Integrating Nonparametric Learning with Path Planning for Data-Ferry Communications

Abstract

In a data-ferrying unmanned aircraft system, ferrying performance requires knowledge of the communication environment through which the aircraft moves. This work integrates ferry planning with opportunistically learning the radio environment through the use of a Gaussian process. The unmanned aircraft’s trajectory is initially optimized with an a priori model. After flying one circuit of the closed trajectory, radio-frequency variations observed by the ferry are used to train a Gaussian process and improve the model of the environment. This iterative ferry-and-learn system is analyzed through a simulation study, showing ferry performance improves rapidly. The ferry achieves 80% of optimal within four iterations and 93% after nine iterations, as the Gaussian process is able to converge quickly to the true radio-frequency environment. This work further compares the Gaussian process to common parameter-based estimation methods through two extremes of radio-frequency environments. The nonparametric nature of ...