COMPUTATIONALLY EFFICIENT ASSESMENT OF FIGHTER AIRCRAFT MISSION SURVIVABILITY WITH PROBABILISTIC GRAPHICAL MODELS

  • Nazım Kemal Üre
Keywords: Fighter Aircraft Survivability, Missions Survivability, Probabilistic Threat Models, Probabilistic Graphical Models

Abstract

This paper proposes a probabilistic model for assessment of fighter aircraft mission survivability. Mission survivability analysis is a critical phase for both design of the fighter aircraft and evaluation of its performance. The standard deterministic performance metrics such as aircraft agility and manoeuvrability are not sufficient to measure mission survivability, since the probability of aircraft surviving the missions depends heavily on lethality and position of threats, such as surface to air missile systems. Since the dynamics and parameters of the threats are mostly uncertain, previous works proposed several different probabilistic models for modelling the mission survivability of fighter aircraft under uncertain threats. However, most of the existing models either oversimplify the problem or leads to in complicated high dimensional probability distributions, which are unfeasible for evaluation of large-scale missions. In this study, we fuse the threat and sensor models from several existing works and show that the mission survivability can be modelled as a probabilistic graphical model, which enables rapid sampling and Monte Carlo evaluation of survivability, even on large-scale missions that involve many different threat and sensor networks. In addition, we show that graphical representation can also be used for addressing the inverse problem of determining required aircraft performance parameters for a specified survivability rate.

References

[1] Paterson, John. "Overview of low observable technology and its effects on combat aircraft survivability." Journal of Aircraft 36.2 (1999): 380-388.
[2] Ball, Robert E. The fundamentals of aircraft combat survivability analysis and design. AIAA (American Institute of Aeronautics & Astronautics), 2003.
[3] Przemieniecki, John S. Mathematical methods in defense analyses. Aiaa, 2000.
[4] Koller, Daphne, and Nir Friedman. Probabilistic graphical models: principles and techniques. MIT press, 2009.
[5] Goossens, A. A. H. E. "Development and evaluation of level 3 situation awareness support functions for a UAV operator station." Digital Avionics Systems Conference, 2004. DASC 04. The 23rd. Vol. 2. IEEE, 2004.
[6] Mattei, Massimiliano, and Luciano Blasi. "Smooth flight trajectory planning in the presence of no-fly zones and obstacles." Journal of guidance, control, and dynamics 33.2 (2010): 454-462.
[7] Beard, Randal W., et al. "Coordinated target assignment and intercept for unmanned air vehicles." IEEE transactions on robotics and automation 18.6 (2002): 911-922.
[8] Chandler, Phillip, Steven Rasmussen, and Meir Pachter. "UAV cooperative path planning." AIAA Guidance, Navigation, and Control Conference and Exhibit. 2000.
[9] Jun, Myungsoo, and Raffaello D’Andrea. "Path planning for unmanned aerial vehicles in uncertain and adversarial environments." Cooperative control: models, applications and algorithms. Springer US, 2003. 95-110.
[10] Koyuncu, Emre, and Gokhan Inalhan. "Exploiting delayed and imperfect information for generating approximate uav target interception strategy." Journal of Intelligent & Robotic Systems 69.1-4 (2013): 313-329.
[11] Orgen, P., and Maja Winstrand. "Combining path planning and target assignment to minimize risk in a SEAD mission." AIAA Guidance, Navigation, and Control Conf. 2005.
[12] Theunissen, Erik, Fok Bolderheij, and G. J. M. Koeners. "Integration of threat information into the route (re-) planning task." 24th Digital Avionics Systems Conference. Vol. 2. IEEE, 2005.
[13] Kabamba, Pierre T., Semyon M. Meerkov, and Frederick H. Zeitz. "Optimal path planning for unmanned combat aerial vehicles to defeat radar tracking." Journal of Guidance, Control, and Dynamics 29.2 (2006): 279-288.
[14] Erlandsson, Tina, and Lars Niklasson. "Automatic evaluation of air mission routes with respect to combat survival." Information Fusion 20 (2014): 88-98.
[15] Randleff, Lars Rosenberg. Decision support system for fighter pilots. DTU Informatics, 2008.
[16] Bishop, Christopher M. "Pattern recognition." Machine Learning 128 (2006).
[17] Jensen, Finn V. An introduction to Bayesian networks. Vol. 210. London: UCL press, 1996.
[18] Beal, Matthew James. Variational algorithms for approximate Bayesian inference. London: University of London, 2003.
[19] Andrieu, Christophe, et al. "An introduction to MCMC for machine learning." Machine learning 50.1-2 (2003): 5-43.
[20] Liefer, Randall K., et al. "Fighter agility metrics, research and test." Journal of Aircraft 29.3 (1992): 452-457.
[21] Ure, N. Kemal, and Gokhan Inalhan. "Autonomous Control of Unmanned Combat Air Vehicles: Design of a Multimodal Control and Flight Planning Framework for Agile Maneuvering." IEEE Control Systems 32.5 (2012): 74-95.
[22] Ure, N. Kemal, and Gokhan Inalhan. "Predictive missile guidance for agile maneuvering targets with stochastic hybrid dynamics." Aerospace Conference, 2016 IEEE. IEEE, 2016.
Published
2017-09-14
How to Cite
[1]
N. Üre, “COMPUTATIONALLY EFFICIENT ASSESMENT OF FIGHTER AIRCRAFT MISSION SURVIVABILITY WITH PROBABILISTIC GRAPHICAL MODELS”, JAST, vol. 10, no. 1, pp. 97-104, Sep. 2017.
Section
Articles