A Bayesian spatio-temporal aircraft route predictive algorithm with applications to military operations

Charlie S. Harrington; Bryan P. Jonas; Frank Czerniakowski; Nicholas J. Clark

doi:10.1117/12.2585129

12 April 2021 A Bayesian spatio-temporal aircraft route predictive algorithm with applications to military operations

Charlie S. Harrington, Bryan P. Jonas, Frank Czerniakowski, Nicholas J. Clark

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 117461B (2021) https://doi.org/10.1117/12.2585129
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

The International Civil Aviation Organization’s 2020 mandate for all aircraft to have Automatic Dependent Surveillance-Broadcast (ADS-B) equipment installed poses a unique challenge to the military. With the use of cheap sensors, adversaries now can collect real-time data on flights transporting troops. In this paper, we outline a statistical model that can be leveraged both defensively and offensively. Our model, a time-series spatial process model with a first-order Markov assumption, can be used to develop a flight path that could help a military aircraft seem like ordinary air traffic to an observer. This would prevent adversaries from identifying and tracking these flights as easily. The model can also be used to predict the destination of flights based on their observed location. These real-time flight predictions use Bayesian updating to improve their accuracy as more information about the flight is observed.

Conference Presentation

Citation Download Citation

Charlie S. Harrington, Bryan P. Jonas, Frank Czerniakowski, and Nicholas J. Clark "A Bayesian spatio-temporal aircraft route predictive algorithm with applications to military operations", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117461B (12 April 2021); https://doi.org/10.1117/12.2585129

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