The loss of Positioning, Timing, and Navigation capabilities from current GPS satellites would negatively impact our modern society. In case of such events, the position of stars could potentially provide an alternative to a deployed GPS. Celestial-based alternative PNT methods would use the star positions in the sky to determine an observer's location. Shown in the image is a wide-field image of stars identified by their astrometry (displayed on a logarithmic scale) for a specific spectral type An integral part of the work at EOSL focuses on developing a high fidelity simulation capability, the Dynamic Model Integration and Simulation Engine (DMISE), for characterizing resident space objects orbiting the Earth. The simulation capability in DMISE addresses both narrow (several arcminutes) and wide-field (50-110 degrees) imaging of stars and satellites in the night sky for a range of observing conditions. This URIP proposes to design and build a celestial PNT simulation algorithm that estimates the astrometry of stars in simulated imagery from DMISE and then uses this information to calculate an observer's location. Part of the simulation will model how the observer location, time of day, and detection noise in the imagery affect position estimates. In addition to the simulation, the student will collect and process wide-field imagery and use it to verify and validate their developed PNT algorithm.