Date of Award

January 2017

Document Type


Degree Name

Master of Science (MS)


Mechanical Engineering

First Advisor

William Semke


An evaluation of the radar systems in the Red River Valley of North Dakota (ND) and its surrounding areas for its ability to provide Detect and Avoid (DAA) capabilities for manned and unmanned aircraft systems (UAS) was performed. Additionally, the data was analyzed for its feasibility to be used in autonomous Air Traffic Control (ATC) systems in the future. With the almost certain increase in airspace congestion over the coming years, the need for a robust and accurate radar system is crucial. This study focused on the Airport Surveillance Radar (ASR) at Fargo, ND and the Air Route Surveillance Radar at Finley, ND. Each of these radar sites contain primary and secondary radars.

It was found that both locations exhibit data anomalies, such as: drop outs, altitude outliers, prolonged altitude failures, repeated data, and multiple aircraft with the same identification number (ID) number. Four weeks of data provided by Harris Corporation throughout the year were analyzed using a MATLAB algorithm developed to identify the data anomalies. The results showed Fargo intercepts on average 450 aircraft, while Finley intercepts 1274 aircraft. Of these aircraft an average of 34% experienced drop outs at Fargo and 69% at Finley. With the average drop out at Fargo of 23.58 seconds and 42.45 seconds at Finley, and several lasting more than several minutes, it shows these data anomalies can occur for an extended period of time. Between 1% to 26% aircraft experienced the other data anomalies, depending on the type of data anomaly and location. When aircraft were near airports or the edge of the effective radar radius, the largest proportion of data anomalies were experienced. It was also discovered that drop outs, altitude outliers, andrepeated data are radar induced errors, while prolonged altitude failures and multiple aircraft with the same ID are transponder induced errors. The risk associated with each data anomaly, by looking at the severity of the event and the occurrence was also produced. The findings from this report will provide meaningful data and likely influence the development of UAS DAA logic and the logic behind autonomous ATC systems.