Date of Award

4-2019

Degree Type

Thesis

Degree Name

Engineering (M.S.E.)

Department

School of Engineering

First Advisor

Nicholas A. Baine, Ph. D., P.E.,

Second Advisor

Bruce Dunne, Ph. D.

Third Advisor

David Zeitler, Ph.D.

Academic Year

2018/2019

Abstract

This paper develops a non-precision, three-dimensional, geodetic positioning algorithm for airborne vehicles. The algorithm leverages the proliferation of Automatic Dependent Surveillance – Broadcast (ADS-B) equipped aircraft, utilizing them as airborne navigation aids to generate an RF Angle-of-Arrival (AOA) and Angle-of-Elevation (AOE) based geodetic position. The resulting geodetic position can serve as a redundant navigation system for use during locally limited Global Navigation Satellite System (GNSS) availability, be used to validate on-board satellite navigation systems in an effort to detect local spoofing attempts, and be used to validate ADS-B position reports.

The navigation algorithm is an implementation of an Extended Kalman Filter (EKF) that is loosely based on Simultaneous Localization and Mapping (SLAM), in that it tracks ADS-B capable aircraft while simultaneously determining the geodetic position and velocity of the host vehicle. Unlike SLAM, where the absolute location – latitude/longitude – of the landmarks is unknown and must be estimated as the vehicle encounters them, the absolute position of the airborne navigation aids is typically well-known and periodically reported in the ADS-B data set. Because the absolute position of the navigation aids are known, the resulting host vehicle position will also be an absolute, rather than a relative position. Secondarily, the continuous tracking of the airborne navigation aids allows reported ADS-B positions to be validated against the estimated navigation aid position; thereby, concurrently accomplishing ADS-B validation and host vehicle geolocation.

This research has demonstrated through a series of simulated Monte-Carlo tests that the algorithm is capable of generating valid position estimates, along with a reliable estimate of its accuracy, across a variety of anticipated input conditions. With multiple GNSS quality navigation aids available, mean position errors below 225 meters were observed. As the quality of the navigation aids decreased, so too did the accuracy of the algorithm. Utilizing navigation aids with an accuracy of 4 nautical miles (95% containment) resulted in mean position errors on the order of 0.75 nautical miles. These results demonstrate that the method is feasible, and even under worst case conditions, the accuracy of the position estimate generated by the algorithm was sufficient to allow an aircraft to navigate to its destination.

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