Knowledge‐based multiple hypothesis tracking and identification of manoeuvring reentry targets
Abstract This paper addresses the integrated tracking and identification problem of a manoeuvring reentry target that performs intentional lateral manoeuvres to disrupt ground radars. Unlike previous approaches, prior knowledge of the lift‐induced drag is incorporated into a new manoeuvring model to...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-10-01
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| Series: | IET Radar, Sonar & Navigation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rsn2.12436 |
| _version_ | 1827797340815097856 |
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| author | Chan‐Seok Lee Ick‐Ho Whang Won‐Sang Ra |
| author_facet | Chan‐Seok Lee Ick‐Ho Whang Won‐Sang Ra |
| author_sort | Chan‐Seok Lee |
| collection | DOAJ |
| description | Abstract This paper addresses the integrated tracking and identification problem of a manoeuvring reentry target that performs intentional lateral manoeuvres to disrupt ground radars. Unlike previous approaches, prior knowledge of the lift‐induced drag is incorporated into a new manoeuvring model to describe the reentry target dynamics more explicitly. This model can account for the constraint between lift and drag, which is beneficial in ensuring the reliability of target state estimation. Noticing that the lift‐induced drag is an inherent characteristics of a reentry target that distinguishes the target's identity from others belonging to the same class, the integrated target tracking and identification problem is formulated within the framework of the multiple hypothesis testing about a set of manoeuvring models constructed by different prior knowledge. The proposed approach enables the authors to derive the optimal solution to the given problem in a mathematically rigorous manner. To cope with the real‐time implementation issue, a hypothesis merging strategy is also devised in view of maintaining the target identification performance. Simulation results demonstrate that the proposed scheme provides superior performance and reliability both in target tracking and identification compared to the existing method, despite imperfectness of prior knowledge. |
| first_indexed | 2024-03-11T19:20:49Z |
| format | Article |
| id | doaj.art-2391656278c04fff8173b0d178bbc903 |
| institution | Directory Open Access Journal |
| issn | 1751-8784 1751-8792 |
| language | English |
| last_indexed | 2024-03-11T19:20:49Z |
| publishDate | 2023-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Radar, Sonar & Navigation |
| spelling | doaj.art-2391656278c04fff8173b0d178bbc9032023-10-07T08:00:40ZengWileyIET Radar, Sonar & Navigation1751-87841751-87922023-10-0117101479149710.1049/rsn2.12436Knowledge‐based multiple hypothesis tracking and identification of manoeuvring reentry targetsChan‐Seok Lee0Ick‐Ho Whang1Won‐Sang Ra2School of Mechanical and Control Engineering Pohang Gyeongbuk South KoreaSchool of Mechanical and Control Engineering Pohang Gyeongbuk South KoreaSchool of Mechanical and Control Engineering Pohang Gyeongbuk South KoreaAbstract This paper addresses the integrated tracking and identification problem of a manoeuvring reentry target that performs intentional lateral manoeuvres to disrupt ground radars. Unlike previous approaches, prior knowledge of the lift‐induced drag is incorporated into a new manoeuvring model to describe the reentry target dynamics more explicitly. This model can account for the constraint between lift and drag, which is beneficial in ensuring the reliability of target state estimation. Noticing that the lift‐induced drag is an inherent characteristics of a reentry target that distinguishes the target's identity from others belonging to the same class, the integrated target tracking and identification problem is formulated within the framework of the multiple hypothesis testing about a set of manoeuvring models constructed by different prior knowledge. The proposed approach enables the authors to derive the optimal solution to the given problem in a mathematically rigorous manner. To cope with the real‐time implementation issue, a hypothesis merging strategy is also devised in view of maintaining the target identification performance. Simulation results demonstrate that the proposed scheme provides superior performance and reliability both in target tracking and identification compared to the existing method, despite imperfectness of prior knowledge.https://doi.org/10.1049/rsn2.12436filtering theoryKalman filtersmissilesradar trackingstate estimationtracking filters |
| spellingShingle | Chan‐Seok Lee Ick‐Ho Whang Won‐Sang Ra Knowledge‐based multiple hypothesis tracking and identification of manoeuvring reentry targets IET Radar, Sonar & Navigation filtering theory Kalman filters missiles radar tracking state estimation tracking filters |
| title | Knowledge‐based multiple hypothesis tracking and identification of manoeuvring reentry targets |
| title_full | Knowledge‐based multiple hypothesis tracking and identification of manoeuvring reentry targets |
| title_fullStr | Knowledge‐based multiple hypothesis tracking and identification of manoeuvring reentry targets |
| title_full_unstemmed | Knowledge‐based multiple hypothesis tracking and identification of manoeuvring reentry targets |
| title_short | Knowledge‐based multiple hypothesis tracking and identification of manoeuvring reentry targets |
| title_sort | knowledge based multiple hypothesis tracking and identification of manoeuvring reentry targets |
| topic | filtering theory Kalman filters missiles radar tracking state estimation tracking filters |
| url | https://doi.org/10.1049/rsn2.12436 |
| work_keys_str_mv | AT chanseoklee knowledgebasedmultiplehypothesistrackingandidentificationofmanoeuvringreentrytargets AT ickhowhang knowledgebasedmultiplehypothesistrackingandidentificationofmanoeuvringreentrytargets AT wonsangra knowledgebasedmultiplehypothesistrackingandidentificationofmanoeuvringreentrytargets |