The aim of situation analysis is to assess the relevant objects in the surroundings and interpret their relations and their impact in order for a decision maker to achieve situation awareness and be able to make suitable decisions. However, the information regarding the relevant objects is typically uncertain, which will induce uncertainty in the result from the situation analysis. If the kinematic states of the objects are estimated with a tracking filter, the estimates can be considered as random variables. Furthermore, the situation analysis algorithm is a function of these estimates entailing that the result from the situation analysis is random variable. This paper studies the fighter aircraft domain and a situation analysis algorithm that calculates the combat survivability, i.e., the probability that the aircraft can a fly a route inside hostile territory without getting hit by enemy fire. The survivability of different routes can be compared in order to decide where to fly. However, the uncertainties regarding the threats' positions imply that the survivability is uncertain and can be desribed as a random variable with a distribution. The unscented transform (UT) is here used for calculating the mean and standard deviation (std) of the survivability in a few scenarios with threats located on the ground. Simulations show that the position uncertainties affect both the mean and std of the survivability and that UT gives similar estimates as a Monte Carlo (MC) approach. UT therefore seems to be a promising approach for calculating the uncertainty in the survivability, which is more computational efficient than MC.