UAVs provide exceptional capabilities and a myriad of potential mission sets, but the ability to disguise where the aircraft takes off and lands would expansively advance the abilities of UAVs. This thesis describes the development of a nonlinear estimation algorithm to predict the terminal location of an aircraft and a trajectory optimization strategy to mitigate the algorithm's success. Vehicle paths are generated using a matrix-based quadratic trajectory computation method. The paths are then tracked by recursively updating time-based observations of vehicle position using Bayesian filtering. The KL divergence is used to compare the probability density of aircraft termination to a normal distribution around the true terminal location. Results show that the optimal conditions to obfuscate path include waypoints at or beyond the vehicle terminal location, variations in velocity throughout the time of flight, and the minimal use of an aircraft's maximum potential time of flight.

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