<p>Unmanned aerial systems (UASs) are at the intersection of robotics and aerospace re-<br>
search. Their rise in popularity spurred the growth of interest in urban air mobility (UAM)<br>
across the world. UAM promises the next generation of transportation and logistics to be<br>
handled by UASs that operate closer to where people live and work. Therefore safety and<br>
security of UASs are paramount for UAM operations. Monitoring UAS traffic is especially<br>
challenging in urban canyon environments where traditional radar systems used for air traffic<br>
control (ATC) are limited by their line of sight (LOS).<br>
This thesis explores the design and preliminary results of a target tracking system for<br>
urban canyon environments based on a network of camera nodes. A network of stationary<br>
camera nodes can be deployed on a large scale to overcome the LOS issue in radar systems<br>
as well as cover considerable urban airspace. A camera node consists of a camera sensor, a<br>
beacon, a real-time kinematic (RTK) global navigation satellite system (GNSS) receiver, and<br>
an edge computing device. By leveraging high-precision RTK GNSS receivers and beacons,<br>
an automatic calibration process of the proposed system is devised to simplify the time-<br>
consuming and tedious calibration of a traditional camera network present in motion capture<br>
(MoCap) systems. Through edge computing devices, the tracking system combines machine<br>
learning techniques and motion detection as hybrid measurement modes for potential targets.<br>
Then particle filters are used to estimate target tracks in real-time within the airspace from<br>
measurements obtained by the camera nodes. Simulation in a 40m×40m×15m tracking<br>
volume shows an estimation error within 0.5m when tracking multiple targets. Moreover,<br>
a scaled down physical test with off-the-shelf camera hardware is able to achieve tracking<br>
error within 0.3m on a micro-UAS in real time.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/20388318 |
| Date | 28 July 2022 |
| Creators | Zhanpeng Yang (13164648) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/UNMANNED_AERIAL_SYSTEM_TRACKING_IN_URBAN_CANYON_ENVIRONMENTS_USING_EXTERNAL_VISION/20388318 |
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