Multisensor data fusion: A review of the state-of-the-art

By Bahador Khaleghi, Alaa Khamis, F. Karray, Saiedeh Navabzadeh Razavi. 2013

1,883 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Media Technology, Signal Processing

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Citations in the corpus

A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion (2019, methodology): “The literatures [15-18] have provided deep discussion and introduction to these methods.”
A survey of data fusion in smart city applications (2019, background): “Objectives and Topics Covered Khaledgi et al. [17] Provides insights on the different types of data fusion techniques by exploring their concept, beneﬁts, and challenges.”
Multimodal information fusion for urban scene understanding (2016, background): “It is also very well suited for information fusion [18].”
Machine Learning in Seismology: Turning Data into Insights (2018, methodology): “introducing unnecessary complexity (Khaleghi et al., 2013).”
Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers? (2017, background): “, integrating features from different data types in recommendation systems [21], sensor fusion [22], dimensionality reduction during feature fusion [14], and controlling data quality during data fusion [11].”
Artificial intelligence for fault diagnosis of rotating machinery: A review (2018, background): “[28,29] have summarized the state of the art of data fusion methods, as well as their beneﬁts and challenging aspects.”
Potential Cyberattacks on Automated Vehicles (2015, background): “Moreover, attack trees do not specifically integrate the detection part, which is necessary to assess the probability of success of the attack.”
An Adaptive Bayesian System for Context-Aware Data Fusion in Smart Environments (2017, background): “data so as to improve confidence and reliability of the reasoning process [6].”
A Survey on Deep Learning for Multimodal Data Fusion (2020, background): “The reasonable fusion of these multimodal data can help us better understand the event of interest, especially when one modality is incomplete (Khaleghi, Khamis, Karray, & Razavi, 2013; Lahat, Adali, & Jutten, 2015).”
From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices (2016, methodology): “The definition of the categories of the data fusion methods has already been been discussed by several authors [85–87].”
A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems (2016, methodology): “A large number of data fusion techniques have been developed, as reviewed by Pohl and van Genderen (1998), Li (2010), Zhang (2010), and Khaleghi et al. (2013).”
Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach (2016, background): “Data fusion deals with the synergistic combination of data and information from one or multiple sources to provide improved information with higher quality and reliability [35].”
Fault detection in multi-sensor networks based on multivariate time-series models and orthogonal transformations (2014, methodology): “imperfect data, inconsistent or uncertain data due to noise levels) — for a recent survey of widely used (data) fusion methods see [7].”
Opinion Mining and Information Fusion: A survey (2016, background): “Furthermore, Khalegi et al. [10] introduce the work done by Kokar et al. [110] as the first step towards a formalization of the theory of information fusion.”
Deep Multimodal Learning: A Survey on Recent Advances and Trends (2017, background): “Techniques for multimodal data fusion, which cover different application domains, have long been investigated by the research community [3], [4].”
A review of natural language processing techniques for opinion mining systems (2017, background): “It is now widely used in various areas including sensor networks, video and image processing, and intelligent system design [13].”