Automated radar heading calibration with collaborating participants and multi-sensor fusion

As unmanned aerial systems (UAS) become more prolific so will the use of radar systems for tracking UAS in the national airspace system (NAS). The future of Urban Air Mobility (UAM) involves large amounts of UAS operating autonomously and simultaneously in urban environments for the purpose of passenger or cargo transportation. Radar detection of UAS in an urban environment can be hindered by line of sight (LOS) blockage by large buildings thus necessitating many surveillance devices to gain full coverage. Currently, UAM is still in development by the Federal Aviation Administration (FAA) and other airspace partners and many cities do not have the need or resources for full radar coverage. Due to the high cost of individual radar systems and the quantity needed to cover urban areas it is currently not practical to have full radar coverage of an area at all times. Permanent stationary radar systems are generally calibrated once with occasional adjustments and low time constraints. Temporary radar systems must be calibrated and aligned before each mission deployment and often under short time constraints. Temporarily stationed mobile radar platforms will be utilized for specific targeted mission objectives until a more permanent solution is developed and implemented. In the case of disaster response or search and rescue, a temporary radar system needs to be quickly deployed. The key abilities required by a temporary radar system are accurate track position reporting and quick setup and breakdown. One of the bottlenecks to quick setup is heading calibration. Radar antenna alignment is crucial to the performance of the system and its ability to accurately determine the position of a tracked object. In this paper, we implement and compare multiple methods of radar heading calibration for accuracy and speed including manually with a handheld compass, manually with a web based heading helper tool, manually with a custom dual Real Time Kinetic (RTK) GPS alignment tool, and automated with a collaborating Radar Cross Section (RCS) device. For RCS devices we use a marine radar reflector and attached RTK GPS when unable to fly and an unmanned aerial vehicle (UAV) also with RTK GPS when able to fly. By leveraging our experience working with UAVs and Radars we show a method to autocalibrate the positioning sensors by using multisensory fusion and collaborating participants, thus reducing the amount of setup time, and increasing the accuracy of the system.Computing SciencesCollege of Science and Engineerin