A Review of Data Fusion Techniques
Open accessAbstract
The integration of data and knowledge from several sources is known as data fusion. This paper summarizes the state of the data fusion field and describes the most relevant studies. We first enumerate and explain different classification schemes for data fusion. Then, the most common algorithms are reviewed. These methods and algorithms are presented using three different categories: (i) data association, (ii) state estimation, and (iii) decision fusion.
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Data fusion is the mechanism by which disparate data sources are merged to create an information state based on the sources' complementarity.
“Data fusion is underpinned by information theory and is the mechanism by which disparate data sources are merged to create an information state based on the sources’ complementarity15,16 (Box 1).”
From Multimodal machine learning in precision health: A scoping review (2022) · cites this paper as [15]
The Introduction defines data fusion as combining multiple sources to obtain improved information. Section 2.1 identifies complementary sources as one case, but also describes fusion of redundant and cooperative sources; the paper does not make complementarity the basis of data fusion generally. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThe advantages of data fusion can be categorized as complementary, redundant and cooperative features, though these are not mutually exclusive.
“More specifically, the advantages of data fusion can be categorized as complementary, redundant and cooperative features [4, 5], though these are not mutually exclusive.”
From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [5]
Section 2.1 (page unavailable) classifies relations between data sources as complementary, redundant, or cooperative, and describes the benefit of each. It does not explicitly say these three categories are nonexclusive; the paper's explicit “nonexclusive” statement in the Introduction refers to a different classification. (LLM, conf=0.84)
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“More specifically, the advantages of data fusion can be categorized as complementary, redundant and cooperative features [4, 5], though these are not mutually exclusive.”
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- Authors
- Castanedo, F.
- Affiliations
- Universidad de Deusto (ES)
- Corresponding author
- Castanedo, Federico
- ORCID
- Castanedo, Federico
- Venue
- The Scientific World Journal
- Publisher
- Wiley
- Published
- 2013-01
- Volume
- 2013
- Issue
- 1
- Pages
- 704504-704504
- ISSN
- 1537-744X (ISSN-L)
- 2356-6140
- PMC
- PMC3826336
- OpenAlex
- W2133297572
- Cited by
- 1,027
- References
- 78
- Citations by year
- 2026: 84 · 2025: 95 · 2024: 110 · 2023: 118 · 2022: 113 · 2021: 121 · 2020: 105 · 2019: 89 · 2018: 70 · 2017: 50 · 2016: 36 · 2015: 25 · 2014: 11
- FWCI
- 3.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- The Scientific World JOURNAL (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- data fusion, data association, state estimation, decision fusion, sensor data integration, classification systems
- MeSH terms
- Algorithms, Electronic Data Processing
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