Comparative Analysis of Sensor Fusion for Angle Estimation Using Kalman and Complementary Filters
(1) Phichitphon Chotikunnan Mail (Rangsit University, Thailand)
(2) Wanida Khotakham Mail (Rangsit University, Thailand)
(3) Alfian Ma'arif Mail (Universitas Ahmad Dahlan, Indonesia)
(4) * Anuchit Nirapai Mail (Rangsit University, Thailand)
(5) Kanyanat Javana Mail (Triamudom Suksa Nomklao Uttaradit School, Thailand)
(6) Pawichaya Pisa Mail (Triamudom Suksa Nomklao Uttaradit School, Thailand)
(7) Phanassanun Thajai Mail (Triamudom Suksa Nomklao Uttaradit School, Thailand)
(8) Supachai Keawkao Mail (Triamudom Suksa Nomklao Uttaradit School, Thailand)
(9) Kittipan Roongprasert Mail (Rangsit University, Thailand)
(10) Rawiphon Chotikunnan Mail (Rangsit University, Thailand)
(11) Pariwat Imura Mail (Rangsit University, Thailand)
(12) Nuntachai Thongpance Mail (Rangsit University, Thailand)
*corresponding author

Abstract


In engineering, especially for robots, navigation, and biomedical uses, accurate angle estimation is absolutely crucial. Using data from the IMU6050 sensor, which combines accelerometer and gyroscope readings, this work contrasts two sensor fusion methods: the Kalman filter and the complementary filter. The aim of the research is to find the most efficient filtering method for preserving accuracy and resilience throughout several motion contexts, including low-noise (standard rotation) and high-noise (external disturbances). With an eye toward improving sensor accuracy in dynamic applications, the study contribution is a thorough investigation of filter performance under different noise levels. MATLAB quantified estimate accuracy using key metrics like root mean square error (RMSE) and mean absolute error (MAE). Under controlled noise levels, our approach included methodical error analysis of both filters. Results show that, especially under low-noise conditions, the Kalman filter beats the complementary filter in terms of lower MAE and RMSE; it also shows adaptability and robustness in high-noise environments with much fewer errors than accelerometer-only and complementary filter outputs. These results show the relevance of the Kalman filter in practical settings like robotic control, motion tracking, and possible biomedical equipment, including patient positioning systems and wheelchairs with balance control. Future studies might investigate the implementation of the Kalman filter in sophisticated systems requiring accuracy, such as telemedicine robots or autonomous navigation. This work develops sensor fusion techniques and offers understanding of consistent sensor data processing in several operating environments.

Keywords


IMU6050; Kalman Filter; Complementary Filter; Sensor Fusion

   

DOI

https://doi.org/10.31763/ijrcs.v5i1.1674 		      

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References


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