Veri Füzyonu Sürecine Yönelik Kuramsal ve Metodolojik Bir İnceleme: Mimari Yapılar, Algoritmalar ve Zorluklar

Öz

Bu çalışma, veri füzyonu sürecini kuramsal ve metodolojik bir perspektiften inceleyerek, farklı uygulama seviyeleri, algoritmalar, mimari modeller ve karşılaşılan zorlukları sistematik biçimde ele almaktadır. Veri füzyonu; sağlık, tarım, çevresel izleme, otonom sistemler, robotik ve endüstriyel alanlar gibi pek çok disiplin için daha güvenilir bilgi üretimi, belirsizliklerin azaltılması ve karar destek mekanizmalarının güçlendirilmesinde kritik bir rol üstlenmektedir. Literatürde, veri füzyon yöntemleri kural tabanlı, olasılık temelli, yapay zekâ tabanlı ve optimizasyon odaklı yaklaşımlar altında sınıflandırılmakta; bu yöntemler farklı senaryo ve veri tiplerine göre avantajlar ve sınırlılıklar sunmaktadır. Ayrıca, merkezî, dağıtık ve hibrit mimariler aracılığıyla tasarlanan sistemler, ölçeklenebilirlik, hata toleransı ve gerçek zamanlılık gibi özellikler açısından değerlendirilmiştir. Çalışma, aynı zamanda veri füzyonu süreçlerinde karşılaşılan temel zorlukları (veri heterojenliği, hizalama problemleri, donanımsal sınırlamalar, gizlilik ve güvenlik kaygıları) tartışarak, bu engellerin aşılmasına yönelik çözüm yaklaşımlarını da ortaya koymaktadır. Sonuç olarak, veri füzyonu yalnızca teknik bir entegrasyon değil, aynı zamanda sürdürülebilir ve güvenilir karar destek sistemleri için stratejik bir metodolojik çerçeve sunmaktadır.

A Theoretical and Methodological Review of the Data Fusion Process: Architectures, Algorithms, and Challenges

Öz

This study provides a comprehensive theoretical and methodological analysis of the data fusion process, systematically addressing its application levels, algorithms, architectural models, and associated challenges. Data fusion plays a pivotal role across various disciplines—including healthcare, agriculture, environmental monitoring, autonomous systems, robotics, and industrial domains—by generating more reliable information, reducing uncertainty, and strengthening decision-support mechanisms. In the literature, data fusion approaches are classified into rule-based, probabilistic, artificial intelligence-driven, and optimization-oriented methods, each offering distinct advantages and limitations depending on the scenario and data type. Furthermore, centralized, distributed, and hybrid architectures are evaluated in terms of scalability, fault tolerance, and real-time performance. This study also highlights critical challenges in data fusion, such as data heterogeneity, alignment issues, hardware constraints, and privacy and security concerns, while exploring strategies to address these barriers. Ultimately, data fusion is positioned not merely as a technical integration process but as a strategic methodological framework for building sustainable and trustworthy decision-support systems.

Anahtar Kelimeler

• Artificial Intelligence
• Data Fusion
• Sensor Integration

Kaynakça

1. Aamir A, Iqbal A, Jawed F, Ashfaque F, Hafsa H, Anas Z, Oduoye MO, Basit A, Ahmed S, Abdul Rauf S, Khan M, Mansoor T (2024) Exploring the current and prospective role of artificial intelligence in disease diagnosis. Ann Med Surg (Lond) 86:943–949. https://doi.org/10.1097/MS9.0000000000001700.
