Unmanned Aircraft System Navigation in the Urban Environment: A Systems Analysis

Abstract

Unmanned aircraft system navigation in urban environments requires consideration of which combination of sensors can provide the most accurate navigation results in a dynamically changing environment. The traditional Global Positioning System, although useful in open spaces, degrades severely when in urban canyons requiring other complementary sensors to provide position and velocity measurements when necessary. One well-known solution is vision-based sensors that provide measurements through optical flow. Another possibility is the long-term evolution network that is currently used for cellular voice and data transmission as well, as coarse Global-Positioning-System-independent navigation. This paper reviews sensor accuracy and availability as a function of environment characteristics. A simulation framework integrates these different types of sensors to allow for efficient high-level testing of sensor combinations and fusion algorithms. Results show that long-term evolution slightly improves position accuracy unless another exteroceptive position sensor such as vision is available. Sinusoidal trajectories that rise above the urban environment also show increases in accuracy as Global Positioning System navigation becomes available during these short windows.