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Machine Learning/Artificial Intelligence for Sensor Data Fusion–Opportunities and Challenges
Abstract
During Fusion 2019 Conference (https://www.fusion2019.org/program.html), leading experts presented ideas on the historical, contemporary, and future coordination of artificial intelligence/machine learning (AI/ML) with sensor data fusion (SDF). While AI/ML and SDF concepts have had a rich history since the early 1900s—emerging from philosophy and psychology—it was not until the dawn of computers that both AI/ML and SDF researchers initiated discussions on how mathematical techniques could be implemented for real-time analysis. ML, and in particular deep learning, has demonstrated tremendous success in computer vision, natural language understanding, and data analytics. As a result, ML has been proposed as the solution to many problems that inherently include multi-modal data. For example, success in autonomous vehicles has validated the promise of ML with SDF, but additional research is needed to explain, understand, and coordinate heterogeneous data analytics for situation awareness. The panel identified opportunities for merging AI/ML and SDF such as computational efficiency, improved decision making, expanding knowledge, and providing security; while highlighting challenges for multi-domain operations, human-machine teaming, and ethical deployment strategies.
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“The expectation in machine learning is that data fusion efforts will result in an improvement in predictive power17,18 and therefore provide more reliable results in potentially low validity settings19.”
From Multimodal machine learning in precision health: A scoping review (2022) · cites this paper as [19]
In the citing paper
“However, the methodology of combinatory information has drawbacks; it adds complexity to specifying the model and reduces the interpretability of results19,20.”
From Multimodal machine learning in precision health: A scoping review (2022) · cites this paper as [19]
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
(1988) · Cited by 16,582
Image Super-Resolution Using Deep Convolutional Networks
Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105
Nagel, T. (1974) · The Philosophical Review · doi:10.2307/2183914 · Cited by 7,440
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302
(2007) · doi:10.1007/978-3-540-71559-7 · Cited by 780
Evidential Deep Learning to Quantify Classification Uncertainty
Sensoy, M., Kaplan, L., & Kandemir, M. (2018) · arXiv · doi:10.48550/arxiv.1806.01768 · Cited by 694
DARPA's explainable artificial intelligence (XAI) program
Gunning, D. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3308446 · Cited by 546
(2001) · doi:10.1201/9781420038545 · Cited by 397
Lo Piano, S. (2020) · Humanities and Social Sciences Communications · doi:10.1057/s41599-020-0501-9 · Cited by 229
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- blasch, 2019, Artificial intelligence in use by multimodal image fusion, Proc Int Conf Inf Fusion, 1
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Metadata
- Authors
- Blasch, E., Pham, T., Chong, C.-Y., Koch, W., Leung, H., Braines, D., & Abdelzaher, T.
- Affiliations
- United States Air Force Office of Scientific Research (US)
- DEVCOM Army Research Laboratory (US)
- Independent Consultant, San Jose, CA, USA
- University of Bonn (DE)
- University of Calgary (CA)
- IBM (United Kingdom) (GB)
- University of Illinois Urbana-Champaign (US)
- Venue
- IEEE Aerospace and Electronic Systems Magazine
- Published
- 2021-07-01
- Volume
- 36
- Issue
- 7
- Pages
- 80-93
- ISSN
- 0885-8985 (ISSN-L)
- 1557-959X
- OpenAlex
- W3180450071
- Cited by
- 207
- References
- 38
- Citations by year
- 2026: 40 · 2025: 65 · 2024: 47 · 2023: 30 · 2022: 18 · 2021: 6
- FWCI
- 8.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Aerospace and Electronic Systems Magazine (publishedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (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
- sensor fusion, machine learning, artificial intelligence, deep learning, multimodal data, situational awareness, autonomous vehicles, human-machine teaming, multi-domain operations, computational efficiency, decision-making, security (computing)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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