dc.contributor |
Graduate Program in Electrical and Electronic Engineering. |
|
dc.contributor.advisor |
Anarım, Emin. |
|
dc.contributor.author |
Karakaya, Murat. |
|
dc.date.accessioned |
2023-03-16T10:21:01Z |
|
dc.date.available |
2023-03-16T10:21:01Z |
|
dc.date.issued |
2021. |
|
dc.identifier.other |
EE 2021 K37 |
|
dc.identifier.uri |
http://digitalarchive.boun.edu.tr/handle/123456789/13001 |
|
dc.description.abstract |
Kalman filter-based solutions proposed for nonlinear systems are frequently used in bearing-only tracking applications. Due to the physical conditions of these tracking applications, the measurements gathered may contain a high amount of noise. For example, if the measurement sensors are too far from the target being tracked, a small error in the calculated bearing or a small amount of noise exposure will cause the uncertainty in the tracking system to increase significantly. Since the effect of this large amount of noise can only be eliminated to a certain extent by Kalman filter-based solutions, tracking performance may decrease in these applications. In this thesis, various statistical and machine learning-based noise removal methods will be applied to reduce the noise in bearing measurements obtained with sensors. Then, these noise-reduced measurements will be used in Kalman filter-based solutions in bearingonly tracking. The effects of noise reduction methods on tracking performance will be compared with simulations on real vessel trajectories. |
|
dc.format.extent |
30 cm. |
|
dc.publisher |
Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2021. |
|
dc.subject.lcsh |
Kalman filtering. |
|
dc.subject.lcsh |
Tracking (Engineering) |
|
dc.title |
Bearing-only tracking with Kalman filters using smoothed measurements |
|
dc.format.pages |
xx, 89 leaves ; |
|