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Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 735.68K | Year: 2014

To help improve the accuracy and efficiency with which battlespace awareness is established in maritime and littoral environments, new capabilities are required for automated detection and correction of sensor metadata errors from ISR platforms. AFOS provides a unique solution to this problem and has been successfully prototyped and demonstrated live on relevant data. The proposed Phase II.5 effort will provide continued development of AFOS"algorithms, software, and interfaces, bringing the system and its metadata correction capabilities closer to the warfighter, inside the analyst"s workstation, and to the fingertips of the sensor operator. The focus in Phase II.5 is on realism and the use of maximally relevant data sets to help ensure that algorithms and software are ready to tackle the challenges of the operational environment.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 124.99K | Year: 2014

During the conduct of a NASA Research Announcement (NRA) in 2012 and 2013, the Mosaic ATM team first developed the Strategic Arrivals Recommendation Tool concept, or START. Though this concept was developed in direct response to Miami Center's request to our team to provide a tool that could assist them during the summer convection season, this concept could be applied anywhere in the NAS. Given that the Florida region has some of the most challenging convection to predict, we believe that if START can be successful for ZMA, it can be adapted to support TMUs in other areas of the NAS where convection is an issue. START is the use of en route weather translation and airspace capacity models to the challenge of strategically planning arrival flows in advance of expected capacity reductions due to convective weather. START provides probabilistic en route capacity estimates for corridors used by arriving flights, models the impact of capacity reductions on traffic, and then provides recommendations for strategic Traffic Management Initiatives that better balance demand and capacity given the uncertainty in the weather. In Phase I, our objective is to demonstrate the feasibility and potential benefit of the START automated recommendations concept. We intend to accomplish this by developing and testing specific algorithms that will be necessary to achieve this goal and providing a preliminary benefit assessment of the concept. These include the airspace capacity model and the reroute recommendation optimization model. Our Phase II objectives are to integrate the algorithms developed in Phase I into the START prototype software. We will continue to work with Miami Center and American Airlines to vet START as the capabilities mature, ensuring that the tool is meeting the needs of the Traffic Management Unit specialists and airspace users. Through the accomplishment of the Phase II activities, START will be ready for full field evaluations to be conducted by NASA.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 124.99K | Year: 2015

A wide variety of flow management techniques is employed every day in the NAS, from strategic Ground Delay Programs (GDP's) with national scope to local Miles-In-Trail (MIT) restrictions that affect traffic over a specific fix. The choice of the flow management technique to employ and the timing, extent, and other parameters associated with the technique are determined by controller judgment informed by experience. However, experience is only as useful as the information that can be assimilated from it, and in the case of flow management decisions the available information is limited and biased. We propose to research and prototype a system that will provide controllers with the metrics they need to understand how their past decisions fared. Our proposal in this Phase I SBIR is to perform the research to determine how well such metrics could be made to function. Phase II would extend the work to implement metrics that could be used within NASA efforts and later transitioned to the FAA for its use. The proposed metrics do not attempt to determine what the "correct" level of restrictions would have been. The appropriate amount of restriction to apply in any situation is a matter of judgment that must weigh the certainty of the information on which it is based as well as the outcomes that would result from errors in either direction. Rather, the metric would quantify the restrictions' performance in hindsight. To quantify performance of a GDP, for instance, it would measure the degree to which tactical flow management had to make up for excessive airborne demand for the airport, and the degree to which insufficient demand was available to fill the airport's capacity. Second-order effects the metric would quantify include the degree to which the traffic originating in-center, in tier one, in tier two, and farther away gained or lost priority relative to each other, indicative of the GDP's timing relative to the timing of the demand/capacity imbalance.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 1.50M | Year: 2015

NASA's Air Traffic Management (ATM) research has produced many important, advanced decision support tools (DSTs) over the past three decades. A key challenge in the design and use of ATM DSTs is to determine how much control should be applied to the flow of traffic and at what point in the flow the control should be applied. This question can be addressed both during the initial operational ATM planning process, as well as during dynamic operations re-planning. This challenge has significant impact on the resulting effectiveness of any ATM control program that is applied, because inefficiencies can be caused by either under or over-control of the flow. Unfortunately, comprehensive DSTs don't exist for many ATM decisions that must be made, and most DSTs that do exist do not provide any guidance regarding when the control should be applied, nor do they quantify the potential risk associated with the timing of control application. An integrated decision support capability is needed to provide ATM specialists and flight operators with information to support planning and decision-making about tactical and strategic TMIs. The significant challenge that exists in providing this decision support capability is the uncertainty of prediction of both demand and capacity. The work that has been conducted in this Phase I SBIR effort, and that is proposed for continuation in Phase II, addresses this fundamental research need in ATM automation system design in the context of the integration of Tactical Departure Scheduling (TDS) with Traffic Flow Management (TFM). During Phase I, we designed, tested and conducted initial validation on mathematical and simulation models that characterize and quantify the relationship between IADS capabilities and other TMIs. These models provide guidance and input for further NASA research efforts and activities, and they will also provide real-time operational decision support for Traffic Management Coordinators (TMCs) and other ATC specialists.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 124.74K | Year: 2015

This project addresses three modeling elements relevant to NASA's IADS research and ATD-2 project, two related to ramp operations at primary airports and one related to departure status at secondary airports. Departure scheduling requires departure status information from secondary airports that lack surface surveillance. We propose a method using aircraft transponder activation and data science modeling to estimate earliest takeoff time and runway. Fast-time simulations of IADS concepts require models of how ramp controllers manage near-gate aircraft movements; and how flight operators will interact with collaborative departure scheduling concepts. We propose to develop a model, with defined interfaces to be usable in any NASA simulation platform, for conflict/congestion-free aircraft movements in alleyways, coordinated to allow efficient, simultaneous movements. We will also offer a model for flight operator's swapping gate delays to free needed gates and prioritize their schedules. These models are also relevant to NASA's SARDA and SMART NAS projects. We will document the model designs, deliver all source code, and, in Phase 2, publish models under and Open Source license.


Grant
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.


Grant
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.


Grant
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.


Grant
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.


Grant
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.

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