Situation analysis for fighter aircraft combat survivability

Fighter pilots operate in environments where an erroneous decision may have fatal consequences. A tactical decision support system (TDSS) could aid the pilots to analyze the situation and make correct decisions. The TDSS can, for instance, highlight important information and suggest suitable actions. The aim of this thesis is to provide a situation analysis model of combat survival that can be utilized in a TDSS. The first part of this thesis describes an analysis of what the model needs to describe and how it can be used. It is concluded that the model should evaluate the outcome of different actions with respect to combat survival. This evaluation can guide the pilot’s decision making, so that actions leading to dangerous situations are avoided. The analysis also highlights the need of handling uncertainties, both measurement precision uncertainty regarding the locations and capabilities of the threats (enemies) and inference uncertainties regarding the prediction of how the threats will act. Finally, arguments for focusing the rest of the work on a single fighter aircraft and threats located on the ground are presented. The second part of the thesis suggests a model, which describes the survivability, i.e., the probability that the aircraft can fly a route without being hit by fire from ground-based threats. Thus, the model represents the inference uncertainty, since it describes the probability of survival. The model’s characteristics are discussed, e.g., that the model is implementable and can be adapted to describe different kinds of ground-based threats. Uncertainty in terms of measurement precision influences the estimate of the survivability. Two different ways of representing this is discussed: calculating the worst case scenario or describing the input as random variables and the resulting survivability as a random variable with a probability distribution. Monte Carlo simulations are used for estimating the distribution for survivability in a few illustrative scenarios, where the input is represented as random variables. The simulations show that when the uncertainty in input is large, the survivability distribution may be both multimodal and mixed. Two uncertainty measures are investigated that condense the information in the distributions into a single value: standard deviation and entropy. The simulations show that both of these measures reflect the uncertainty. Furthermore, the simulations indicate that the uncertainty measures can be used for sensor management, since they point out which information that is the most valuable to gather in order to decrease the uncertainty in the survivability. Finally, directions for future work are suggested. A number of TDSS functions that can be developed based on the model are discussed e.g., warnings, countermeasure management, route-planning and sensor management. The design of these functions could require extending the threat model to incorporate airborne threats and the effects of countermeasures. Further investigations regarding the uncertainty in the model are also suggested