Comparison between Kalman Filter and Exponential Filter on IMU Data Acquisition
IMU (Inertial Measurement Unit) is the main component of inertial guidance systems used in aircraft, spacecraft, and watercraft, including guided missiles. The IMU works by sensing the motion, including the rate and direction of that motion, using the combination of accelerometers and gyroscopes. Th...
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2011
|
| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/32752/1/22_-_Comparison_between_Kalman_Filter_and_Exponential_Filter_on_IMU_Data_Acquitition.pdf |
| _version_ | 1797020169052094464 |
|---|---|
| author | Wahyudi, Wahyudi Susanto, Adhi Hadi, Sasongko Pramono |
| author_facet | Wahyudi, Wahyudi Susanto, Adhi Hadi, Sasongko Pramono |
| author_sort | Wahyudi, Wahyudi |
| collection | UGM |
| description | IMU (Inertial Measurement Unit) is the main component of inertial guidance systems used in aircraft, spacecraft, and watercraft, including guided missiles. The IMU works by sensing the motion, including the rate and direction of that motion, using the combination of accelerometers and gyroscopes. The data are collectedfrom these sensors allows a computer to track a craft’s position and rotation, using a method known as dead reckoning. One part of the IMU detecs the current rate of acceleration by using accelerometers, and the other the changes in rotational attributes like pitch, roll, and yaw by using gyroscopes. These data are transferred into the computer which calculates the current speed rotation and position, under a known initial position.
In this paper, we report our effort to utilize properly the ecquired data from those detectors. The result of two scrutinizing digital filters, namely the Kalman and exponential ones as well as the angle and position estimations base on microcontroller. The average error of rotation estimation using the exponential filter is smaller than by using the Kalman filter, which are around 4.98 % and 1.73 % respectively .On the other hand the average error of position estimation using Kalman filter is about 2.24 % whereas the error average using exponential filter is about 5 %.
|
| first_indexed | 2024-03-05T23:19:28Z |
| format | Conference or Workshop Item |
| id | oai:generic.eprints.org:32752 |
| institution | Universiti Gadjah Mada |
| language | English |
| last_indexed | 2024-03-13T19:11:45Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | oai:generic.eprints.org:327522014-03-05T01:35:58Z https://repository.ugm.ac.id/32752/ Comparison between Kalman Filter and Exponential Filter on IMU Data Acquisition Wahyudi, Wahyudi Susanto, Adhi Hadi, Sasongko Pramono Makalah prosiding IMU (Inertial Measurement Unit) is the main component of inertial guidance systems used in aircraft, spacecraft, and watercraft, including guided missiles. The IMU works by sensing the motion, including the rate and direction of that motion, using the combination of accelerometers and gyroscopes. The data are collectedfrom these sensors allows a computer to track a craft’s position and rotation, using a method known as dead reckoning. One part of the IMU detecs the current rate of acceleration by using accelerometers, and the other the changes in rotational attributes like pitch, roll, and yaw by using gyroscopes. These data are transferred into the computer which calculates the current speed rotation and position, under a known initial position. In this paper, we report our effort to utilize properly the ecquired data from those detectors. The result of two scrutinizing digital filters, namely the Kalman and exponential ones as well as the angle and position estimations base on microcontroller. The average error of rotation estimation using the exponential filter is smaller than by using the Kalman filter, which are around 4.98 % and 1.73 % respectively .On the other hand the average error of position estimation using Kalman filter is about 2.24 % whereas the error average using exponential filter is about 5 %. 2011-07 Conference or Workshop Item PeerReviewed application/pdf en https://repository.ugm.ac.id/32752/1/22_-_Comparison_between_Kalman_Filter_and_Exponential_Filter_on_IMU_Data_Acquitition.pdf Wahyudi, Wahyudi and Susanto, Adhi and Hadi, Sasongko Pramono (2011) Comparison between Kalman Filter and Exponential Filter on IMU Data Acquisition. In: The 12th International Conference on QiR (Quality in Research), 4 - 7 July 2011, Bali, Indonesia. |
| spellingShingle | Makalah prosiding Wahyudi, Wahyudi Susanto, Adhi Hadi, Sasongko Pramono Comparison between Kalman Filter and Exponential Filter on IMU Data Acquisition |
| title | Comparison between Kalman Filter and Exponential Filter on IMU Data Acquisition |
| title_full | Comparison between Kalman Filter and Exponential Filter on IMU Data Acquisition |
| title_fullStr | Comparison between Kalman Filter and Exponential Filter on IMU Data Acquisition |
| title_full_unstemmed | Comparison between Kalman Filter and Exponential Filter on IMU Data Acquisition |
| title_short | Comparison between Kalman Filter and Exponential Filter on IMU Data Acquisition |
| title_sort | comparison between kalman filter and exponential filter on imu data acquisition |
| topic | Makalah prosiding |
| url | https://repository.ugm.ac.id/32752/1/22_-_Comparison_between_Kalman_Filter_and_Exponential_Filter_on_IMU_Data_Acquitition.pdf |
| work_keys_str_mv | AT wahyudiwahyudi comparisonbetweenkalmanfilterandexponentialfilteronimudataacquisition AT susantoadhi comparisonbetweenkalmanfilterandexponentialfilteronimudataacquisition AT hadisasongkopramono comparisonbetweenkalmanfilterandexponentialfilteronimudataacquisition |