2. Mohan AMA, Kumar SS, Annam V, Yadav M, Prasanth PV (2023) Role of AI (Artificial Intelligence) and Machine Learning in Transforming Operations in Healthcare Industry: An Empirical Study. International Journal of Membrane Science and Technology 10:2069–2076. https://doi.org/10.15379/ijmst.v10i2.2774
3. Gagolewski M (2015) Data Fusion: Theory, Methods, and Applications
4. Torres ABB, Da Rocha AR, Coelho Da Silva TL, De Souza JN, Gondim RS (2020) Multilevel data fusion for the internet of things in smart agriculture. Computers and Electronics in Agriculture 171:105309. https://doi.org/10.1016/j.compag.2020.105309
5. Chung Baek AM, Kim T, Seong M, Lee S, Kang H, Park E, Jung ID, Kim N (2025) Multimodal deep learning for enhanced temperature prediction with uncertainty quantification in directed energy deposition (DED) process. Virtual and Physical Prototyping 20:e2474532. https://doi.org/10.1080/17452759.2025.2474532
6. Dong J, Hao M, Ding F, Chen S, Wu J, Zhuo J, Jiang D (2025) A Novel Multimodal Data Fusion Framework: Enhancing Prediction and Understanding of Inter-State Cyberattacks. Big Data and Cognitive Computing 9:63. https://doi.org/10.3390/bdcc9030063
7. Wei Y, Wu D, Terpenny J (2021) Decision-Level Data Fusion in Quality Control and Predictive Maintenance. IEEE Transactions on Automation Science and Engineering 18:184–194. https://doi.org/10.1109/TASE.2020.2964998
8. Conti F, Madeo F, Boiano A, Tarabini M (2023) Electrical and mechanical data fusion for hydraulic valve leakage diagnosis. Meas Sci Technol 34:044011. https://doi.org/10.1088/1361-6501/acb376

9. Hong D, Gao L, Yokoya N, Yao J, Chanussot J, Du Q, Zhang B (2021) More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification. IEEE Transactions on Geoscience and Remote Sensing 59:4340–4354. https://doi.org/10.1109/TGRS.2020.3016820
10. Duenias D, Nichyporuk B, Arbel T, Riklin Raviv T (2025) Hyperfusion: A hypernetwork approach to multimodal integration of tabular and medical imaging data for predictive modeling. Medical Image Analysis 102:103503. https://doi.org/10.1016/j.media.2025.103503
11. Teoh JR, Dong J, Zuo X, Lai KW, Hasikin K, Wu X (2024) Advancing healthcare through multimodal data fusion: a comprehensive review of techniques and applications. PeerJ Comput Sci 10:e2298. https://doi.org/10.7717/peerj-cs.2298
12. Gezimati M, Singh G (2025) Deep Learning for Multimodal Breast Cancer Characterization With Emergence of Terahertz and Infrared Imaging. IEEE Transactions on Instrumentation and Measurement 74:1–14. https://doi.org/10.1109/TIM.2025.3547084
13. Feng W-S, Chen W-C, Lin J-Y, Tseng H-Y, Chen C-L, Chou C-Y, Cho D-Y, Lin Y-B (2024) Design and Implementation of an Intensive Care Unit Command Center for Medical Data Fusion. Sensors 24:3929. https://doi.org/10.3390/s24123929
14. Yang G, Ye Q, Xia J (2022) Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond. Information Fusion 77:29–52. https://doi.org/10.1016/j.inffus.2021.07.016
15. Uddin MdZ, Hassan MM, Alsanad A, Savaglio C (2020) A body sensor data fusion and deep recurrent neural network-based behavior recognition approach for robust healthcare. Information Fusion 55:105–115. https://doi.org/10.1016/j.inffus.2019.08.004
16. Qiu H, Qiu M, Lu Z (2020) Selective encryption on ECG data in body sensor network based on supervised machine learning. Information Fusion 55:59–67. https://doi.org/10.1016/j.inffus.2019.07.012
17. Lin Y-C, Chi W-J, Lin Y-Q (2020) The improvement of spatial-temporal resolution of PM2.5 estimation based on micro-air quality sensors by using data fusion technique. Environment International 134:105305. https://doi.org/10.1016/j.envint.2019.105305
