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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Data fusion efforts are expected to result in an improvement in predictive power and therefore provide more reliable results in potentially low validity settings.
“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]
The methodology of combinatory information adds complexity to specifying the model and reduces the interpretability of results.
“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]
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Digital Twins for Cognitive Situation Awareness
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Crop Health Monitoring Through AI-Based Crop Leaf Disease Detection Systems
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Self-Supervised Learning from Unlabeled IoT Data
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Deep Fusion of Multi-Object Densities Using Transformer
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Scalable XAI: Towards Explainable Machine Learning Models in Distributed Systems
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- 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 · 2020: 1
- 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)
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