Avionics sensor fusion for small size unmanned aircraft Sense-and-Avoid

Cooperative and non-cooperative Sense-and-Avoid (SAA) systems are key enablers for Unmanned Aircraft (UA) to routinely access non-segregated airspace. In this paper some state-of-the-art cooperative and non-cooperative sensor and system technologies are investigated for small size UA applications, and the associated multisensor data fusion techniques are discussed. Non-cooperative sensors including both passive and active Forward Looking Sensors (FLS) and cooperative systems including Traffic Collision Avoidance System (TCAS), Automatic Dependent Surveillance - Broadcast (ADS-B) system and/or Mode C transponders are part of the proposed SAA architecture. After introducing the SAA system processes, the key mathematical models for data fusion are presented. The Interacting Multiple Model (IMM) algorithm is used to estimate the state vector of the intruders and this is propagated to predict the future trajectories using a probabilistic model. Adopting these mathematical models, conflict detection and resolution strategies for both cooperative and un-cooperative intruders are identified. Additionally, a detailed error analysis is performed to determine the overall uncertainty volume in the airspace surrounding the intruder tracks. This is accomplished by considering both the navigation and the tracking errors affecting the measurements and translating them to unified range and bearing uncertainty descriptors, which apply both to cooperative and non-cooperative scenarios. Detailed simulation case studies are carried out to evaluate the performance of the proposed SAA approach on a representative host platform (AEROSONDE UA) and various intruder platforms, including large transport aircraft and other UA. Results show that the required safe separation distance is always maintained when the SAA process is performed from ranges in excess of 500 metres.

[1]  M Berry,et al.  Literature Review on Detect, Sense, and Avoid Technology for Unmanned Aircraft Systems , 2009 .

[2]  Jason J. Ford,et al.  See and Avoid Using Onboard Computer Vision , 2012 .

[3]  Michael D. Snyder,et al.  HUMAN FACTORS DESIGN GUIDE (HFDG) FOR ACQUISITION OF COMMERCIAL OFF-THE-SHELF SUBSYSTEMS, NON-DEVELOPMENTAL ITEMS, AND DEVELOPMENTAL SYSTEMS , 1996 .

[4]  Roberto Sabatini,et al.  Navigation and Guidance System Architectures for Small Unmanned Aircraft Applications , 2014 .

[5]  Alessandro Gardi,et al.  Reverse engineering of a fixed wing Unmanned Aircraft 6-DoF model based on laser scanner measurements , 2014, 2014 IEEE Metrology for Aerospace (MetroAeroSpace).

[6]  Peter Thomas,et al.  On-board trajectory generation for collision avoidance in unmanned aerial vehicles , 2011, 2011 Aerospace Conference.

[7]  Mykel J. Kochenderfer,et al.  Encounter modeling for sense and avoid development , 2008, 2008 Integrated Communications, Navigation and Surveillance Conference.

[8]  Cui Pingyuan,et al.  The Method to Determine Spherical Harmonic Model of Asteroid based on Polyhedron , 2012 .

[9]  Patrick Garrec,et al.  Sense and avoid radar using Data Fusion with other sensors , 2011, 2011 Aerospace Conference.

[10]  Robert A. Klaus,et al.  A Radar-Based, Tree-Branching Sense and Avoid System for Small Unmanned Aircraft , 2013 .

[11]  E. Garboczi.  Three-dimensional mathematical analysis of particle shape using X-ray tomography and spherical harmonics: Application to aggregates used in concrete , 2002 .

[12]  Alessandro Gardi,et al.  A Laser Obstacle Warning and Avoidance system for Manned and Unmanned Aircraft , 2014, 2014 IEEE Metrology for Aerospace (MetroAeroSpace).

[13]  Alessandro Gardi,et al.  4-Dimensional Trajectory Negotiation and Validation System for the Next Generation Air Traffic Management , 2013 .

[14]  Keith D. Hoffler,et al.  UAS Sense and Avoid Requirements Assessment , 2012 .

[15]  Roberto Sabatini.  Development of a Laser Test Range for the Italian Air Force: Airborne Laser Systems Performance Prediction, Safety Analysis, Flight Testing and Operational Training , 2008 .

[16]  Alessandro Gardi,et al.  Novel Flight Management System for Real-Time 4-Dimensional Trajectory Based Operations , 2013 .

[17]  Roberto Sabatini,et al.  A Novel GNSS Integrity Augmentation System for Civil and Military Aircraft , 2013 .

[18]  Giancarmine Fasano,et al.  Multi-Sensor-Based Fully Autonomous Non-Cooperative Collision Avoidance System for Unmanned Air Vehicles , 2008, J. Aerosp. Comput. Inf. Commun..

[19]  Daniel Hodouin,et al.  UAV Optimal Obstacle Avoidance while Respecting Target Arrival Specifications , 2011 .

[20]  Thomas P. Spriesterbach,et al.  Unmanned Aircraft System Airspace Integration in the National Airspace Using a Ground-Based Sense and Avoid System , 2013 .

[21]  Roberto Sabatini,et al.  A New Avionics-Based GNSS Integrity Augmentation System: Part 2 – Integrity Flags , 2013, Journal of Navigation.

[22]  Alessandro Gardi,et al.  LIDAR Obstacle Warning and Avoidance System for Unmanned Aircraft , 2014 .

[23]  Alessandro Gardi,et al.  Low-cost sensors data fusion for small size unmanned aerial vehicles navigation and guidance , 2013 .

[24]  David R. Maroney,et al.  UNMANNED AIRCRAFT COLLISION AVOIDANCE – TECHNOLOGY ASSESSMENT AND EVALUATION METHODS , 2007 .