Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 124.94K | Year: 2016
Both the current NAS, as well as NextGen, need successful use of advanced tools. Successful training is required today because more information gathering and decision making must be done manually, which requires training in the fundamental principles and objectives of traffic management. Successful training is required in NextGen due to the increased reliance on automation. Given the multitude of input channels and actors that must be included in an environment for comprehensive training of Traffic Management Coordinators (TMCs), it would be too costly and too complex to attempt a full-scale human-in-the-loop simulation or table-top exercise that includes the direct participation of all of these entities. In this research, we will study and prototype effective techniques and technologies to allow virtual and/or constructive simulation of key components of the TMC's environment to achieve a significant step forward in the state of the art of TMC training. The proposed innovation and focus on this research is called the COMprehensive Environment for TM Training by Simulation (COMETTS). NASA's recent research thrust in the Shadow Mode Assessment using Realistic Technologies for the National Airspace System (SMART NAS) provides an important step toward, and platform for, research in simulation-based training for the controller and TMC workforce. Such research holds the potential to significantly improve the transition of technologies from NASA to the FAA and onward to fully successful implementation and acceptance by the end users. This proposed effort will leverage SMART NAS to conduct research, development, prototyping and evaluation of advanced simulation-based TMC training.
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 739.64K | Year: 2015
A key component to solving many engineering challenges of UAS integration into the National Airspace System is the ability to state the numbers of forecasted UAS by airframe and mission/operation type being performed within discrete airspaces. The UAS Demand Generator for Discrete Airspace Density (UAXPAN) is a cloud-based application producing UA demand forecasts from user defined scenarios consisting of: UAS, industry, missions, and forecast elements. In Phase I, UAXPAN was developed as a prototype to demonstrate Government and commercial UAS operations. In Phase II, the overall project objective is to enhance the UAXPAN system to allow users and governance groups to start actively adding UAS missions, forecasting UAS growth, and assessing the impact of UAS operations in different areas. The collected data can then be shared with other users seeking to perform assessments of impact or demand and to optimize the crowd sourced input in a cloud-based hosting approach to receive feedback. This will be accomplish by developing UAXPAN as a full system and testing this system as a Beta Operation, designing and developing a user friendly wizard forecast system, and conducting research and analysis on communications and spectrum planning, ATC loading, and environmental impacts of noise and atmospheric emissions.
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 123.13K | Year: 2015
Air traffic control research, air traffic control operations and user operations rely on simulators that predict the future time history of three-dimensional aircraft trajectories. Such predicted trajectories are fundamental inputs to a wide variety of planning, monitoring and control tasks, including airline seasonal fleet planning, pre-departure flight planning, real-time airspace and airport load forecasting, traffic sequencing and spacing, separation assurance, weather routing, runway assignment, etc. Clearly trajectory simulators are core components of many air traffic applications; it is important that they be as accurate, as efficient and as manageable as possible. We propose an innovative trajectory constraint modeling utility that leads to improvements in all three areas. Regarding trajectory simulator accuracy, there are several categories of error sources that contribute to trajectory prediction uncertainty. One key, and overlooked, source is flight plan nonconformance. Flights often fail to follow their flight plan due to various constraints that are encountered, such as altitude holding, speed control, path stretching and reroutes. It is important to model these constraints both for flight time forecasting as well as load forecasting. Such constraints are not deterministic and their variance is a major contributor to trajectory prediction error. Therefore our constraint modeler produces probabilistic constraint forecasts which, in turn, support probabilistic trajectory prediction. Advanced air traffic applications not only require trajectory predictions that (i) are as accurate as possible, but also that (ii) provide an indication of their error as well, which can vary substantially. Traditionally, predictors have produced deterministic trajectories and their uncertainty is often ignored. We also discuss in our proposal how our trajectory constraint modeler supports significant improvements in efficiency and manageability of trajectory predictors.
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 124.58K | Year: 2015
On a daily basis, airport managers manually analyze current and future weather conditions to determine whether their facility will be negatively impacted. While not the only weather factor, one of the more important factors is wind, specifically wind shifts. Every morning the runway configuration for an airport is set based on the expected dominant wind flow across the area in order to maximize the efficiency of the terminal area. If the wind does not change direction over the course of the day, the airport is able operate at its optimum level, barring any other impactful weather event. If the wind does shift its direction, a change in the airport's runway configuration is required. This decision of when to change the runway configuration, however, is not always easy, and often times it can be a difficult and sometimes costly one. If the configuration of the runway is changed too late or too early in relation to the time of the wind shift, the throughput at the airport will decrease. To support this decision, a wind shift detection model is proposed. This model will utilize operational weather products, including the Localized Aviation MOS Product (LAMP) and the High Resolution Rapid Refresh (HRRR), to produce a probabilistic estimate of when a wind shift is expected to occur. By automating the process of detecting wind shifts, it improves the efficiency of the airport by allowing airport managers to focus on configuring the airport rather than when the wind shift will occur. To determine the accuracy and feasibility of the model for use in real-time operations, it will be tested at number of airports around the NAS, specifically for historical scenarios when an unexpected wind shift negatively impacted operations. Phase II will look at adding a live weather data feed to the, incorporating traffic data, as well as integrating the model within the Airport Runway Configuration Management (ARCM) concept.
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 124.97K | Year: 2015
In this proposed research and development project, we investigate and design an innovative system that solves the key problem of multi-vehicle cooperation and interoperability. Our approach is based upon the principles and techniques of Performance Based Navigation (PBN) and Required Navigation Performance (RNP) concepts and is adjusted for other separation, safety, and weather effects. We design the architecture for a system that simultaneously maintains the efficiency and success of a multi-vehicle mission while also detecting and resolving potential loss of separation and conflicts within the NAS. The challenge is that for a variety of missions, teams of unmanned vehicles can perform the mission efficiently in particular configurations, but simultaneously the team of vehicles must be aware of and accommodate themselves, external traffic, potential intruders, environmental constraints, terrain, and so forth. Our software based system, UA-Teamer, provides the architecture and solutions to achieve mission success and the efficiency promised that multi-vehicle teams can accomplish while maintaining system safety. Our primary technical objectives are: i) Demonstrate a common set of flight path planning parameters built using PBN and other constraints enabling UA to interoperate and cooperate as a team; ii) Produce an algorithmic software approach that selects a best fit flight path set for a UA team mission that involves heterogeneous UAs; and iii) Show that the planners can response to conformance monitoring needs for re-planning and contingencies. The project includes a feasibility demonstration and human factors research into the display of optional trajectory sets for the UA team.