, contrast with the more complex but potentially more accurate Kalman Filters. Our investigation delves into the underpinnings of each filter, followed by a practical analysis of their performance in real-world IMU applications. We comprehensively compare these filters in terms of accuracy, computational efficiency, and ease of implementation. This research offers valuable insights for practitioners and researchers in selecting the most suitable filtering approach for specific IMU-based applications, enhancing the overall quality of motion sensing and analysis.
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