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Bearing-only tracking with Kalman filters using smoothed measurements

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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 ;


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