FUSI DATA SENSOR MENGGUNAKAN FILTER KALMAN UNTUK ESTIMASI SIKAP SATELIT LAPAN A2
The largest population of artificial satellites today is in Low Earth Orbit (LEO) with significant magnetic field peturbation. Another characteristic of satellites in LEO is the frequency of eclipses that are more frequent, affecting the observability of the sun sensor, therefore a combination of se...
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| Format: | Theses |
| Language: | Indonesia |
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| Online Access: | https://digilib.itb.ac.id/gdl/view/67198 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | The largest population of artificial satellites today is in Low Earth Orbit (LEO) with significant magnetic field peturbation. Another characteristic of satellites in LEO is the frequency of eclipses that are more frequent, affecting the observability of the sun sensor, therefore a combination of sensors and noise processing will be required.
Noise processing is a process to minimize error in measurement and give the estimation value close to the true value. Methods that can be used for noise reduction in a corrupted signal are Kalman Filter(KF) and its various derivatives. Previous studies show a successful noise processing but only in one noise processing method and one sensor combination.
Hence, this research is aimed to investigate the utiliziation of the sensor output raw data fusion combined with the Kalman Filter algorithm to estimate the satellite’s attitude of an existing satellite. Input data of the sun sensor and magnetometer used in this thesis is the observation state before on-board processing. Then the state is transformed into quaternion to prevent the singularity then applied the noise processing method to obtain optimal method in estimating error. |
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