Multi-sensor joint target detection, tracking and classification via Bernoulli filter Article Swipe
Gaiyou Li
,
Ping Wei
,
Giorgio Battistelli
,
Luigi Chisci
,
Lin Gao
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2109.11259
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2109.11259
This paper focuses on \textit{joint detection, tracking and classification} (JDTC) of a target via multi-sensor fusion. The target can be present or not, can belong to different classes, and depending on its class can behave according to different kinematic modes. Accordingly, it is modeled as a suitably extended Bernoulli \textit{random finite set} (RFS) uniquely characterized by existence, classification, class-conditioned mode and class\&mode-conditioned state probability distributions. By designing suitable centralized and distributed rules for fusing information on target existence, class, mode and state from different sensor nodes, novel \textit{centralized} and \textit{distributed} JDTC \textit{Bernoulli filters} (C-JDTC-BF and D-JDTC-BF), are proposed. The performance of the proposed JDTC-BF approach is evaluated by means of simulation experiments.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2109.11259
- https://arxiv.org/pdf/2109.11259
- OA Status
- green
- Cited By
- 1
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3200807514
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3200807514Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2109.11259Digital Object Identifier
- Title
-
Multi-sensor joint target detection, tracking and classification via Bernoulli filterWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-09-23Full publication date if available
- Authors
-
Gaiyou Li, Ping Wei, Giorgio Battistelli, Luigi Chisci, Lin GaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2109.11259Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2109.11259Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2109.11259Direct OA link when available
- Concepts
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Bernoulli's principle, Tracking (education), Joint (building), Computer science, Artificial intelligence, Filter (signal processing), Computer vision, Track-before-detect, Pattern recognition (psychology), Engineering, Particle filter, Psychology, Aerospace engineering, Architectural engineering, PedagogyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1Per-year citation counts (last 5 years)
- References (count)
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25Number 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.novel | 86 |
| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.rules | 71 |
| abstract_inverted_index.state | 62, 81 |
| abstract_inverted_index.(JDTC) | 9 |
| abstract_inverted_index.behave | 34 |
| abstract_inverted_index.belong | 24 |
| abstract_inverted_index.class, | 78 |
| abstract_inverted_index.finite | 50 |
| abstract_inverted_index.fusing | 73 |
| abstract_inverted_index.modes. | 39 |
| abstract_inverted_index.nodes, | 85 |
| abstract_inverted_index.sensor | 84 |
| abstract_inverted_index.target | 12, 17, 76 |
| abstract_inverted_index.JDTC-BF | 103 |
| abstract_inverted_index.focuses | 2 |
| abstract_inverted_index.fusion. | 15 |
| abstract_inverted_index.modeled | 43 |
| abstract_inverted_index.present | 20 |
| abstract_inverted_index.approach | 104 |
| abstract_inverted_index.classes, | 27 |
| abstract_inverted_index.extended | 47 |
| abstract_inverted_index.filters} | 92 |
| abstract_inverted_index.proposed | 102 |
| abstract_inverted_index.suitable | 67 |
| abstract_inverted_index.suitably | 46 |
| abstract_inverted_index.tracking | 6 |
| abstract_inverted_index.uniquely | 53 |
| abstract_inverted_index.Bernoulli | 48 |
| abstract_inverted_index.according | 35 |
| abstract_inverted_index.depending | 29 |
| abstract_inverted_index.designing | 66 |
| abstract_inverted_index.different | 26, 37, 83 |
| abstract_inverted_index.evaluated | 106 |
| abstract_inverted_index.kinematic | 38 |
| abstract_inverted_index.proposed. | 97 |
| abstract_inverted_index.(C-JDTC-BF | 93 |
| abstract_inverted_index.detection, | 5 |
| abstract_inverted_index.existence, | 56, 77 |
| abstract_inverted_index.simulation | 110 |
| abstract_inverted_index.D-JDTC-BF), | 95 |
| abstract_inverted_index.centralized | 68 |
| abstract_inverted_index.distributed | 70 |
| abstract_inverted_index.information | 74 |
| abstract_inverted_index.performance | 99 |
| abstract_inverted_index.probability | 63 |
| abstract_inverted_index.Accordingly, | 40 |
| abstract_inverted_index.experiments. | 111 |
| abstract_inverted_index.multi-sensor | 14 |
| abstract_inverted_index.\textit{joint | 4 |
| abstract_inverted_index.characterized | 54 |
| abstract_inverted_index.\textit{random | 49 |
| abstract_inverted_index.distributions. | 64 |
| abstract_inverted_index.classification, | 57 |
| abstract_inverted_index.classification} | 8 |
| abstract_inverted_index.\textit{Bernoulli | 91 |
| abstract_inverted_index.class-conditioned | 58 |
| abstract_inverted_index.\textit{centralized} | 87 |
| abstract_inverted_index.\textit{distributed} | 89 |
| abstract_inverted_index.class\&mode-conditioned | 61 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |