Unmanned Aircraft System Navigation Research Articles

UAS-Borne Radar for Autonomous Navigation and Surveillance Applications

The autonomous navigation of UAS requires, among others, detect-and-avoid capability as a prerequisite for enabling wide-ranging applications, including the transportation of goods and people. This article presents the design, implementation, and experimental results of a UAS-borne radar system detecting drones. The applications of the proposed system include not only detect-and-avoid systems for safe and autonomous navigation of unmanned aircraft systems but also airborne surveillance of malicious drones in controlled or restricted airspace for mitigating security and privacy threats. The system performance in terms of maximum detection range is evaluated through field tests. The experimental results show that the proposed UAS-borne radar can detect a DJI Phantom 4 and a DJI Matrice 600 Pro at a maximum distance of 440 and 500 meters, respectively. The article also provides insights into the system implementation and integration aspects, discusses future research direction, and stresses the need for standardization efforts to benchmark the required performance levels for UAS-borne radars.

Optical navigation of unmanned aircraft systems

Improving machine vision in robotic systems for large-scale sensing in forestry is more relevant than ever. Currently, most UAVs are equipped with various navigation systems (SN), including optical ones. Automatic return-to-point systems are also widely used. These systems are used to facilitate piloting by aircraft, or activated in case of emergency situations. There are also optical tracking systems for the object, which are used mainly in video shooting. Optical navigation systems can be used to more accurately position spaces, and can also be used as a backup navigation system when the main navigation system fails.

Feasibility Analysis of LTE-Based UAS Navigation in Deep Urban Areas and DSRC Augmentation.

The current autonomous navigation of unmanned aircraft systems (UAS) heavily depends on Global Navigation Satellite Systems (GNSS). However, in challenging environments, such as deep urban areas, GNSS signals can be easily interrupted, so that UAS may lose navigation capability at any instant. For urban positioning and navigation, Long Term Evolution (LTE) has been considered a promising signal of opportunity due to its dense network in urban areas, and there has recently been great advancement in LTE positioning technology. However, the current LTE positioning accuracy is found to be insufficient for safe UAS navigation in deep urban areas. This paper evaluates the positioning performance of the current network of LTE base stations in a selected deep urban area and investigates the effectiveness of LTE augmentations using dedicated short range communication (DSRC) transceivers through the optimization of the ground LTE/DSRC network and cooperative positioning among UAS. The analysis results based on simulation using an urban canyon model and signal line of sight propagations show that the addition of four or five DSRC transceivers to the existing LTE base station network could provide better than 4–6 m horizontal positioning accuracy (95%) in the selected urban canyon at a position of 150 ft above the ground, while a dense LTE network alone may result in a 15–20 m horizontal positioning error. Additionally, the simulation results of cooperative positioning with inter-UAS ranging measurements in the DSRC augmented LTE network were shown to provide horizontal positioning accuracy better than 1 m in most flight space, assuming negligible time-synchronization errors in inter-UAS ranging measurements.

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

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.