Abstract
The work is dedicated to the study of trajectory filtering algorithms based on the use of Kalman filter. A new algorithm for estimating trajectory parameters was synthesized. The algorithm is based on the model in the body-fixed frame during observations in the spherical coordinate system. A mathematical modelling was performed to obtain and analyze the results of trajectory filtration using the known linear and nonlinear Kalman filters and proposed algorithm. The study is carried out by mathematical modelling via MATLAB environment. It is established that the use of filtering with adjustment in body-fixed coordinates is more effective than the algorithm based on the known Kalman filters. Such result is explained that the proposed algorithm allows to take into account the nature of the motion of the tracked object more fully. At the same time, it combines the simplicity of linear filtering with a separate estimation for each coordinate. Thus, it is more preferred for practical application.
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Vasiliev, K.K., Saverkin, O.V. (2020). Comparative Evaluation of Algorithms for Trajectory Filtering. In: Favorskaya, M., Jain, L. (eds) Computer Vision in Control Systems—6. Intelligent Systems Reference Library, vol 182. Springer, Cham. https://doi.org/10.1007/978-3-030-39177-5_5
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DOI: https://doi.org/10.1007/978-3-030-39177-5_5
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