Mitigating Risk in Commercial Aviation Operations

The MAchine learning ESTimations for uRban Operations (MAESTRO) system is a novel approach that couples commodity sensors with advanced algorithms to provide real-time onboard local wind and kinematics estimations to a vehicle's guidance and navigation system. Sensors and computations are integrated in a novel way to predict local winds and promote safe operations in dynamic urban regions where Global Positioning System/Global Navigation Satellite System (GPS/GNSS) and other network communications may be unavailable or are difficult to obtain when surrounded by tall buildings due to multi-path reflections and signal diffusion. The system can be implemented onboard an Unmanned Aerial Systems (UAS) and once airborne, the system does not require communication with an external data source or the GPS/GNSS. Estimations of the local winds (speed and direction) are created using inputs from onboard sensors that scan the local building environment. This information can then be used by the onboard guidance and navigation system to determine safe and energy-efficient trajectories for operations in urban and suburban settings. The technology is robust to dynamic environments, input noise, missing data, and other uncertainties, and has been demonstrated successfully in lab experiments and computer simulations.

Actual air traffic data and weather information are utilized to evaluate an aircrafts flight-plan route and predict its trajectories for the climb, cruise, and descent phases. The dynamics for heading (the direction the aircraft nose is pointing) and airspeed are also modeled by the FACET software, while performance parameters, such as climb/descent rates and speeds and cruise speeds, can also be obtained from data tables. The resulting trajectories and traffic flow data are presented in a 3-D graphical user interface. The FACET software is modular and is written in the Java and C programming languages. Notable FACET applications include reroute conformance monitoring algorithms that have been implemented in one of the Federal Aviation Administrations nationally deployed, real-time operational systems.

The digital twin NAS simulator provides a complete digital copy of the individual systems that comprise the NAS to allow for the creation of offline simulations to test proposed changes to one or more individual systems based on actual historical data from the NAS or on real-time data from the NAS. The NAS is composed of a collection of systems, including source systems such as weather stations from various locations or airports, which are used by other systems such as individual aircraft flight data and airline operators. Other systems may include management systems such as the FAA, air traffic control centers, and flight traffic monitors. Operational data from each of these systems may be archived by a central information sharing platform such as the System Wide Information Management (SWIM) Program operated by the FAA. The digital twin NAS simulator can access archived SWIM data to create a digital twin NAS system to provide a virtual environment that may operate in real-time alongside the actual NAS, with the digital twin receiving live data updates from the actual NAS. A dedicated application programming interface (API) is used to facilitate communication between various distributed external components and the testbed. The testbed receives NAS data during a test and feeds the data to the simulation manager for use with a digital twin of the NAS system. The result is a virtual environment that is an exact twin of the actual operational system and is able to function identically to the actual NAS system because it is based on and uses the same data archived from the actual NAS system. A primary function of the virtual twin NAS is that it will allow for changes to one or more systems to be simulated against the archived NAS data and subsequently allow for a comparison between the simulated results and the actual results from the operational system. The digital twin simulator may also function in a distributed network environment, allowing for simulations of different elements to run simultaneously, which speeds up and improves the testing and evaluation of proposed changes.

NASA Ames has developed an Autonomous Situational Awareness Platform system for a UAS (ASAP-U), a traffic management system to incorporate Unmanned Aerial Systems (UASs) into the National Airspace System. The Autonomous Situational Awareness Platform (ASAP) is a system that combines existing navigation technology (both aviation and maritime) with new procedures to safely integrate Unmanned Aerial Systems (UASs) with other airspace vehicles. It uses a module called ASAP-U, which includes a transmitter, receivers, and various links to other UAS systems. The module collects global positioning system GPS coordinates and time from a satellite antenna, and this data is fed to the UAS's flight management system for navigation. The ASAP-U module autonomously and continuously sends UAS information via a radio frequency (RF) antenna using Self-Organized Time Division Multiple Access (SOTDMA) to prevent signal overlap. It also receives ASAP data from other aircraft. In case of transmission overload, priority is given to closer aircraft. Additionally, the module can receive weather data, navigational aid data, terrain data, and updates to the UAS flight plan. The collected data is relayed to the flight management system, which includes various databases and a navigation computer to calculate necessary flight plan modifications based on regulations, right-of-way rules, terrain, and geofencing. Conflicts are checked against databases, and if none are found, the flight plan is implemented. If conflicts arise, modifications can be made. The ASAP-U module continuously receives and transmits data, including UAS data and data from other aircraft, to detect conflicts with other aircraft, terrain, weather, and geofencing. Based on this information, the flight management system determines the need for course adjustments and the flight control system executes them for a safe flight route.

The Flight Awareness Collaboration Tool (FACT) user interface is a quad design with four areas. The Primary Map View shows the US with several traffic and weather overlays. The Surface Map View displays the selected airport with information on runway conditions and other factors. The Information View has specific data from various sources about the area of interest. This view also has a built-in algorithm that predicts the impact of the forecast winter weather on airport capacity. The Communication View supports messaging within the geographically-dispersed team that is using FACT. When an airport is selected in the Primary Map View, the information presented in the Surface Map and Information Views is focused on that choice. FACT is a web-based application using Node.js and MongoDB. It receives Java messages from the Federal Aviation Administration System Wide Information Management (SWIM) data repository. Data acquired from web pages and SWIM are tailored for FACTs Information View area. FACT is designed to reside on an existing workstation monitor to be put into use as needed.