Title: Interactive Multiple Model Sensor Analysis for Unmanned Aircraft Systems (UAS) Detect and Avoid (DAA)
Author(s): Adriano Canolla, Michael B. Jamoom, Boris Pervan
Published in: Proceedings of IEEE/ION PLANS 2018
April 23 - 26, 2018
Hyatt Regency Hotel
Monterey, CA
Pages: 757 - 766
Cite this article: Canolla, Adriano, Jamoom, Michael B., Pervan, Boris, "Interactive Multiple Model Sensor Analysis for Unmanned Aircraft Systems (UAS) Detect and Avoid (DAA)," Proceedings of IEEE/ION PLANS 2018, Monterey, CA, April 2018, pp. 757-766.
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Abstract: This research aims at improving Detect and Avoid (DAA) functions in Unmanned Aircraft Systems (UAS) using a Multiple Model Estimation algorithm to track maneuvering intruders. This research builds on previous work that used predefined aircraft encounter trajectories. An established encounter model generates the intruder trajectories while a multiple model algorithm is introduced to improve intruder dynamics estimation. A new method based on the Kalman prediction phase inside the Interactive Multiple Model (IMM) algorithm is presented to estimate time to closest point of approach, horizontal miss distance, and vertical separation. An analysis of the sensor error on the algorithm estimation and the sensor field of regard requirement from the Air-to-Air Radar Minimum Operational Performance Standards (MOPS) is performed. The efficiency of the trajectory estimation has direct implication on the estimation of the intruder trajectory in relation to the own aircraft. The methods described in this research can aid a certification authority in determining if a DAA system is sufficient for safely integration of UAS into the National Airspace System.