A Hierarchical Adaptive Moment Matching Multiple Model Tracking Method for Hypersonic Glide Target Under Measurement Uncertainty Article Swipe
Hanxing Shao
,
Jibin Zheng
,
Yulong Bai
,
Hongwei Liu
,
Y. L. Ge
,
Boyang Liu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25216621
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25216621
Hypersonic glide targets (HGTs) pose significant challenges for radar tracking due to complex maneuver strategies and time-varying statistics of measurement noise. Conventional single-model tracking methods are generally insufficient to fully capture maneuver modes, while existing multiple-model methods face trade-offs between model set completeness and computational efficiency. In addition, existing tracking methods struggle to cope with the non-Gaussian noise during hypersonic flight. To overcome these limitations, a Hierarchical Adaptive Moment Matching (HAMM) multiple-model method is proposed in this paper. Firstly, a comprehensive model set is constructed to cover characteristic maneuver modes. Subsequently, a hierarchical multiple-model framework is developed where: (1) a coarse model set is dynamically adapted by multi-frame posterior probability evolution and Rényi divergence criteria; (2) a fine model set is generated based on the moment matching method. Furthermore, the minimum error entropy cubature Kalman filter (MEECKF) is proposed to suppress the non-Gaussian measurement noise with high stability. Monte Carlo simulations demonstrate that the proposed method achieves improved positioning accuracy and faster convergence.
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- https://www.mdpi.com/1424-8220/25/21/6621/pdf?version=1761651535
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- References
- 40
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A Hierarchical Adaptive Moment Matching Multiple Model Tracking Method for Hypersonic Glide Target Under Measurement UncertaintyWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-10-28Full publication date if available
- Authors
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Hanxing Shao, Jibin Zheng, Yulong Bai, Hongwei Liu, Y. L. Ge, Boyang LiuList of authors in order
- Landing page
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0Total citation count in OpenAlex
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| abstract_inverted_index.measurement | 19, 143 |
| abstract_inverted_index.multi-frame | 107 |
| abstract_inverted_index.positioning | 158 |
| abstract_inverted_index.probability | 109 |
| abstract_inverted_index.significant | 5 |
| abstract_inverted_index.simulations | 150 |
| abstract_inverted_index.Conventional | 21 |
| abstract_inverted_index.Furthermore, | 128 |
| abstract_inverted_index.Hierarchical | 66 |
| abstract_inverted_index.completeness | 42 |
| abstract_inverted_index.convergence. | 162 |
| abstract_inverted_index.hierarchical | 92 |
| abstract_inverted_index.insufficient | 27 |
| abstract_inverted_index.limitations, | 64 |
| abstract_inverted_index.non-Gaussian | 56, 142 |
| abstract_inverted_index.single-model | 22 |
| abstract_inverted_index.time-varying | 16 |
| abstract_inverted_index.Subsequently, | 90 |
| abstract_inverted_index.comprehensive | 80 |
| abstract_inverted_index.computational | 44 |
| abstract_inverted_index.characteristic | 87 |
| abstract_inverted_index.multiple-model | 35, 71, 93 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5100633345 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I149594827 |
| citation_normalized_percentile |