Perspectives on AI-driven systems for multiple sensor data fusion
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Wolfgang Koch
Wolfgang Koch (Fellow, IEEE) earned his PhD in theoretical physics at RWTH Aachen and his habilitation in computer science at the University of Bonn. There, he teaches on signal processing and sensor data fusion, and resources management. In these fields he has published a monograph, edited handbooks, and wrote numerous technical articles. For many years, he is working for the Bundeswehr and the German defence and industry. His work comprises all aspects of Intelligence, Surveillance, and Reconnaissance ISR, Electronic/Navigation Warfare, resources management, and Manned-Unmanned Teaming. Being the Chief Scientist of Fraunhofer FKIE, he co-ordinates on a broader scale R & D activities related to digitalization in the defence and security domain. At the international level, he is active in the International Information Fusion Society ISIF, the IEEE Aerospace and Electronics Systems Society AESS, and the NATO Science and Technology Organization STO.
Abstract
Artificially intelligent automation has not only impact on sensor technologies, but also on comprehensive multiple sensor systems for assisting situational awareness and decision-making. This is particularly true for integrated Manned-unManned-Teaming (MuM-T), for example. From a systems engineering perspective which does not exclude applications in the defence domain, three tasks need to be fulfilled: (1) Design artificially intelligent automation in a way that human beings are mentally and emotionally able to master each situation. (2) Identify technical design principles to facilitate the responsible use of AI in ethically critical applications. (3) Guarantee that human decision makers always have full superiority of information, decision-making, and options of action. Our discussion of AI-driven systems for multiple sensor data fusion results in recommendations and key results. We are addressing the algorithms needed, the data to be processed, the programming skills required, the computing devices to be used, the inevitable anthropocentric design, the reviewing of R & D efforts necessary, and the integration of different dimensions in a systems-of-systems point of view.
About the author
Wolfgang Koch (Fellow, IEEE) earned his PhD in theoretical physics at RWTH Aachen and his habilitation in computer science at the University of Bonn. There, he teaches on signal processing and sensor data fusion, and resources management. In these fields he has published a monograph, edited handbooks, and wrote numerous technical articles. For many years, he is working for the Bundeswehr and the German defence and industry. His work comprises all aspects of Intelligence, Surveillance, and Reconnaissance ISR, Electronic/Navigation Warfare, resources management, and Manned-Unmanned Teaming. Being the Chief Scientist of Fraunhofer FKIE, he co-ordinates on a broader scale R & D activities related to digitalization in the defence and security domain. At the international level, he is active in the International Information Fusion Society ISIF, the IEEE Aerospace and Electronics Systems Society AESS, and the NATO Science and Technology Organization STO.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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© 2023 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Editorial
- Measurement systems and sensors with cognitive features II
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- Uncertainty-aware automated machine learning toolbox
- Explainable AI for sensor-based sorting systems
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- Perspectives on AI-driven systems for multiple sensor data fusion
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Artikel in diesem Heft
- Frontmatter
- Editorial
- Measurement systems and sensors with cognitive features II
- Research Articles
- Uncertainty-aware automated machine learning toolbox
- Explainable AI for sensor-based sorting systems
- Review Article
- Perspectives on AI-driven systems for multiple sensor data fusion
- Research Articles
- A collection of machine learning assisted distributed fiber optic sensors for infrastructure monitoring
- Automated end-of-line quality assurance with visual inspection and convolutional neural networks
- Review Article
- Automotive mass production of camera systems: Linking image quality to AI performance
