A Final Approach Trajectory Model for Current Operations

Gong, Chester; Sadovsky, Alexander V.

doi:10.2514/6.2010-9117

Cited by 12 publications

(6 citation statements)

References 5 publications

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“…Our approach is to represent the state of the moving target by time-dependent polynomials. Because a trajectory can be a very complex curve, we rely on polynomial fitting to model a predicted reference trajectory [29].…”

Section: Intruder Trajectory Predictionmentioning

confidence: 99%

Conflict-Free Trajectory Optimization with Target Tracking and Conformance Monitoring

Vilardaga¹,

Prats²,

Duan³

et al. 2018

Journal of Aircraft

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This paper proposes an optimisation framework that computes conflict free optimal trajectories in dense terminal airspace, while continuously monitoring trajectory conformance in an effort to improve predictability. The objective is to allow, as much as possible, continuous vertical trajectory profiles without impacting negatively on airspace capacity. Given ADS-B (Automatic Dependent Surveillance-Broadcast) intent information, we predict the future state of potential intruder aircraft and use this nominal trajectory as a constraint in the ownship trajectory optimisation process. In it, a continuous multiphase optimal control problem is solved, taking into account spatial and temporal constraints. Additionally, a linearised Kalman filter keeps track of the target by estimating the deviations of its actual trajectory from its nominal trajectory, issuing a warning when an appropriate threshold is exceeded. This may be due to unexpected events, biases in the performance and weather models, wrong parameter assumptions, etc. An illustrative example is given, based on a computer simulation of two hypothetical trajectories in the Barcelona terminal manoeuvring area. The results show how this framework resolves the problem of uncertainties in the trajectory predictions and results in a more efficient conflict resolution.

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Section: Intruder Trajectory Predictionmentioning

confidence: 99%

Conflict-Free Trajectory Optimization with Target Tracking and Conformance Monitoring

Vilardaga¹,

Prats²,

Duan³

et al. 2018

Journal of Aircraft

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“…Recently, Gong and Sadovsky investigated the performance of compression monitoring along the final approach course using a simple constant ground speed profile (i.e., dead-reckoning) and an empirical ground speed profile mined from a database of historical operations. 9 Although empirical speed profiles of sufficiently small sub-populations of flights might lead to better trajectory predictions, such speed profiles are the least feasible given the capabilities of today's air traffic automation systems. This study complements Gong and Sadovsky's work by modeling several analytical speed profiles that are feasible in today's automation systems.…”

Section: Fig 3 Examples Of Automated Terminal Proximity Alert Warninmentioning

confidence: 99%

Comparison of Trajectory Synthesis Algorithms for Monitoring Final Approach Compression

Robinson

Diallo

Reisman

2011

11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference

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This study analyzes the performance of aircraft in-trail separation monitoring algorithms using 480,000 flights on the final approach courses of 25 major airports in the National Airspace System. While compression monitoring is expected to help air traffic controllers achieve and maintain higher arrival rates, the trajectory prediction requirements for it are not well understood. To address this gap, analytical trajectories were constructed from flight plan and track data for flights arriving at the 14 major and 11 satellite airports of the 8 busiest terminal areas. Three types of analytical trajectory models were compared. These trajectory models were a constant speed model, and two heuristic deceleration models. The trajectory prediction accuracy and separation prediction accuracy of each of these models were calculated for all aircraft pairs along the final approach course. The results were used to rank the overall performance of the various trajectory models in terms of the true and false alerts by the compression monitoring algorithms. The best performing trajectory model enforced the landing speed constraint, used a landing speed based upon weight class, and did not adjust the landing speed by airport elevation. All of the trajectory models exhibited significantly more false alarms when excess in-trail separation was less than 0.5 nm.

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“…Additionally, a fixed throttle setting is assumed throughout the climbing phase. Because the optimisation solver requires all variables to be modelled as twice-differential continuous functions, we model the intruder's trailing trajectory following polynomials that depend on the time [22]. To this end, we store the current and past states (from ADS-B) to form a trailing trajectory, which is then approximated with curves represented by basis splines (B-splines).…”

Section: Mass Estimationmentioning

confidence: 99%

Mass Estimation for an Adaptive Trajectory Predictor using Optimal Control

Vilardaga¹,

Prats²

2015

Proceedings of the 5th International Conference on Application and Theory of Automation in Command and Control Systems

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Air traffic predictability is paramount in the air traffic system in order to enable concepts such as Trajectory Based Operations (TBO) and higher automation levels for selfseparation. Whereas in simulated environments 4D conflictfree trajectory optimisation has shown good potential in the improvement of air traffic efficiency, its application to real operations has been very challenging due to the current lack of information sharing between airspace users. Consequently, such operations are still very limited in scope and rarely attempted in dense traffic situations. Better predictability of other traffic future states would be an enabler for each aircraft to fly its user preferred route without decreasing safety in a self-separation context. But this is not an easy task when basic aircraft parameters such as aircraft weight, performance data or airline strategies are not available at the time of prediction. In this paper the authors propose to compensate this hindrance by continuously integrating the state of the surounding traffic to improve the ownship's knowledge of other aircraft's dynamics. Specifically, conventional position (and velocity) messages, as coming from Automatic Dependent Surveillance Broadcast (ADS-B), are integrated at the ownship. Then, an optimisation problem is formulated, using optimal control theory, that minimises the error with the known states, having the parameters of study (i.e. mass) as decision variables. A scenario with two departing trajectories is used to demonstrate the effectiveness of this parameter estimation method. In it, the take-off mass of the potential intruder is estimated onboard the ownship and its impact to conflict detection and resolution is presented, demonstrating the big improvements in predictability and safety.

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A Final Approach Trajectory Model for Current Operations

Cited by 12 publications

References 5 publications

Conflict-Free Trajectory Optimization with Target Tracking and Conformance Monitoring

Conflict-Free Trajectory Optimization with Target Tracking and Conformance Monitoring

Comparison of Trajectory Synthesis Algorithms for Monitoring Final Approach Compression

Mass Estimation for an Adaptive Trajectory Predictor using Optimal Control

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