18. Duan R, Huang C, Dou P, Hou J, Zhang Y, Gu J (2025) Fine-scale forest classification with multi-temporal sentinel-1/2 imagery using a temporal convolutional neural network. International Journal of Digital Earth 18:2457953. https://doi.org/10.1080/17538947.2025.2457953
19. Allu AR, Mesapam S (2025) Impact of remote sensing data fusion on agriculture applications: A review. European Journal of Agronomy 164:127478. https://doi.org/10.1016/j.eja.2024.127478
20. Chang W-Q, Hou H-Y, Li P-Y, Shen MW, Kuo C-L, Lin T-H, Chang LC, Chao C-K, Liu J-Y (2025) Hyper Spectral Camera ANalyzer (HyperSCAN). Remote Sensing 17:842. https://doi.org/10.3390/rs17050842
21. Meraner A, Ebel P, Zhu XX, Schmitt M (2020) Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion. ISPRS Journal of Photogrammetry and Remote Sensing 166:333–346. https://doi.org/10.1016/j.isprsjprs.2020.05.013
22. Li S, Chen S, Li X, Zhou Y, Wang S (2025) Accurate and automatic spatiotemporal calibration for multi-modal sensor system based on continuous-time optimization. Information Fusion 120:103071. https://doi.org/10.1016/j.inffus.2025.103071
23. Zha L, Gong C, Lv K (2025) Real-time localization and navigation method for autonomous vehicles based on multi-modal data fusion by integrating memory transformer and DDQN. Image and Vision Computing 156:105484. https://doi.org/10.1016/j.imavis.2025.105484
24. Liu Z, Cai Y, Wang H, Chen L, Gao H, Jia Y, Li Y (2022) Robust Target Recognition and Tracking of Self-Driving Cars With Radar and Camera Information Fusion Under Severe Weather Conditions. IEEE Transactions on Intelligent Transportation Systems 23:6640–6653. https://doi.org/10.1109/TITS.2021.3059674
25. Liu Y, Wang Z, Peng L, Xu Q, Li K (2023) A Detachable and Expansible Multisensor Data Fusion Model for Perception in Level 3 Autonomous Driving System. IEEE Transactions on Intelligent Transportation Systems 24:1814–1827. https://doi.org/10.1109/TITS.2022.3220025
26. Yang J, Liu S, Su H, Tian Y (2021) Driving assistance system based on data fusion of multisource sensors for autonomous unmanned ground vehicles. Computer Networks 192:108053. https://doi.org/10.1016/j.comnet.2021.108053
27. Zhuang Y, Sun X, Li Y, Huai J, Hua L, Yang X, Cao X, Zhang P, Cao Y, Qi L, Yang J, El-Bendary N, El-Sheimy N, Thompson J, Chen R (2023) Multi-sensor integrated navigation/positioning systems using data fusion: From analytics-based to learning-based approaches. Information Fusion 95:62–90. https://doi.org/10.1016/j.inffus.2023.01.025
28. Motroni A, Buffi A, Nepa P (2021) A Survey on Indoor Vehicle Localization Through RFID Technology. IEEE Access 9:17921–17942. https://doi.org/10.1109/ACCESS.2021.3052316
29. Gao Q, Liu J, Ju Z (2021) Hand gesture recognition using multimodal data fusion and multiscale parallel convolutional neural network for human–robot interaction. Expert Systems 38:e12490. https://doi.org/10.1111/exsy.12490
30. Liu Z, Hui J (2024) Advancing predictive maintenance: a deep learning approach to sensor and event-log data fusion. Sensor Review 44:563–574. https://doi.org/10.1108/SR-03-2024-0183
31. Joshi G, Tasgaonkar V, Deshpande A, Desai A, Shah B, Kushawaha A, Sukumar A, Kotecha K, Kunder S, Waykole Y, Maheshwari H, Das A, Gupta S, Subudhi A, Jain P, Jain NK, Walambe R, Kotecha K (2025) Multimodal machine learning for deception detection using behavioral and physiological data. Sci Rep 15:8943. https://doi.org/10.1038/s41598-025-92399-6
32. Avioz D, Linker R, Raveh E, Baram S, Paz-Kagan T (2025) Multi-scale remote sensing for sustainable citrus farming: Predicting canopy nitrogen content using UAV-satellite data fusion. Smart Agricultural Technology 11:100906. https://doi.org/10.1016/j.atech.2025.100906
33. Li S, Zhu P, Song N, Li C, Wang J (2025) Regional Soil Moisture Estimation Leveraging Multi-Source Data Fusion and Automated Machine Learning. Remote Sensing 17:837. https://doi.org/10.3390/rs17050837
34. Potamos G, Stavrou E, Stavrou S (2024) Enhancing Maritime Cybersecurity through Operational Technology Sensor Data Fusion: A Comprehensive Survey and Analysis. Sensors 24:3458. https://doi.org/10.3390/s24113458
35. Xu Y, Wu Z, Chanussot J, Comon P, Wei Z (2020) Nonlocal Coupled Tensor CP Decomposition for Hyperspectral and Multispectral Image Fusion. IEEE Transactions on Geoscience and Remote Sensing 58:348–362. https://doi.org/10.1109/TGRS.2019.2936486
36. Zhou G, Luo J, Xu S, Zhang S, Meng S, Xiang K (2021) An EKF-based multiple data fusion for mobile robot indoor localization. AA 41:274–282. https://doi.org/10.1108/AA-12-2020-0199
37. Hechkel W, Helali A (2025) Early detection and classification of Alzheimer’s disease through data fusion of MRI and DTI images using the YOLOv11 neural network. Front Neurosci 19:. https://doi.org/10.3389/fnins.2025.1554015
38. Zhang C, Song W, Lyu Y, Liu Z, Gao X, Hou Z, Wang Z (2025) Dual-branch convolutional neural network with attention modules for LIBS-NIRS data fusion in cement composition quantification. Analytica Chimica Acta 1351:343899. https://doi.org/10.1016/j.aca.2025.343899
39. Cai J, Liu T, Wang T, Feng H, Fang K, Bashir AK, Wang W (2024) Multisource-Fusion-Enhanced Power-Efficient Sustainable Computing for Air Quality Monitoring. IEEE Internet of Things Journal 11:39041–39055. https://doi.org/10.1109/JIOT.2024.3420956
40. Qi L, Hu C, Zhang X, Khosravi MR, Sharma S, Pang S, Wang T (2021) Privacy-Aware Data Fusion and Prediction With Spatial-Temporal Context for Smart City Industrial Environment. IEEE Transactions on Industrial Informatics 17:4159–4167. https://doi.org/10.1109/TII.2020.3012157
41. Pourghebleh B, Hekmati N, Davoudnia Z, Sadeghi M (2022) A roadmap towards energy‐efficient data fusion methods in the Internet of Things. Concurrency and Computation 34:e6959. https://doi.org/10.1002/cpe.6959
42. Zheng J, Lu W, Hu W, Teng J (2025) Real-time estimation method for horizontal displacement of high-rise buildings based on fusion of inclination and acceleration monitoring data. Journal of Building Engineering 103:112083. https://doi.org/10.1016/j.jobe.2025.112083
43. Sun Y, Zuo W, Yun P, Wang H, Liu M (2021) FuseSeg: Semantic Segmentation of Urban Scenes Based on RGB and Thermal Data Fusion. IEEE Transactions on Automation Science and Engineering 18:1000–1011. https://doi.org/10.1109/TASE.2020.2993143
44. Jin G, Wang Y, Li M, Li T, Huang W, Li L, Deng W-W, Ning J (2021) Rapid and real-time detection of black tea fermentation quality by using an inexpensive data fusion system. Food Chemistry 358:129815. https://doi.org/10.1016/j.foodchem.2021.129815
45. Huang Y, Zhao J, Zheng C, Li C, Wang T, Xiao L, Chen Y (2025) The Fermentation Degree Prediction Model for Tieguanyin Oolong Tea Based on Visual and Sensing Technologies. Foods 14:983. https://doi.org/10.3390/foods14060983
46. Shen X, Li H, Shankar A, Viriyasitavat W, Chamola V (2024) Evolutionary computation-based self-supervised learning for image processing: a big data-driven approach to feature extraction and fusion for multispectral object detection. Journal of Big Data 11:130. https://doi.org/10.1186/s40537-024-00988-5
47. Wang M, Yan Z, Wang T, Cai P, Gao S, Zeng Y, Wan C, Wang H, Pan L, Yu J, Pan S, He K, Lu J, Chen X (2020) Gesture recognition using a bioinspired learning architecture that integrates visual data with somatosensory data from stretchable sensors. Nat Electron 3:563–570. https://doi.org/10.1038/s41928-020-0422-z
48. Hall DL, Llinas J (1997) An introduction to multisensor data fusion. Proceedings of the IEEE 85:6–23. https://doi.org/10.1109/5.554205
49. Wang M, Perera C, Jayaraman PP, Zhang M, Strazdins P, Ranjan R (2015) City Data Fusion: Sensor Data Fusion in the Internet of Things. International Journal of Distributed Systems and Technologies (IJDST). https://doi.org/10.4018/IJDST.2016010102
50. Alofi A, Alghamdi A, Alahmadi R, Aljuaid N, M. H (2017) A Review of Data Fusion Techniques. IJCA 167:37–41. https://doi.org/10.5120/ijca2017914318
51. Balazs G, Stechele W (2019) Deep Grid Fusion of Feature-Level Sensor Data with Convolutional Neural Networks. In: 2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE). pp 1–6
52. Wald L (2002) Data fusion: definitions and architectures ; fusion of images of different spatial resolutions. Les Presses de l’École des Mines, Paris
53. Ben Ayed S, Trichili H, Alimi AM (2015) Data fusion architectures: A survey and comparison. In: 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA). pp 277–282
54. Xiao F, Wen J, Pedrycz W, Aritsugi M (2024) Complex Evidence Theory for Multisource Data Fusion. Chin J Inf Fusion 1:134–159. https://doi.org/10.62762/CJIF.2024.999646
55. Foo PH, Ng G-W (2013) High-level information fusion: An overview. Journal of Advances in Information Fusion 8:33–72
56. Mandreoli F, Montangero M (2019) Dealing With Data Heterogeneity in a Data Fusion Perspective. In: Data Handling in Science and Technology. Elsevier, pp 235–270
57. Zhao Y, Li X, Zhou C, Peng H, Zheng Z, Chen J, Ding W (2024) A review of cancer data fusion methods based on deep learning. Information Fusion 108:102361. https://doi.org/10.1016/j.inffus.2024.102361
58. Hassani S, Dackermann U, Mousavi M, Li J (2024) A systematic review of data fusion techniques for optimized structural health monitoring. Information Fusion 103:102136. https://doi.org/10.1016/j.inffus.2023.102136
59. Liu Z, Zhang W, Quek TQS, Lin S (2017) Deep fusion of heterogeneous sensor data. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp 5965–5969
60. Pereira LM, Salazar A, Vergara L (2024) A Comparative Study on Recent Automatic Data Fusion Methods. Computers 13:13. https://doi.org/10.3390/computers13010013
61. Sirasapalli JJ, Malla RM (2023) A deep learning approach to text-based personality prediction using multiple data sources mapping. Neural Comput & Applic 35:20619–20630. https://doi.org/10.1007/s00521-023-08846-w
62. Wald L (1999) Definitions and terms of reference in data fusion. In: ISPRS (ed) International Archives of Photogrammetry and Remote Sensing. ISPRS, Valladolid, Spain, pp 2–6
63. Lau BPL, Marakkalage SH, Zhou Y, Hassan NU, Yuen C, Zhang M, Tan U-X (2019) A survey of data fusion in smart city applications. Information Fusion 52:357–374. https://doi.org/10.1016/j.inffus.2019.05.004
64. Azcarate SM, Ríos-Reina R, Amigo JM, Goicoechea HC (2021) Data handling in data fusion: Methodologies and applications. TrAC Trends in Analytical Chemistry 143:116355. https://doi.org/10.1016/j.trac.2021.116355
65. Kolar P, Benavidez P, Jamshidi M (2020) Survey of Datafusion Techniques for Laser and Vision Based Sensor Integration for Autonomous Navigation. Sensors 20:2180. https://doi.org/10.3390/s20082180
66. Rashinkar P, Krushnasamy VS (2017) An overview of data fusion techniques. In: 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). pp 694–697
67. Gravina R, Alinia P, Ghasemzadeh H, Fortino G (2017) Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges. Information Fusion 35:68–80. https://doi.org/10.1016/j.inffus.2016.09.005
68. Wunsch L, Tenorio CG, Anding K, Golomoz A, Notni G (2024) Data Fusion of RGB and Depth Data with Image Enhancement. J Imaging 10:73. https://doi.org/10.3390/jimaging10030073
