A Review of Data Fusion Techniques

By Federico Castanedo. 2013

952 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Signal Processing

Citations in the corpus

A survey of data fusion in smart city applications (2019, methodology): “We use smart city single perspective data fusion review papers [19], [26] and non-smart city data fusion classiﬁcation papers [18], [39]– [41] as references.”
Cooperative Perception for 3D Object Detection in Driving Scenarios Using Infrastructure Sensors (2019, methodology): “For this reason the late fusion scheme is an example of high level fusion (object level), while early fusion is an example of low level fusion (signal level) [13].”
Multi-Factor Authentication: A Survey (2018, methodology): “In this work, we consider a more general case of the probabilistic decision-making methodology, while a combination of the measurement results for the individual sensors is made similarly to the previous works by using the Bayes estimator [218].”
Visualizing Big Data with augmented and virtual reality: challenges and research agenda (2015, background): “Data fusion combines multiple sources to obtain improved information that is more relevant or less expensive and has higher quality [94].”
Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review (2021, background): “Readers interested in detailed discussions about sensor fusion techniques and algorithms for various application ranging from environmental perception, including 2D or 3D object detection and lane tracking, to localization and mapping are recommended to refer to [16,26,181,187-199].”
A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research (2019, background): “In a review carried out by [30], it is possible to find an extensive classification of fusion methods according to different criteria such as: (1) relations between the input data sources; (2) input/output data types and their nature; (3) abstraction level of the employed data; (4) different data fu”
Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach (2016, methodology): “One of the most well-known data fusion classifications was provided by Dasarathy [36].”
Multimodal deep learning for biomedical data fusion: a review (2022, background): “More specifically, the advantages of data fusion can be categorized as complementary, redundant and cooperative features [4, 5], though these are not mutually exclusive.”
Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey (2020, background): “Considering the second question about abstraction level, the answers offered by [126] are: • Low level (i.”
The Architecture of Dynamic Capability Research Identifying the Building Blocks of a Configurational Approach (2016, neutral): “This is an unexplored area in the social sciences but examples exist in other domains and some scholars have attempted to apply fusion models more broadly (see e.g., Castanedo, 2013).”
Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review (2020, neutral): “Utilized to enhance data imputation using a statistical model to model the sensory information [64,67] Can handle ; Tolerant[68,69] Limited to linear estimators; Computation complexity is high [65] Estimation Low [70] Based on probability representation for the sensory information [64] Uncertainty…”