A Strong Maneuvering Target-Tracking Filtering Based on Intelligent Algorithm Article Swipe
Jing Li
,
Xinru Liang
,
Shengzhi Yuan
,
Haiyan Li
,
Changsheng Gao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1155/2024/9981332
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1155/2024/9981332
In this paper, a variable-structure multimodel (VSMM) filtering algorithm based on the long short-term memory (LSTM) regression-deep Q network (L-DQN) is proposed to accurately track strong maneuvering targets. The algorithm can map the selection of the model set to the selection of the action label and realize the purpose of a deep reinforcement-learning agent to replace the model switching in the traditional VSMM algorithm by reasonably designing a reward function, state space, and network structure. At the same time, the algorithm introduces a LSTM algorithm, which can compensate the error of tracking results based on model history information. The simulation results show that compared with the traditional VSMM algorithm, the proposed algorithm can quickly capture the maneuvering of the target, the response time is short, the calculation accuracy is significantly improved, and the range of adaptation is wider. Precise tracking of maneuvering targets was achieved.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2024/9981332
- https://downloads.hindawi.com/journals/ijae/2024/9981332.pdf
- OA Status
- gold
- Cited By
- 3
- References
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390806240
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390806240Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2024/9981332Digital Object Identifier
- Title
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A Strong Maneuvering Target-Tracking Filtering Based on Intelligent AlgorithmWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-11Full publication date if available
- Authors
-
Jing Li, Xinru Liang, Shengzhi Yuan, Haiyan Li, Changsheng GaoList of authors in order
- Landing page
-
https://doi.org/10.1155/2024/9981332Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/ijae/2024/9981332.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://downloads.hindawi.com/journals/ijae/2024/9981332.pdfDirect OA link when available
- Concepts
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Tracking (education), Computer science, Artificial intelligence, Algorithm, Computer vision, Psychology, PedagogyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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15Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(L-DQN) | 21 |
| abstract_inverted_index.<a:math | 17 |
| abstract_inverted_index.Precise | 140 |
| abstract_inverted_index.capture | 116 |
| abstract_inverted_index.history | 98 |
| abstract_inverted_index.network | 20, 75 |
| abstract_inverted_index.purpose | 50 |
| abstract_inverted_index.quickly | 115 |
| abstract_inverted_index.realize | 48 |
| abstract_inverted_index.replace | 57 |
| abstract_inverted_index.results | 94, 102 |
| abstract_inverted_index.target, | 121 |
| abstract_inverted_index.targets | 144 |
| abstract_inverted_index.accuracy | 129 |
| abstract_inverted_index.compared | 105 |
| abstract_inverted_index.proposed | 23, 112 |
| abstract_inverted_index.response | 123 |
| abstract_inverted_index.targets. | 29 |
| abstract_inverted_index.tracking | 93, 141 |
| abstract_inverted_index.achieved. | 146 |
| abstract_inverted_index.algorithm | 8, 31, 65, 82, 113 |
| abstract_inverted_index.designing | 68 |
| abstract_inverted_index.filtering | 7 |
| abstract_inverted_index.function, | 71 |
| abstract_inverted_index.improved, | 132 |
| abstract_inverted_index.selection | 35, 42 |
| abstract_inverted_index.switching | 60 |
| abstract_inverted_index.accurately | 25 |
| abstract_inverted_index.adaptation | 137 |
| abstract_inverted_index.algorithm, | 86, 110 |
| abstract_inverted_index.compensate | 89 |
| abstract_inverted_index.introduces | 83 |
| abstract_inverted_index.multimodel | 5 |
| abstract_inverted_index.reasonably | 67 |
| abstract_inverted_index.short-term | 13 |
| abstract_inverted_index.simulation | 101 |
| abstract_inverted_index.structure. | 76 |
| abstract_inverted_index.calculation | 128 |
| abstract_inverted_index.maneuvering | 28, 118, 143 |
| abstract_inverted_index.traditional | 63, 108 |
| abstract_inverted_index.information. | 99 |
| abstract_inverted_index.significantly | 131 |
| abstract_inverted_index.regression-deep | 16 |
| abstract_inverted_index.variable-structure | 4 |
| abstract_inverted_index.reinforcement-learning | 54 |
| abstract_inverted_index.id="M1"><a:mi>Q</a:mi></a:math> | 19 |
| abstract_inverted_index.xmlns:a="http://www.w3.org/1998/Math/MathML" | 18 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5051194962 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I2800710378 |
| citation_normalized_percentile.value | 0.82282049 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |