Sergio Torres

Trajectory synchronization and negotiation in Trajectory Based Operations

Trajectory Based Operations (TBO) is a key component of both the US Next Generation Air Transport System (NextGen) and Europes Single European Sky ATM Research (SESAR). There is a significant amount of effort underway in both programs to advance this concept. Trajectory Synchronization and Negotiation are key required capabilities in both the NextGen and SESAR TBO concepts, and they provide the framework to improve the efficiency of airspace operations. In recognition of the importance of TBO, General Electric and Lockheed Martin have created a Joint Strategic Research Initiative (JSRI), which aims to generate technologies that accelerate adoption of TBO. This paper explores various trajectory synchronization and negotiation concepts, including existing gaps and shortfalls. This paper also presents the JSRI simulation and evaluation environment being developed, which embeds trajectory synchronization and negotiation concepts, and has the potential to address existing gaps and shortfalls.

Trajectory Synchronization between air and ground trajectory predictors

Trajectory Based Operations is a key concept of future air traffic management systems in both the United States and Europe. An overarching goal of Trajectory Based Operations is to reduce the uncertainty associated with the prediction of an aircrafts future location through the use of an accurate 4-Dimensional Trajectory. This is not realizable without improving the coordination and interoperability of air and ground systems. By leveraging GEs Flight Management System and aircraft expertise with Lockheed Martins Air Traffic Control domain expertise including the En Route Automation Modernization system, a research effort has been undertaken to explore and evaluate Trajectory Synchronization and Negotiation concepts that bring airspace operations closer to the business-optimal goal in a safe and efficient manner. This paper focuses on different aspects of Trajectory Synchronization concepts, including existing gaps and shortfalls, and potential approaches to resolve them. An analysis of trajectory synchronization use cases and an air-ground trajectory synchronization algorithm are presented. The simulation infrastructure incorporating actual Flight Management System and En Route Automation Modernization system trajectory predictors is discussed, including simulation results from a trajectory synchronization case study. The cases studied show that consistent trajectory predictions can be achieved between the air and ground systems through trajectory data exchange via Controller-Pilot Data Link Communications uplink and downlink messages as well as the Automatic Dependent Surveillance-Contract service, including the Extended Projected Profile application.

Determination and ranking of trajectory accuracy factors

Trajectory accuracy improvements provide the opportunity to reduce fuel consumption and emissions by increasing the predictability of the National Airspace System (NAS). In addition to the environmental benefits, the ability to improve trajectory prediction accuracy enables trajectory based operations (TBO). Because of the foundational reliance on accurate gate-to-gate four dimensional trajectory (4DT) predictions in TBO, trajectory predictors (TP) will have to meet stringent accuracy performance requirements. There have been significant advances in understanding the accuracy performance and limitations of different TP algorithms. However, implementation of TBO requires identification of the specific aspects of trajectory prediction that will need improvement in order to deliver the necessary accuracy. The challenge is not to select a single trajectory model approach but rather to define which modeling approaches can deliver better performance under different circumstances and to understand the limitations of each. A clear understanding of accuracy factors and the way they affect different modeling approaches allows development of hybrid algorithms that combine the strengths of different approaches. It is known, for instance, that kinetic models are affected by the uncertainty in aircraft mass, while parametric models are affected by the variance in the velocities encoded in lookup tables, but what is the relative contribution of these errors to the overall accuracy of predictions? The paper presents an accuracy factors analysis methodology to rank the sources of error according to their respective impact. This analysis facilitates the identification of the modeling issues that have the largest impact in prediction accuracy and the systematic evaluation of the potential improvements that could be expected from the use of aircraft intent information that will be available when air-ground data link is deployed. The sources of errors in trajectory prediction have been amply studied; however, a clear understanding of the relative contribution of these errors to accuracy performance using realistic scenarios is necessary in order to be able to improve the models. The accuracy factors ranking methodology presented here relies on the computation of the Past Maximum Deviation (PMD) metric for each error measurement and using the PMD to attribute the error to one of four possible error source categories: lateral, vertical, velocity and heading. The PMD metric keeps track of the largest track-trajectory deviation prior to the measurement along each of the four categories. PMD distributions serve as a diagnostic that reveal the performance of a trajectory predictor (TP) along the four dimensions discussed and can be used as an effective tool to compare two TPs or two variants of the same TP. Results of accuracy factor analysis based on the En Route Automation Modernization ERAM parametric algorithm using a live recorded scenario are presented.

An integrated approach to air traffic management to achieve trajectory based operations

An overarching goal of the Federal Aviation Administrations (FAA) Next Generation Air Transportation System (NextGen) and the Single European Sky ATM Research (SESAR) is to improve predictability of the air traffic system as a whole in order to increase capacity and efficiency while maintaining safety and incorporating user preferences. The achievement of these goals requires tight integration of new technologies and adjustments to operational procedures as envisioned in Trajectory Based Operations. This paper presents a concept that directly integrates the trajectory generated by the aircrafts Flight Management System with air traffic management operations. The concept is an application of the principles of self-organization (swarm theory) where safety and traffic flow management goals are achieved by broadcasting all the operational constraints on a 4D-Grid and populating the grid with the 4D-trajectories of all other flights. As new flights get added to the system the remaining capacity is in full view to prospective operators who will be able to plan flights using available capacity. The paper shows how self-organized air traffic leads to optimality, provides details of the concept, describes near term implementation strategies and explores long term implications.

Enabling Trajectory Based Operations through Air-Ground Data Exchange

Trajectory Based Operations (TBO), a key concept in the US Next Generation Air Transportation System (NextGen) and in Europe’s Single European Sky ATM Research (SESAR), allows for more efficient planning and execution of flight plans. The basis for TBO is to plan and perform operations using a shared view of the flight that takes into account user preferences and is expressed by a four-dimensional (4D) trajectory. Interoperability issues, due to dissimilar requirements that trajectories need to satisfy in the various automation systems involved in air traffic management, could limit the efficiency gains expected from TBO. Recognizing the importance to TBO of advancing trajectory synchronization and negotiation technologies, Lockheed Martin, GE Aviation Systems and GE Global Research have joined forces to develop and test trajectory synchronization strategies. We identified the steps and specific messages that need to be exchanged in order to resolve discrepancies between the trajectory used by the Flight Management System (FMS) to guide the aircraft and the trajectory used by Air Traffic Control (ATC) ground automation systems. Using real world FMS and ATC systems we developed trajectory synchronization and negotiation algorithms and methods to integrate the FMS trajectory directly into the ground automation systems used by ATC. The FMS trajectory takes into account operator preferences and has the benefit of the high fidelity aircraft performance models and flight specific parameters that affect the trajectory. In the process of synchronizing the air-ground trajectories, all the advantages of the FMS trajectory are now available to ground automation.

Air-ground trajectory synchronization — Metrics and simulation results

It has been established that Trajectory Based Operations are a key component of future Air Traffic Management systems as currently underway in the United States with NextGen and Europe with SESAR. One of the major goals of Trajectory Based Operations is to provide participants accurate 4-Dimensional Trajectories predicting the future location of the aircraft with a high level of certainty. This is not realizable without improving the coordination and interoperability of air and ground systems. By leveraging GEs Flight Management System and aircraft expertise with Lockheed Martins Air Traffic Control domain expertise, including the En Route Automation Modernization system, a research initiative has been formed to explore and evaluate means of better integrating air and ground systems to bring airspace operations closer to the business-optimal goal in a safe and efficient manner. The two main components of this effort are trajectory synchronization and trajectory negotiation. Trajectory synchronization will essentially result in a more complete flight plan in the air and a more accurate trajectory representation on the ground, which is a prerequisite for trajectory negotiation. This paper briefly discusses the high-level trajectory synchronization algorithm and its implementation in a fast-time simulation environment that incorporates actual Flight Management and Air Traffic Control software. It then focuses on the analysis of metrics and simulation results from several case studies. The conclusion of these studies shows that implementation of the trajectory synchronization algorithm using Controller-Pilot Data Link Communications messages as well as the Automatic Dependent Surveillance-Contract service (including the Extended Projected Profile application) achieves consistent trajectory predictions between the air and ground systems.

En-Route Automation Modernization (ERAM) trajectory model evolution to support trajectory-based operations (TBO)

The En Route Automation Modernization (ERAM) system is the Federal Aviation Administrations (FAA) automation platform used to manage air traffic at 20 en route centers in the US. This paper describes the trajectory model in the currently operational ERAM system and presents an approach to enhance ERAMs aircraft performance model with a high fidelity kinetic capability that can provide greater consistency with the guidance trajectory generated by Flight Management Systems (FMS). ERAM has a parametric trajectory predictor that uses static aircraft data. A physics-based kinetic trajectory predictor, on the other hand, can exploit flight-specific aircraft intent that will become increasingly available as the Operational Improvements in the Next Generation Air Transportation (NextGen) system are deployed. Additionally, a kinetic model will provide increased interoperability between air traffic management systems. A kinetic aircraft performance model for ERAM has been prototyped and evaluated. When flight-specific intent information is available or can be derived, the accuracy of predicting the top of descent (TOD) is significantly improved with a kinetic model. An ERAM test run with the kinetic prototype based on recorded traffic of 50 flights executing idle thrust descents shows that, relative to the baseline ERAM legacy system, the number of trajectories with along-track TOD prediction errors less than 5 nautical miles increases from 9% to 63%. Considering the incremental nature of the NextGen implementation, a low risk approach to evolving ERAM is to first move to a hybrid kinetic-parametric trajectory model, where current parametric modeling is retained and kinetic modeling is performed when flight-specific intent is available.

Trajectory management driven by user preferences

The evolution of the Traffic Flow Management (TFM) and Air Traffic Control (ATC) systems towards Trajectory Based Operations (TBO) is constrained by the need to support a mixed fleet of aircraft for the foreseeable future. Trajectory synchronization and negotiation in particular are key enablers of TBO that need to take into account both the advanced capabilities in existing and future Flight Management Systems (FMS), and the limitations in performance and capabilities of legacy aircraft. Trajectory management systems will have to address the challenges imposed by this reality. Lockheed Martin and General Electric (GE) have established a Joint Strategic Research Initiative (JSRI) to develop technologies that can efficiently address the challenges of trajectory management. In this paper, we describe a user preference driven trajectory management concept that was developed under the JSRI. The idea consists of providing flight-specific cost information to Decision Support Tools (DST) used by controllers for strategic conflict resolution and schedule management. The cost of operating a flight may be decomposed into the cost of fuel and other direct and time related costs (such as crew pay, aircraft maintenance, or connecting passengers). In current operations, there is no practical mechanism to make this information known to the ground automation (or the controller) which needs to make changes to the business trajectory; moreover, much of this information is considered proprietary or sensitive by the operators, making it difficult or impossible to determine the true cost impact of modifications to the reference business trajectory. However, it is precisely the overall cost of operations that should be driving the decision process. A mechanism has been developed to extract the effective operating cost from the FMS and to make that information available to ground DSTs. Cost information is encoded in coefficients that do not reveal operator specific business strategies that may be considered proprietary. This enables the ground controller (and DSTs) to make an informed decision that may increase the likelihood of operators business objectives being accommodated, and would allow operators to better inform ATCs decisions such that the impact on the business objectives are minimized when changes are required. The encoding of cost information is made in a compact way so that communication band-width requirements are reasonable.

Assessing tactical alert function accuracy performance

An effective test program for the evaluation of the performance of the Short Term Conflict Alert (STCA) function must consider the definition of the test scenario, assessment metrics, and alert classification rules. This paper discusses test issues encountered using a realistic scenario based on live data. The paper describes a method to obtain systematic and automated measurements of nuisance rate, missed rate, and alert response time for a realistic traffic scenario. Based on experience using live data, the concept of a valid alert is introduced to deal with an alert that is not associated with an actual conflict nor considered to be a nuisance alert (e.g., an alert issued prior to an aircraft maneuver that avoids loss of separation). To classify alerts as nuisance or valid, and to check timeliness of alerts associated with a conflict, the approach relies on the comparison of the alerts declared by the system with those that would be expected from “truth data” projected forward in time (linear predictor) — truth data defined as the actual aircraft paths. Detailed alert classification rules addressing issues encountered in performance testing with realistic scenario data are described. Approaches to obtaining a representation of “truth data” are referenced. The method of using a Test Predictor operating on truth data in association with alert classification rules was used in performance evaluation of, and problem identification related to, the tactical alert function in the En Route Automation Modernization (ERAM) system and for studies of the Common Automated Radar Terminal System (Common ARTS). Aspects of the performance measurement approach described herein may be applicable to the development of accuracy requirements of future systems.

Accuracy impact of trajectory sampling and representation for Trajectory-Based Operations

In Trajectory-Based Operations (TBO) 4-dimensional trajectories (4DT) are the basis for planning and executing all aspects of flight operations. The exchange of 4DT data is therefore necessary and essential for TBO. The effectiveness of TBO is limited by trajectory accuracy, which depends on prediction errors due to assumptions and limitations in the construction process (wind errors, unknown aircraft intent, etc.) and in losses introduced in the sampling and representation of the trajectory. Trajectory sampling is the process of selecting the points included in the trajectory that is exported to clients. Trajectory representation is the set of attributes, such as location, speeds, etc., associated with each trajectory point. Emerging trajectory exchange standards need to take into account the effects of sampling and representation because the loss of accuracy in trajectory representation has a direct and significant impact on the ability to support TBO. This paper focuses on the analysis of issues and solutions related to loss of accuracy due to sampling and exclusion of high order time derivatives in 4DT data under realistic conditions. Analytical expressions of errors are derived and situations where these errors are more likely to arise are identified. The analysis examines mitigation strategies, such as segment length reduction and 4DT rebuild methods, to achieve required accuracy goals. The effects of wind uncertainty, spatial wind shear, variations of air speed and vertical rate in Optimized Profile Descent (OPD), and speed variations resulting from vertical guidance are examined under assumptions of limited sample rate and lack of acceleration information.