Track-Before-Detect Algorithm Based on Particle Filter with Sub-Band Adaptive Weighting Article Swipe
Xiaolin Wang
,
Yaowu Chen
,
Kaiyue Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/electronics14122349
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/electronics14122349
In the realm of underwater acoustic signal processing, challenges such as random missing measurements due to low signal-to-noise ratios, merging–splitting contacts in the measurement space, and prolonged trajectory losses due to target interference pose significant difficulties for passive sonar tracking. Conventional tracking methods often struggle with tracking losses or association errors in these scenarios. However, particle filter (PF)-based track-before-detect (TBD) methods have demonstrated significant advantages in avoiding association challenges. The PF-TBD method calculates the posterior density distribution using the energy accumulation of multiple pings along the particle trajectories, thereby circumventing the association problem between measurements. Consequently, this method is less sensitive to missing measurements but relies on trajectory continuity. When a weak target crosses paths with a strong one, it can be submerged by strong interference for an extended period, leading to discontinuities in the tracking results. To address these issues, this study proposes a TBD algorithm based on particle states and band features. The algorithm employs frequency-band adaptive matching for each tracking target to enhance the continuity of the target trajectories. This joint processing improves tracking outcomes for weak targets, particularly in crossing scenarios processed by PF-TBD. The effectiveness of the algorithm is validated using experimental data obtained at sea. The proposed algorithm demonstrates superior performance in terms of tracking accuracy and trajectory continuity compared to existing methods, making it a valuable addition to the field of underwater target tracking.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics14122349
- https://www.mdpi.com/2079-9292/14/12/2349/pdf?version=1749370623
- OA Status
- gold
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411156645
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411156645Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/electronics14122349Digital Object Identifier
- Title
-
Track-Before-Detect Algorithm Based on Particle Filter with Sub-Band Adaptive WeightingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-08Full publication date if available
- Authors
-
Xiaolin Wang, Yaowu Chen, Kaiyue ZhangList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics14122349Publisher landing page
- PDF URL
-
https://www.mdpi.com/2079-9292/14/12/2349/pdf?version=1749370623Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2079-9292/14/12/2349/pdf?version=1749370623Direct OA link when available
- Concepts
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Weighting, Particle filter, Track (disk drive), Algorithm, Computer science, Adaptive filter, Particle (ecology), Filter (signal processing), Artificial intelligence, Computer vision, Physics, Acoustics, Geology, Oceanography, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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35Number 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.due | 14, 29 |
| abstract_inverted_index.for | 36, 126, 160, 178 |
| abstract_inverted_index.low | 16 |
| abstract_inverted_index.the | 1, 22, 73, 78, 85, 90, 134, 166, 169, 191, 225 |
| abstract_inverted_index.This | 172 |
| abstract_inverted_index.When | 109 |
| abstract_inverted_index.band | 152 |
| abstract_inverted_index.data | 197 |
| abstract_inverted_index.each | 161 |
| abstract_inverted_index.have | 61 |
| abstract_inverted_index.less | 99 |
| abstract_inverted_index.one, | 118 |
| abstract_inverted_index.pose | 33 |
| abstract_inverted_index.sea. | 200 |
| abstract_inverted_index.such | 9 |
| abstract_inverted_index.this | 96, 141 |
| abstract_inverted_index.weak | 111, 179 |
| abstract_inverted_index.with | 45, 115 |
| abstract_inverted_index.(TBD) | 59 |
| abstract_inverted_index.along | 84 |
| abstract_inverted_index.based | 147 |
| abstract_inverted_index.field | 226 |
| abstract_inverted_index.joint | 173 |
| abstract_inverted_index.often | 43 |
| abstract_inverted_index.paths | 114 |
| abstract_inverted_index.pings | 83 |
| abstract_inverted_index.realm | 2 |
| abstract_inverted_index.sonar | 38 |
| abstract_inverted_index.study | 142 |
| abstract_inverted_index.terms | 208 |
| abstract_inverted_index.these | 52, 139 |
| abstract_inverted_index.using | 77, 195 |
| abstract_inverted_index.PF-TBD | 70 |
| abstract_inverted_index.energy | 79 |
| abstract_inverted_index.errors | 50 |
