ISSN 1991-2927
 

ACP № 3 (57) 2019

Mathematical Modeling and Research of Filtering Algorithms While Target Data Tragectory Processing

Aleksandr Sergeevich Gutorov, Federal Research-and-Production Center Open Joint-Stock Company ‘Research-and-Production Association ‘Mars’, graduated from the Faculty of Radioengineering at Ulyanovsk State Technical University; a post-graduate student of Ulyanovsk State Technical University, Chief Designer at Federal Research-and-Production Center Open Joint-Stock Company ‘Research-and-Production Association ‘Mars’; an author of articles in the field of statistical methods of signal processing. [e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it. ]A. Gutorov

Mathematical Modeling and Research of Filtering Algorithms While Target Data Tragectory Processing 39_5.pdf

The tracking of a maneuvering target in automated systems is a quite difficult task. A sudden change in speed of a target or change of its movement direction can have a major effect on the result of target movement parameters filtering. There are some methods of target movement characterization in statically indeterminate situations, at which conformities between measured and real objects positions are unknown, such as algorithms using Kalman Filter, multimodel filtering algorithms, and algorithms of intermodel interaction. The goal of this article is a research of algorithms for preliminary processing of target trajectories experimental data, intended for smoothing random interferences. The algorithm based on the spline smoothing function constructed by several trajectory points is also offered to be used to increase accuracy of maneuvering trajectory estimation in target tracking. This algorithm makes it possible to evaluate the intensive change of target movement parameters in case the dynamic movement model is not available, based on measured data and its approximation. The simulation of target movement parameters estimation algorithms proves that this algorithm provides a more exact result in comparison with algorithms using Kalman filter. In addition, this algorithm is quite simple to implement and requires not many computational resources. This algorithm can be used together with multimodel radar data processing algorithms.

Radar location, detection, discrimination, estimation, filtering, simulation modeling.

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