  This paper explores how a flying drone can autonomously navigate while
constructing a narrowband radio map for signal localization. As flying drones
become more ubiquitous, their wireless signals will necessitate new wireless
technologies and algorithms to provide robust radio infrastructure while
preserving radio spectrum usage. A potential solution for this spectrum-sharing
localization challenge is to limit the bandwidth of any transmitter beacon.
However, location signaling with a narrow bandwidth necessitates improving a
wireless aerial system's ability to filter a noisy signal, estimate the
transmitter's location, and self-pilot to improve the location estimate. By
showing results through simulation, emulation, and a final drone flight
experiment, this work provides an algorithm using a Gaussian process for radio
signal estimation and Bayesian optimization for drone automatic guidance. This
research supports advanced radio and aerial robotics applications in critical
areas such as search-and-rescue, last-mile delivery, and large-scale platform
digital twin development.
