Automatic-dependent surveillance-broadcast experimental deployment using system wide information management

This paper describes an automatic-dependent surveillance-broadcast (ADS-B) implementation for air-to-air and ground-based experimental surveillance within a prototype of a fully automated air traffic management (ATM) system, under a trajectory-based-operations paradigm. The system is built using an air-inclusive implementation of system wide information management (SWIM). This work describes the relations between airborne and ground surveillance (SURGND), the prototype surveillance systems, and their algorithms. System's performance is analyzed with simulated and real data. Results show that the proposed ADS-B implementation can fulfill the most demanding surveillance accuracy requirements

Runway Incursion Prevention: A Technology Solution

A runway incursion occurs any time an airplane, vehicle, person or object on the ground creates a collision hazard with an airplane that is taking off or landing at an airport under the supervision of Air Traffic Control (ATC). Despite the best efforts of the Federal Aviation Administration (FAA), runway incursions continue to occur more frequently. The number of incursions reported in the U.S. rose from 186 in 1993 to 431 in 2000, an increase of 132 percent. Recently, the National Transportation Safety Board (NTSB) has made specific recommendations for reducing runway incursions including a recommendation that the FAA require, at all airports with scheduled passenger service, a ground movement safety system that will prevent runway incursions; the system should provide a direct warning capability to flight crews. To this end, NASA and its industry partners have developed an advanced surface movement guidance and control system (A-SMGCS) architecture and operational concept that are designed to prevent runway incursions while also improving operational capability. This operational concept and system design have been tested in both full-mission simulation and operational flight test experiments at major airport facilities. Anecdotal, qualitative, and specific quantitative results will be presented along with an assessment of technology readiness with respect to equipage

Cooperative and non-cooperative sense-and-avoid in the CNS+A context: a unified methodology

A unified approach to cooperative and noncooperative Sense-and-Avoid (SAA) is presented that addresses the technical and regulatory challenges of Unmanned Aircraft Systems (UAS) integration into nonsegregated airspace. In this paper, state-of-the-art sensor/system technologies for cooperative and noncooperative SAA are reviewed and a reference system architecture is presented. Automated selection of sensors/systems including passive and active Forward Looking Sensors (FLS), Traffic Collision Avoidance System (TCAS) and Automatic Dependent Surveillance - Broadcast (ADS-B) system is performed based on Boolean Decision Logics (BDL) to support trusted autonomous operations during all flight phases. The BDL adoption allows for a dynamic reconfiguration of the SAA architecture, based on the current error estimates of navigation and tracking sensors/systems. The significance of this approach is discussed in the Communication, Navigation and Surveillance/Air Traffic Management and Avionics (CNS+A) context, with a focus on avionics and ATM certification requirements. Additionally, the mathematical models employed in the SAA Unified Method (SUM) to compute the overall uncertainty volume in the airspace surrounding an intruder/obstacle are described. In the presented methodology, navigation and tracking errors affecting the host UAS platform and intruder sensor measurements are translated to unified range and bearing uncertainty descriptors. Simulation case studies are presented to evaluate the performance of the unified approach on a representative UAS host platform and a number of intruder platforms. The results confirm the validity of the proposed unified methodology providing a pathway for certification of SAA systems that typically employ a suite of non-cooperative sensors and/or cooperative systems

UAS Surveillance Criticality

The integration of unmanned aircraft systems (UAS) into the national airspace system (NAS) poses considerable challenges. Maintaining human safety is perhaps chief among these challenges as UAS remote pilots will need to interact with other UAS, piloted aircraft, and other conditions associated with flight. A research team of 6 leading UAS research universities was formed to respond to a set of surveillance criticality research questions. Five analysis tools were selected following a literature review to evaluate airborne surveillance technology performance. The analysis tools included: Fault Trees, Monte Carlo Simulations, Hazard Analysis, Design of Experiments (DOE), and Human-in-the-Loop Simulations. The Surveillance Criticality research team used results from these analyses to address three primary research questions and provide recommendations for UAS detect-and-avoid mitigation and areas for further research

