Multichannel Sense and Avoid Radar for Small UAVs

This dissertation investigates the feasibility of creating a multichannel sense and avoid radar system for small fixed-wing UAVs (also known as sUAS or drones). The target sUAS is a 40% Yak-54 remote controlled aircraft with a typical payload of 10 lbs. Small UAS’s such as these are increasing in popularity for both personal, commercial, and government use including precision agriculture, infrastructure monitoring, and assisting first response. However, due to their lack of situation awareness, the FAA has placed strict regulations on their operation limiting their use on both the civil and government sides across the U.S. This miniature radar system is intended to provide these sUAS with target detection, tracking, and 3-D location and velocity information on potential non-cooperative hazards, primarily focusing on general aviation (GA) aircraft. The resulting FMCW miniature radar system has a size weight and power (SWaP) that is suitable for installing onboard the 40% Yak-54 UAS with the exception of replacing a TX power amplifier and has demonstrated, through measuring moving cars, that it is capable of target detection using a 2-D FFT processing algorithm and a constant false alarm rate (CFAR) detector. Tracking of the target was performed using the range-Doppler relationship of targets in the resulting radar image. The target’s angular information in the form of target echo angle of arrival (AoA, needed for location estimation) was estimated using interferometry. While the angular estimations were in the right direction, their uncertainties resulting in significant fluctuations in estimated target XYZ position and XYZ velocities. It was observed that in the near term, averaging the AoA (which changes relatively slowly for steady flight) is a way to reduce this uncertainly. In the future, the radar system needs to be upgraded so that it can provide the ideal 10-Hz update rate which will also provide sufficient data for more complex target AoA detection algorithms.

Grantor dc:publisher

University of Kansas

Year dc:date.issued

2016

Author dc:creator

• Shi, Lei

Advisor dc:contributor.advisor

• Allen, Christopher

• Electrical engineering
• collision avoidance
• Radar
• UAS

Statement dc:rights

• Copyright held by the author.

Language dc:language.iso

en

Dc Identifier Other

http://dissertations.umi.com/ku:14743

OAI identifier oai:identifier

oai:kuscholarworks.ku.edu:1808/25766