Dynamic data driven analytics for multi-domain environments

Erik Blasch; Jonathan Ashdown; Fotis Kopsaftopoulos; Carlos Varela; Richard Newkirk

doi:10.1117/12.2519210

10 May 2019 Dynamic data driven analytics for multi-domain environments

Erik Blasch, Jonathan Ashdown, Fotis Kopsaftopoulos, Carlos Varela, Richard Newkirk

Proceedings Volume 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications; 1100604 (2019) https://doi.org/10.1117/12.2519210
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Recent trends in artificial intelligence and machine learning (AI/ML), dynamic data driven application systems (DDDAS), and cloud computing provide opportunities for enhancing multidomain systems performance. The DDDAS framework utilizes models, measurements, and computation to enhance real-time sensing, performance, and analysis. One example the represents a multi-domain scenario is “fly-by-feel” avionics systems that can support autonomous operations. A "fly-by-feel" system measures the aerodynamic forces (wind, pressure, temperature) for physics-based adaptive flight control to increase maneuverability, safety and fuel efficiency. This paper presents a multidomain approach that identifies safe flight operation platform position needs from which models, data, and information are invoked for effective multidomain control. Concepts are presented to demonstrate the DDDAS approach for enhanced multi-domain coordination bringing together modeling (data at rest), control (data in motion) and command (data in use).

Conference Presentation

Citation Download Citation

Erik Blasch, Jonathan Ashdown, Fotis Kopsaftopoulos, Carlos Varela, and Richard Newkirk "Dynamic data driven analytics for multi-domain environments", Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 1100604 (10 May 2019); https://doi.org/10.1117/12.2519210

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