System elements required to guarantee the reliability, availability and integrity of decision-making information in a complex airborne autonomous system

Current air traffic management systems are centred on piloted aircraft, in which all the main decisions are made by humans. In the world of autonomous vehicles, there will be a driving need for decisions to be made by the system rather than by humans due to the benefits of more automation such as reducing the likelihood of human error, handling more air traffic in national airspace safely, providing prior warnings of potential conflicts etc. The system will have to decide on courses of action that will have highly safety critical consequences. One way to ensure these decisions are robust is to guarantee that the information being used for the decision is valid and of very high integrity. [Continues.

DANTi: Detect and Avoid iN The Cockpit

Mid-air collision risk continues to be a concern for manned aircraft operations, especially near busy non-towered airports. The use of Detect and Avoid (DAA) technologies and draft standards developed for unmanned aircraft systems (UAS), either alone or in combination with other collision avoidance technologies, may be useful in mitigating this collision risk for manned aircraft. This paper describes a NASA research effort known as DANTi (DAA iN The Cockpit), including the initial development of the concept of use, a software prototype, and results from initial flight tests conducted with this prototype. The prototype used a single Automatic Dependent Surveillance - Broadcast (ADS-B) traffic sensor and the own aircraft's position, track, heading and air data information, along with NASA-developed DAA software to display traffic alerts and maneuver guidance to manned aircraft pilots on a portable tablet device. Initial flight tests with the prototype showed a successful DANTi proof-of-concept, but also demonstrated that the traffic separation parameter set specified in the RTCA SC-228 Phase I DAA MOPS may generate excessive false alerts during traffic pattern operations. Several parameter sets with smaller separation values were also tested in flight, one of which yielded more timely alerts for the maneuvers tested. Results from this study may further inform future DANTi efforts as well as Phase II DAA MOPS development

Flight Deck Automation Support with Dynamic 4D Trajectory Management for ACAS: AUTOFLY-AID

AUTOFLY-Aid Project aims to develop and demonstrate novel automation support algorithms and tools to the flight crew for flight critical collision avoidance using “dynamic 4D trajectory management”. The automation support system is envisioned to improve the primary shortcomings of TCAS, and to aid the pilot through add-on avionics/head-up displays and reality augmentation devices in dynamically evolving collision avoidance scenarios. The main theoretical innovative and novel concepts to be developed by AUTOFLY-Aid Project are a) design and development of the mathematical models of the full composite airspace picture from the flight deck’s perspective, as seen/measured/informed by the aircraft flying in SESAR 2020 b) design and development of a dynamic trajectory planning algorithm that can generate at real-time (on the order of seconds) flyable (i.e. dynamically and performance-wise feasible)alternative trajectories across the evolving stochastic composite airspace picture (which includes new conflicts, blunder risks, terrain and weather limitations) and c) development and testing of the Collision Avoidance Automation Support System on a Boeing 737 NG FNPT II Flight Simulator with synthetic vision and reality augmentation while providing the flight crew with quantified and visual understanding of collision risks in terms of time and directions and countermeasures

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

Detect-and-Avoid: Flight Test 6 Scripted Encounters Data Analysis

The Unmanned Aircraft System (UAS) in the National Airspace System (NAS) project conducted Flight Test 6 (FT6) in 2019. The ultimate goal of this flight test was to produce data to inform RTCA SC-228's Phase II Minimum Operational Performance Standards (MOPS) for Detect and Avoid (DAA) and Low Size, Weight, and Power Sensors. This report documents the analysis of scripted encounters' data. Scripted encounters own were analyzed and categorized based on the outcome of alert, maneuver guidance, and effectiveness of pilots' maneuver in resolving conflicts. Results indicate that UAS pilots' decisions as well as intruder maneuvers are leading factors that contribute to ineffective DAA maneuvers. Results also show that adding buffers to the DAA's suggested minimum turn angle improves effectiveness of the DAA maneuvers