Machine Learning/Artificial Intelligence for Sensor Data Fusion-Opportunities and Challenges

Erik Blasch, Tien Pham, Chee Yee Chong, Wolfgang Koch, Henry Leung, Dave Braines, Tarek Abdelzaher

Research output: Contribution to journalArticlepeer-review


During Fusion 2019 Conference (, 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.

Original languageEnglish (US)
Article number9475913
Pages (from-to)80-93
Number of pages14
JournalIEEE Aerospace and Electronic Systems Magazine
Issue number7
StatePublished - Jul 1 2021

ASJC Scopus subject areas

  • Aerospace Engineering
  • Space and Planetary Science
  • Electrical and Electronic Engineering


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