69. Kimm H, Guan K, Jiang C, Peng B, Gentry LF, Wilkin SC, Wang S, Cai Y, Bernacchi CJ, Peng J, Luo Y (2020) Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the U.S. Corn Belt using Planet Labs CubeSat and STAIR fusion data. Remote Sensing of Environment 239:111615. https://doi.org/10.1016/j.rse.2019.111615
70. Sadeh Y, Zhu X, Dunkerley D, Walker JP, Zhang Y, Rozenstein O, Manivasagam VS, Chenu K (2021) Fusion of Sentinel-2 and PlanetScope time-series data into daily 3 m surface reflectance and wheat LAI monitoring. International Journal of Applied Earth Observation and Geoinformation 96:102260. https://doi.org/10.1016/j.jag.2020.102260
71. Shao Z, Wu W, Li D (2021) Spatio-temporal-spectral observation model for urban remote sensing. Geo-spatial Information Science 24:372–386. https://doi.org/10.1080/10095020.2020.1864232
72. Wang K, Zhang X, Sun Y, Xu T, Li J, Cao S (2024) YOLO‐DFT: An object detection method based on cloud data fusion and transfer learning for power system equipment maintenance. IET Collab Intel Manufact 6:e12104. https://doi.org/10.1049/cim2.12104
73. Saufi MSRM, Isham MF, Talib MHA, Zain MZMd (2024) Extremely Low-Speed Bearing Fault Diagnosis Based on Raw Signal Fusion and DE-1D-CNN Network. J Vib Eng Technol 12:5935–5951. https://doi.org/10.1007/s42417-023-01228-5
74. Shao G, Chen Y, Wei Y (2020) Deep Fusion for Radar Jamming Signal Classification Based on CNN. IEEE Access 8:117236–117244. https://doi.org/10.1109/ACCESS.2020.3004188
75. Zhang D, Yin C, Zeng J, Yuan X, Zhang P (2020) Combining structured and unstructured data for predictive models: a deep learning approach. BMC Med Inform Decis Mak 20:280. https://doi.org/10.1186/s12911-020-01297-6
76. Zhou X, Liang W, Wang KI-K, Wang H, Yang LT, Jin Q (2020) Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things. IEEE Internet of Things Journal 7:6429–6438. https://doi.org/10.1109/JIOT.2020.2985082
77. Maimaitijiang M, Sagan V, Sidike P, Hartling S, Esposito F, Fritschi FB (2020) Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sensing of Environment 237:111599. https://doi.org/10.1016/j.rse.2019.111599
78. Wan L, Cen H, Zhu J, Zhang J, Zhu Y, Sun D, Du X, Zhai L, Weng H, Li Y, Li X, Bao Y, Shou J, He Y (2020) Grain yield prediction of rice using multi-temporal UAV-based RGB and multispectral images and model transfer – a case study of small farmlands in the South of China. Agricultural and Forest Meteorology 291:108096. https://doi.org/10.1016/j.agrformet.2020.108096
79. Li H, He Y, Xu Q, Deng J, Li W, Wei Y (2022) Detection and segmentation of loess landslides via satellite images: a two-phase framework. Landslides 19:673–686. https://doi.org/10.1007/s10346-021-01789-0
80. Saim AA, Aly M (2025) Enhancing Tree Species Mapping in Arkansas’ Forests Through Machine Learning and Satellite Data Fusion: A Google Earth Engine–Based Approach. J geovis spat anal 9:20. https://doi.org/10.1007/s41651-025-00220-9
81. Huang Z, Ye G, Yang P, Yu W (2025) Application of multi-sensor fusion localization algorithm based on recurrent neural networks. Sci Rep 15:8195. https://doi.org/10.1038/s41598-025-90492-4
82. Zou L, Huang Z, Wang F, Yang Z, Wang G (2021) CMA: Cross-modal attention for 6D object pose estimation. Computers & Graphics 97:139–147. https://doi.org/10.1016/j.cag.2021.04.018
83. Pu C, Liu Y, Lin S, Shi X, Li Z, Huang H (2025) Multimodal Deep Learning for Semisupervised Classification of Hyperspectral and LiDAR Data. IEEE Transactions on Big Data 11:821–834. https://doi.org/10.1109/TBDATA.2024.3433494
84. Liang W, Xiao L, Zhang K, Tang M, He D, Li K-C (2022) Data Fusion Approach for Collaborative Anomaly Intrusion Detection in Blockchain-Based Systems. IEEE Internet of Things Journal 9:14741–14751. https://doi.org/10.1109/JIOT.2021.3053842
