STK is deterministic, not probabilistic, so what you define in STK is what it models. With that being said, there are many points in STK where you can model imperfect systems. For example, with certain sensors, you can model jitter. You can have a position and velocity covariance on a satellite. You can model losses and efficiencies in communications systems. This is not uncertainty, but it is tangentially related.
There are two main ways that AGI recommends for modeling uncertainty. One of them is using a script that evaluates a system over a range of inputs. You can also do this analysis with tools like
ModelCenter and
Analyzer. This is great when you are trying to derive requirements, because you can see where limits on a system will be based on the uncertainty the system can handle. For example, if you have a radar targeted at an aircraft, you could run a Monte Carlo analysis where you vary the orientation off of boresight and see where the signal to noise ratio drops below an acceptable level.
Another way to model uncertainty, specifically position uncertainty, is with volumetric analysis. If you have some set system requirements, perhaps in your communications systems, but are uncertain of where other objects are in the scenario, you can evaluate your ability to detect these objects by creating volume grids. For example, if you have a ship with a receiver, you can create a volume around it to represent the airspace. You can then calculate the received isotropic power throughout the volume to give you an idea of where you will be able to detect objects of interest. Consider trying out the volumetric tutorial
Using the Spatial Analysis Tool with the Volumetric Object.
