Banavar Sridhar

Optimal Strategies for Free-Flight Air Traffic Conflict Resolution

Recent advances in navigation and data communication technologies make it feasible for individual aircraft to plan and fly their trajectories in the presence of other aircraft in the airspace. This way, individual aircraft can take advantage of the atmospheric and traffic conditions to optimally plan their paths. This capability is termed as the free flight concept. While the free flight concept provides new degrees of freedom to the aircraft operators, it also brings-in complexities not present in the current air traffic control system. In the free flight concept, each aircraft has the responsibility for navigating around other aircraft in the airspace. While this is not a difficult task under low speed, low traffic density conditions, the complexities of dealing with potential conflict with multiple aircraft can significantly increase the pilot’s work load. This paper presents the development of a conflict resolution algorithm based on the quasilinearization method to enable the practical implementation of the free flight concept. The algorithm development uses nonlinear point-mass aircraft models, and incorporates realistic operational constraints on individual aircraft. The analytical framework can also incorporate information about ambient atmospheric conditions. Realistic conflict resolution scenarios are illustrated. Due to their speed of execution, these conflict resolution algorithms are suitable for implementation on-board aircraft.

Aggregate Flow Model for Air-Traffic Management

Traditionally, models used in air-traffic control and flow management are based on simulating the trajectories of individual aircraft. This approach results in models with a large number of states, which are intrinsically susceptible to errors and difficult for designing and implementing optimal strategies for traffic flow management. This paper outlines an innovative approach for the development of linear-time-variant dynamic traffic flow system models based on historical data about the behavior of air traffic. The resulting low-order, linear, robust models can be used both for the analysis and synthesis of traffic flow management techniques for current and future systems.

Modeling and Optimization in Traffic Flow Management

Traffic flow management (TFM) allocates the various airport, airspace, and other resources to maintain an efficient traffic flow consistent with safety. TFM is a complex area of research involving the disciplines of operations research, guidance and control, human factors, and software engineering. Hundreds of human operators make TFM decisions that involve tens of thousands of aircraft, en route air traffic control centers, the Federal Aviation Administrations System Command Center, and many airline operation centers. This paper provides an overview of how TFM decisions are made today and challenges facing the system in the future, and reviews modeling and optimization approaches for facilitating system-wide modeling, performance assessments, and system-level optimization of the national airspace system in the presence of both en route and airport capacity constraints.

Initial Concepts for Dynamic Airspace Configuration

Future airspace needs to be flexible, dynamic and adaptable based on traffic demand, equipage, and weather. Initial concepts focus on three core areas: 1) restructuring airspace, 2) adaptable airspace, and 3) generic airspace. The paper presents mid-term and long-term airspace configuration concepts. These concepts were developed based on literature reviews, workshops, subject matter expert discussions, and field trips. The mid-term airspace configuration concept includes high altitude airspace where user-preferred routes will be predominant and low altitude airspace divided into regions for super density and metroplex areas and remaining portion. Subsequently, the long-term airspace configuration may include four primary regions: 1) airspace for automated separation assurance, 2) high altitude airspace, 3) super density and metroplex operations airspace, and 4) structured classic airspace.

Sequential Traffic Flow Optimization with Tactical Flight Control Heuristics

‡A sequential optimization method is applied to manage air traffic flow under uncertainty in airspace capacity and demand. To support its testing, a decision support system is developed by integrating a deterministic integer programming model for assigning delays to aircraft under en route capacity constraints to reactively account for system uncertainties. To reduce computational complexity, the model assigns only departure controls, while a tactical control loop consisting of a shortest path routing algorithm and an airborne holding algorithm refines the strategic plan to keep flights from deviating into capacity constrained airspace. This integrated approach is used to conduct thirty-two, 6-hour fast-time simulation experiments to explore variations in the number and severity of departure controls, tactical reroutes, and airborne holding controls. Three feasible types of traffic flow controls emerged. The first type relied primarily on departure controls and strategic reroutes on the 300 to 400 nmi look-ahead horizon and worked best when rerouting occurred at a frequency of 10 to 15 minutes. The second type generated more tactical reroutes on the 200 ‐ 300 nmi look-ahead horizon and required little airborne holding or pre-departure control when rerouting occurred at a frequency of 5 minutes. The last type relied heavily on airborne holding controls and infrequent updates to the weather avoidance reroutes. This last type was the least desirable solution due to the impact of its airborne holding on airspace complexity and airspace users.

