Discretization of Dynamic Model and Process Noise in Ballistic Object Filtering

In this paper, we consider the discretization of the continuous-time dynamic model and process noise that occurs in ballistic object filtering problems. Our analysis shows that existing discretization methods are ad hoc in nature. We apply the 1.5 order Ito-Taylor approximation to these discretization problems. We specifically study the ballistic projectile filtering problem and derive equations for the discretized dynamic model and process noise. Our results are compared with those from existing algorithms.

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Publication Details

Published
2024-11-26
DOI
https://doi.org/10.1109/iccais63750.2024.10814233
Primary Topic
Neural Networks and Applications
Type
article
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article

Discretization of Dynamic Model and Process Noise in Ballistic Object Filtering

Xiaoqing Tian, Mahendra Mallick, Linfeng Xu
Neural Networks and Applications
article

Discretization of Dynamic Model and Process Noise in Ballistic Object Filtering

Xiaoqing Tian, Mahendra Mallick, Linfeng Xu
article en

Abstract

In this paper, we consider the discretization of the continuous-time dynamic model and process noise that occurs in ballistic object filtering problems. Our analysis shows that existing discretization methods are ad hoc in nature. We apply the 1.5 order Ito-Taylor approximation to these discretization problems. We specifically study the ballistic projectile filtering problem and derive equations for the discretized dynamic model and process noise. Our results are compared with those from existing algorithms.

Xi'an University of Science and Technology (CN), Northwestern Polytechnical University (CN)
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Neural Networks and Applications
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