Implementation of Kalman Filter for Accuracy Improvement and Angular Stability as a Control Reference Parallel Manipulator for Camera Pointing on CAN Satellite
DOI:
https://doi.org/10.55981/ijoa.2025.8981Keywords:
Kalman Filter, Parallel manipulator, CanSat, Inertial Measurement UnitAbstract
This study focuses on the application of the Kalman Filter to improve the accuracy and stability of angular data obtained from Inertial Measurement Unit sensors, which are often affected by noise and bias. The refined angular data serves as a control reference for the parallel manipulator used in the camera pointing system on CAN satellites. Accurate and stable reading angles are essential to ensure precise camera alignment, especially in dynamic environments with disturbances. This study integrates the Kalman Filter into the IMU data processing pipeline to filter the raw roll, pitch, and yaw. We tested the yaw stability improvement by 5.29% and filter performance improvement with 29.25% accuracy, pitch stability improved by 4.63% with 31.12% filter accuracy improvement, and roll stability improved by 1.71% with 28.99% filter accuracy improvement. These filtered angles are then used to control the parallel manipulator, allowing for precise orientation adjustment. The system performance is evaluated in terms of angular accuracy, stability, and manipulator responsibility. The results show a significant improvement in the angular quality of the data, with reduced noise and bias, leading to improved manipulator control. This implementation supports the development of high-precision camera systems for CAN satellites, which require robust and reliable orientation mechanisms. The proposed approach contributes to advancing control systems in small-scale satellite technology, where accuracy and stability are of critical importance. This study highlights the potential of the Kalman Filter in enhancing sensor accuracy for CAN satellite camera pointing systems. However, further research is needed to address dynamic environmental variations that may affect sensor performance. Future studies could explore integrating complementary filtering techniques or machine learning models to optimize data fusion and improve overall system resilience.
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