GPS/machine vision navigation system for aircraft

A Kalman filter based multiple sensor fusion method for determining the position of a general aviation aircraft with respect to the runway during night landing and takeoff is discussed. Known structure of the airport lights, video images acquired by an onboard video camera, position estimates from an onboard Global Positioning System (GPS), and data from an attitude indicator are integrated in a Kalman filtering algorithm. Simulation results are presented to demonstrate the feasibility of the proposed concept.

Passive range estimation for rotorcraft low-altitude flight

The automation of rotorcraft low-altitude flight presents challenging problems in control, computer vision, and image understanding. A critical element in this problem is the ability to detect and locate obstacles, using on-board sensors, and to modify the nominal trajectory. This requirement is also necessary for the safe landing of an autonomous lander on Mars. This paper examines some of the issues in the location of objects, using a sequence of images from a passive sensor, and describes a Kalman filter approach to estimate range to obstacles. The Kalman filter is also used to track features in the images leading to a significant reduction of search effort in the feature-extraction step of the algorithm. The method can compute range for both straightline and curvilinear motion of the sensor. An experiment is designed in the laboratory to acquire a sequence of images along with the sensor motion parameters under conditions similar to helicopter flight. The paper presents range estimation results using this imagery.

Considerations for Automated Nap-of-the-Earth Rotorcraft Flight

In this paper, we consider nap-of-the-earth (NOE) rotorcraft flight as one of the applications in which obstacle avoidance plays a key role, and investigate the prospects of automating the guidance functions of NOE flight. Based on a proposed structure for the guidance functions, we identify obstacle detection and obstacle avoidance as the two critical components requiring substantial advancement before an automatic guidance system can be realized. We discuss the major sources of difficulties in developing these two components, including sensor requirements for which we provide a systematic analysis.

Initial Study of Tube Networks for Flexible Airspace Utilization

Air traffic in the United States is reaching new highs as it continues to increase at a steady pace. There is a general consensus that the National Airspace System needs to be transformed from today’s rigid airways and airspace structure to a more flexible arrangement in order to accommodate and manage this growth. It has been suggested that one way to support this divergent growth in the mix of air traffic is to divide the airspace into different categories with different levels of service and entry requirements based on policy or price; e.g., connect high-traffic regions with a network of dedicated “tubes” analogous to the interstate highway system. This paper starts with today’s air traffic as a baseline, groups airports into regions, and models a series of tubes connecting major regions. We achieve this grouping in two different ways, and present results based on the two methods. Next, we present simulation results by connecting these regions with a network of tubes. The modeling approach provides a basis for systematically studying the design and impact of dynamic airspace concepts in the National Airspace System.

National Airspace System Delay Estimation Using Weather Weighted Traffic Counts

Assessment of National Airspace System performance, which is usually measured in terms of delays resulting from the application of traffic flow management initiatives in response to weather conditions, volume, equipment outages and runway conditions, is needed both for guiding flow control decisions during the day of operations and for post operations analysis. Comparison of the actual delay, resulting from the traffic flow management initiatives, with the expected delay, based on traffic demand and other conditions, provides the assessment of the National Airspace System performance . This paper provides a method for estimating delay using the expected traffic demand and weather. In order to identify the cause of delays, 517 days of National Airspace System delay data reported by the Federal Aviation Administration’s Operations Netw ork were analyzed. This analysis shows that weather is the most important causal factor for delays followed by equipment and runway delays. Guided by these results, the concept of weather weighted traffic counts as a measure of system delay is described. Examples are given to show the variation of these counts as a function of time of the day. The various datasets, consisting of aircraft position data, enroute severe weather data, surface wind speed and visibility data, reported delay data and number of ai rcraft handled by the Centers data, and their sources are described. The procedure for selecting reference days, one for each day of the week on which traffic was minimally impacted by weather, is described. Next, the method for computing the weather weig hted traffic counts, using the expected traffic demand derived from reference days and the expanded regions around severe weather cells, is discussed. It is shown via a numerical example that this approach considerably improves the dynamic range of the wea ther weighted traffic counts. Time histories of these new weather weighted traffic counts are used for synthesizing two statistical features, six histogram features and six time domain features. In addition to these enroute weather features, two surface we ather features -- number of major airports in the United States with high mean winds and low mean visibility are also described. A least squares procedure for establishing a functional relation between the features, using combinations of these features, and system delays is explored using 39 days of data. Correlations between the estimated delays obtained using the least squares procedure with different combinations of features and the actual delays provided by the Operations Network are obtained. The set o f features with the best correlation are computed for 26 additional days (not used in computing the least -squares coefficients) and used in the delay estimation model, previously setup using 39 days of data, for estimating NAS delays. The resulting estimat es are then compared with the OPSNET delays to validate the delay estimation model. Finally, a procedure for using the estimated delays and OPSNET delays to assess NAS performance is described via an example.