Filtering in the presence of information losses based on the extended least squares method
O b j e c t i v e s . In radar systems for moving objects tracking, there are often gaps in the measurement of coordinates.The problem is mostly fully solved in continuous time in the theory of systems with a random structure within the framework of statistical Bayesian theory of filtration in ...
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| Format: | Article |
| Language: | Russian |
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National Academy of Sciences of Belarus, the United Institute of Informatics Problems
2022-03-01
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| Series: | Informatika |
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| Online Access: | https://inf.grid.by/jour/article/view/1179 |
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| author | V. M. Artemiev A. O. Naumov |
| author_facet | V. M. Artemiev A. O. Naumov |
| author_sort | V. M. Artemiev |
| collection | DOAJ |
| description | O b j e c t i v e s . In radar systems for moving objects tracking, there are often gaps in the measurement of coordinates.The problem is mostly fully solved in continuous time in the theory of systems with a random structure within the framework of statistical Bayesian theory of filtration in the presence of complete a priori statistical information. This approach leads to complex algorithms that are difficult to implement in practice. The purpose of investigation was to develop a filtering algorithm in conditions of information interruptions based on the use of extended least squares method.M e t h o d s . Methods of estimation theory are used, in particular, the extended least squares method, which makes it possible to find relatively simple algorithms with a minimum amount of a priori knowledge about the characteristics of the impacts.R e s u l t s . The algorithm for filtering radar signals has been developed, based on measurements of the moments of breaks and extrapolation of the measured coordinates at intervals of information lack. The resulting algorithm is nonlinear and therefore tracking disruptions may occur in the filter. The results of the algorithm are demonstrated using a model example. The estimation of the filtering accuracy and tracking failure conditions is carried out.Co n c l u s i o n . A filtering algorithm has been developed that allows determining the moments of the onset of breaks and extrapolating the estimates of useful information. The comparative simplicity of the algorithm makes it suitable for practical use. |
| format | Article |
| id | doaj-art-f3e61c49731e4be4a328fdbaaa1a204e |
| institution | Kabale University |
| issn | 1816-0301 |
| language | Russian |
| publishDate | 2022-03-01 |
| publisher | National Academy of Sciences of Belarus, the United Institute of Informatics Problems |
| record_format | Article |
| series | Informatika |
| spelling | doaj-art-f3e61c49731e4be4a328fdbaaa1a204e2025-02-03T11:46:28ZrusNational Academy of Sciences of Belarus, the United Institute of Informatics ProblemsInformatika1816-03012022-03-01191505810.37661/1816-0301-2022-19-1-50-58995Filtering in the presence of information losses based on the extended least squares methodV. M. Artemiev0A. O. Naumov1Institute of Applied Physics of the National Academy of Sciences of BelarusInstitute of Applied Physics of the National Academy of Sciences of BelarusO b j e c t i v e s . In radar systems for moving objects tracking, there are often gaps in the measurement of coordinates.The problem is mostly fully solved in continuous time in the theory of systems with a random structure within the framework of statistical Bayesian theory of filtration in the presence of complete a priori statistical information. This approach leads to complex algorithms that are difficult to implement in practice. The purpose of investigation was to develop a filtering algorithm in conditions of information interruptions based on the use of extended least squares method.M e t h o d s . Methods of estimation theory are used, in particular, the extended least squares method, which makes it possible to find relatively simple algorithms with a minimum amount of a priori knowledge about the characteristics of the impacts.R e s u l t s . The algorithm for filtering radar signals has been developed, based on measurements of the moments of breaks and extrapolation of the measured coordinates at intervals of information lack. The resulting algorithm is nonlinear and therefore tracking disruptions may occur in the filter. The results of the algorithm are demonstrated using a model example. The estimation of the filtering accuracy and tracking failure conditions is carried out.Co n c l u s i o n . A filtering algorithm has been developed that allows determining the moments of the onset of breaks and extrapolating the estimates of useful information. The comparative simplicity of the algorithm makes it suitable for practical use.https://inf.grid.by/jour/article/view/1179radar stationdigital filteringdetection of moving objectsthe method of least squarestrajectory selectioncycle slip |
| spellingShingle | V. M. Artemiev A. O. Naumov Filtering in the presence of information losses based on the extended least squares method Informatika radar station digital filtering detection of moving objects the method of least squares trajectory selection cycle slip |
| title | Filtering in the presence of information losses based on the extended least squares method |
| title_full | Filtering in the presence of information losses based on the extended least squares method |
| title_fullStr | Filtering in the presence of information losses based on the extended least squares method |
| title_full_unstemmed | Filtering in the presence of information losses based on the extended least squares method |
| title_short | Filtering in the presence of information losses based on the extended least squares method |
| title_sort | filtering in the presence of information losses based on the extended least squares method |
| topic | radar station digital filtering detection of moving objects the method of least squares trajectory selection cycle slip |
| url | https://inf.grid.by/jour/article/view/1179 |
| work_keys_str_mv | AT vmartemiev filteringinthepresenceofinformationlossesbasedontheextendedleastsquaresmethod AT aonaumov filteringinthepresenceofinformationlossesbasedontheextendedleastsquaresmethod |