| abstract_inverted_index.filter | 56 |
| abstract_inverted_index.losses | 28, 47 |
| abstract_inverted_index.making | 219 |
| abstract_inverted_index.method | 71, 97 |
| abstract_inverted_index.random | 11 |
| abstract_inverted_index.relies | 105 |
| abstract_inverted_index.signal | 6 |
| abstract_inverted_index.space, | 24 |
| abstract_inverted_index.states | 150 |
| abstract_inverted_index.strong | 117, 124 |
| abstract_inverted_index.target | 31, 112, 163, 170, 229 |
| abstract_inverted_index.PF-TBD. | 187 |
| abstract_inverted_index.address | 138 |
| abstract_inverted_index.between | 93 |
| abstract_inverted_index.crosses | 113 |
| abstract_inverted_index.density | 75 |
| abstract_inverted_index.employs | 156 |
| abstract_inverted_index.enhance | 165 |
| abstract_inverted_index.issues, | 140 |
| abstract_inverted_index.leading | 130 |
| abstract_inverted_index.methods | 42, 60 |
| abstract_inverted_index.missing | 12, 102 |
| abstract_inverted_index.passive | 37 |
| abstract_inverted_index.period, | 129 |
| abstract_inverted_index.problem | 92 |
| abstract_inverted_index.ratios, | 18 |
| abstract_inverted_index.thereby | 88 |
| abstract_inverted_index.However, | 54 |
| abstract_inverted_index.accuracy | 211 |
| abstract_inverted_index.acoustic | 5 |
| abstract_inverted_index.adaptive | 158 |
| abstract_inverted_index.addition | 223 |
| abstract_inverted_index.avoiding | 66 |
| abstract_inverted_index.compared | 215 |
| abstract_inverted_index.contacts | 20 |
| abstract_inverted_index.crossing | 183 |
| abstract_inverted_index.existing | 217 |
| abstract_inverted_index.extended | 128 |
| abstract_inverted_index.improves | 175 |
| abstract_inverted_index.matching | 159 |
| abstract_inverted_index.methods, | 218 |
| abstract_inverted_index.multiple | 82 |
| abstract_inverted_index.obtained | 198 |
| abstract_inverted_index.outcomes | 177 |
| abstract_inverted_index.particle | 55, 86, 149 |
| abstract_inverted_index.proposed | 202 |
| abstract_inverted_index.proposes | 143 |
| abstract_inverted_index.results. | 136 |
| abstract_inverted_index.struggle | 44 |
| abstract_inverted_index.superior | 205 |
| abstract_inverted_index.targets, | 180 |
| abstract_inverted_index.tracking | 41, 46, 135, 162, 176, 210 |
| abstract_inverted_index.valuable | 222 |
| abstract_inverted_index.algorithm | 146, 155, 192, 203 |
| abstract_inverted_index.features. | 153 |
| abstract_inverted_index.posterior | 74 |
| abstract_inverted_index.processed | 185 |
| abstract_inverted_index.prolonged | 26 |
| abstract_inverted_index.scenarios | 184 |
| abstract_inverted_index.sensitive | 100 |
| abstract_inverted_index.submerged | 122 |
| abstract_inverted_index.tracking. | 39, 230 |
| abstract_inverted_index.validated | 194 |
| abstract_inverted_index.(PF)-based | 57 |
| abstract_inverted_index.advantages | 64 |
| abstract_inverted_index.calculates | 72 |
| abstract_inverted_index.challenges | 8 |
| abstract_inverted_index.continuity | 167, 214 |
| abstract_inverted_index.processing | 174 |
| abstract_inverted_index.scenarios. | 53 |
| abstract_inverted_index.trajectory | 27, 107, 213 |
| abstract_inverted_index.underwater | 4, 228 |
| abstract_inverted_index.association | 49, 67, 91 |
| abstract_inverted_index.challenges. | 68 |
| abstract_inverted_index.continuity. | 108 |
| abstract_inverted_index.measurement | 23 |
| abstract_inverted_index.performance | 206 |
| abstract_inverted_index.processing, | 7 |
| abstract_inverted_index.significant | 34, 63 |
| abstract_inverted_index.Conventional | 40 |
| abstract_inverted_index.accumulation | 80 |
| abstract_inverted_index.demonstrated | 62 |
| abstract_inverted_index.demonstrates | 204 |
| abstract_inverted_index.difficulties | 35 |
| abstract_inverted_index.distribution | 76 |
| abstract_inverted_index.experimental | 196 |
| abstract_inverted_index.interference | 32, 125 |
| abstract_inverted_index.measurements | 13, 103 |
| abstract_inverted_index.particularly | 181 |
| abstract_inverted_index.Consequently, | 95 |
| abstract_inverted_index.circumventing | 89 |
| abstract_inverted_index.effectiveness | 189 |
| abstract_inverted_index.measurements. | 94 |
| abstract_inverted_index.trajectories, | 87 |
| abstract_inverted_index.trajectories. | 171 |
| abstract_inverted_index.frequency-band | 157 |
| abstract_inverted_index.discontinuities | 132 |
| abstract_inverted_index.signal-to-noise | 17 |
| abstract_inverted_index.merging–splitting | 19 |
| abstract_inverted_index.track-before-detect | 58 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5100395177 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I4210144142, https://openalex.org/I76130692 |
| citation_normalized_percentile.value | 0.09398872 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |