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A maneuvering target tracking based on fastIMM-extended Viterbi algorithm.

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Title: A maneuvering target tracking based on fastIMM-extended Viterbi algorithm.
Authors: Di, Yi1,2 (AUTHOR), Li, Ruiheng1,2,3 (AUTHOR) liruiheng@cqu.edu.cn, Tian, Hao1,2 (AUTHOR), Guo, Jia1,2 (AUTHOR), Shi, Binghua1,2 (AUTHOR), Wang, Zheng1,2 (AUTHOR), Yan, Ke4 (AUTHOR), Liu, Yueheng5 (AUTHOR)
Source: Neural Computing & Applications. Apr2025, Vol. 37 Issue 12, p7925-7934. 10p.
Subjects: Acoustic arrays, Viterbi decoding, Artificial intelligence, Image processing, Algorithms
Abstract: A fastIMM-extended Viterbi (fastIMM-EV) algorithm-based maneuvering target tracking method is proposed for the real-time tracking of ground maneuvering targets by a ballistic acoustic array, which firstly adopts the extended Viterbi interactive multi-model (IMM-EV) algorithm to select the best model from a given model set to match the maneuvering target motion pattern; secondly, the α–β filter and α–β–γ filter are used to replace the 2D or 3D Kalman filter in the traditional IMM algorithm, respectively, to form the fastIMM-EV algorithm, which nearly improves the algorithm efficiency, and at the same time, for the switching problem of different fastIMM-EV modules, a target maneuver recognition parameter is defined as the switching factor of the fastIMM-EV module, so that fastIMM-EV to switch the module when the target maneuver occurs; finally, the MATLAB simulation test results verify the practicality and high efficiency of the algorithm in this paper compared with different IMM target tracking methods. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
  Group: Ti
  Data: A maneuvering target tracking based on fastIMM-extended Viterbi algorithm.
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  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Di%2C+Yi%22">Di, Yi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Ruiheng%22">Li, Ruiheng</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> liruiheng@cqu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Tian%2C+Hao%22">Tian, Hao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guo%2C+Jia%22">Guo, Jia</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shi%2C+Binghua%22">Shi, Binghua</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Zheng%22">Wang, Zheng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yan%2C+Ke%22">Yan, Ke</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Yueheng%22">Liu, Yueheng</searchLink><relatesTo>5</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Apr2025, Vol. 37 Issue 12, p7925-7934. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Acoustic+arrays%22">Acoustic arrays</searchLink><br /><searchLink fieldCode="DE" term="%22Viterbi+decoding%22">Viterbi decoding</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: A fastIMM-extended Viterbi (fastIMM-EV) algorithm-based maneuvering target tracking method is proposed for the real-time tracking of ground maneuvering targets by a ballistic acoustic array, which firstly adopts the extended Viterbi interactive multi-model (IMM-EV) algorithm to select the best model from a given model set to match the maneuvering target motion pattern; secondly, the α–β filter and α–β–γ filter are used to replace the 2D or 3D Kalman filter in the traditional IMM algorithm, respectively, to form the fastIMM-EV algorithm, which nearly improves the algorithm efficiency, and at the same time, for the switching problem of different fastIMM-EV modules, a target maneuver recognition parameter is defined as the switching factor of the fastIMM-EV module, so that fastIMM-EV to switch the module when the target maneuver occurs; finally, the MATLAB simulation test results verify the practicality and high efficiency of the algorithm in this paper compared with different IMM target tracking methods. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00521-023-09039-1
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        Type: general
      – SubjectFull: Viterbi decoding
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      – SubjectFull: Artificial intelligence
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      – SubjectFull: Image processing
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      – SubjectFull: Algorithms
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            NameFull: Di, Yi
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              Text: Apr2025
              Type: published
              Y: 2025
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