85. Jiang J, Liu J, Kadziński M, Liao X (2025) A Bayesian network approach for dynamic behavior analysis: Real-time intention recognition. Information Fusion 118:102873. https://doi.org/10.1016/j.inffus.2024.102873
86. Sidek O, Quadri SA (2012) A review of data fusion models and systems. International Journal of Image and Data Fusion 3:3–21. https://doi.org/10.1080/19479832.2011.645888
87. Huang Z, Ye G, Yang P, Yu W (2025) Application of multi-sensor fusion localization algorithm based on recurrent neural networks. Sci Rep 15:8195. https://doi.org/10.1038/s41598-025-90492-4
88. Wang J, Quasim MT, Yi B (2025) Privacy-preserving heterogeneous multi-modal sensor data fusion via federated learning for smart healthcare. Information Fusion 120:103084. https://doi.org/10.1016/j.inffus.2025.103084
89. Pesti R, Sarcevic P, Odry A (2025) Artificial neural network-based MEMS accelerometer array calibration. Int J Intell Robot Appl. https://doi.org/10.1007/s41315-025-00438-2
90. Wang A, Xu K, Wang W, Wang T, Jia Z, Fan C (2025) Robust SAR-assisted cloud removal via supervised align-guided fusion and bidirectional hybrid reconstruction. International Journal of Digital Earth 18:2472909. https://doi.org/10.1080/17538947.2025.2472909
91. Nan Z, Liu W, Zhu G, Zhao H, Xia W, Lin X, Yang Y (2025) LiDAR-Camera joint obstacle detection algorithm for railway track area. Expert Systems with Applications 275:127089. https://doi.org/10.1016/j.eswa.2025.127089
92. Canalle GK, Salgado AC, Loscio BF (2021) A survey on data fusion: what for? in what form? what is next? J Intell Inf Syst 57:25–50. https://doi.org/10.1007/s10844-020-00627-4
93. Alam F, Mehmood R, Katib I, Albogami NN, Albeshri A (2017) Data Fusion and IoT for Smart Ubiquitous Environments: A Survey. IEEE Access 5:9533–9554. https://doi.org/10.1109/ACCESS.2017.2697839
94. Ounoughi C, Ben Yahia S (2023) Data fusion for ITS: A systematic literature review. Information Fusion 89:267–291. https://doi.org/10.1016/j.inffus.2022.08.016
95. Castanedo F (2013) A Review of Data Fusion Techniques. The Scientific World Journal 2013:704504. https://doi.org/10.1155/2013/704504
96. Xinhan H, Min W (2003) Multi-sensor data fusion structures in autonomous systems: a review. In: Proceedings of the 2003 IEEE International Symposium on Intelligent Control. pp 817–821
97. Bedworth M, O’Brien J (2000) The Omnibus model: a new model of data fusion? IEEE Aerospace and Electronic Systems Magazine 15:30–36. https://doi.org/10.1109/62.839632
98. Veloso M, Bento C, Pereira F (2009) Transportation Systems Working Paper Series Multi-Sensor Data Fusion on Intelligent Transport Systems
99. Chen G, Liu Z, Yu G, Liang J (2021) A New View of Multisensor Data Fusion: Research on Generalized Fusion. Mathematical Problems in Engineering 2021:5471242. https://doi.org/10.1155/2021/5471242
100. Esteban J, Starr A, Willetts R, Hannah P, Bryanston-Cross P (2005) A Review of data fusion models and architectures: towards engineering guidelines. Neural Comput & Applic 14:273–281. https://doi.org/10.1007/s00521-004-0463-7
101. Dasarathy BV (1997) Sensor fusion potential exploitation-innovative architectures and illustrative applications. Proceedings of the IEEE 85:24–38. https://doi.org/10.1109/5.554206
102. Thomopoulos SCA (1990) Sensor integration and data fusion. Journal of Robotic Systems 7:337–372. https://doi.org/10.1002/rob.4620070305
103. Luo RC, Kay MG (1989) Multisensor integration and fusion in intelligent systems. IEEE Transactions on Systems, Man, and Cybernetics 19:901–931. https://doi.org/10.1109/21.44007
104. Meng T, Jing X, Yan Z, Pedrycz W (2020) A survey on machine learning for data fusion. Information Fusion 57:115–129. https://doi.org/10.1016/j.inffus.2019.12.001