Interactive multiple model sensor analysis for Unmanned Aircraft Systems (UAS) Detect and Avoid (DAA)

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.

[1]  Mykel J. Kochenderfer,et al.  Uncorrelated Encounter Model of the National Airspace System, Version 1.0 , 2008 .

[2]  Boris Pervan,et al.  Correction: Interactive Multiple Model Hazard States Prediction for Unmanned Aircraft Systems (UAS) Detect and Avoid (DAA) , 2018 .

[3]  Mathieu Joerger,et al.  Sense and Avoid for Unmanned Aircraft Systems: Ensuring Integrity and Continuity for Three Dimensional Intruder Trajectories , 2015 .

[4]  Rodney E. Cole,et al.  Defining Well Clear for Unmanned Aircraft Systems , 2015 .

[5]  Andrew D Zeitlin,et al.  Sense & Avoid capability development challenges , 2010, IEEE Aerospace and Electronic Systems Magazine.

[6]  Boris Pervan,et al.  Unmanned Aircraft Systems Detect and Avoid Sensor Hybrid Estimation Error Analysis , 2017 .

[7]  Michael B. Jamoom,et al.  Unmanned aircraft system sense and avoid integrity and continuity , 2016 .

[8]  Youmin Zhang,et al.  Sense and avoid technologies with applications to unmanned aircraft systems: Review and prospects , 2015 .

[9]  X. R. Li,et al.  Performance Prediction of the Interacting Multiple Model Algorithm , 1992 .

[10]  Giancarmine Fasano,et al.  Sense and avoid for unmanned aircraft systems , 2016, IEEE Aerospace and Electronic Systems Magazine.

[11]  Amir Averbuch,et al.  Interacting Multiple Model Methods in Target Tracking: A Survey , 1988 .

[12]  X. R. Li,et al.  Performance Prediction of the Interacting Multiple Model Algorithm , 1992, 1992 American Control Conference.

[13]  Mathieu Joerger,et al.  Unmanned Aircraft System Sense and Avoid Integrity: Intruder Linear Accelerations and Analysis , 2017, J. Aerosp. Inf. Syst..

[14]  Matthew Edwards,et al.  A Safety Driven Approach to the Development of an Airborne Sense and Avoid System , 2012, Infotech@Aerospace.

[15]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[16]  Inseok Hwang,et al.  Performance analysis of hybrid estimation algorithms , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).