Using aircraft trajectory data to infer aircraft intent

Abstract

A method is provided for inferring the aircraft intent of an aircraft from an observed trajectory. Aircraft performance data relating to that type of aircraft is retrieved from memory, along with atmospheric conditions along the observed trajectory. An initial set of candidate aircraft intents is generated. Each aircraft intent provides an unambiguous description of how the aircraft may be flown that allows a determination of an unambiguous resulting trajectory. A computer system calculates a trajectory defined by each candidate aircraft intent and forms a cost function from a comparison of each calculated trajectory to the observed trajectory. An evolutionary algorithm evolves the initial candidate aircraft intents, wherein the evolutionary algorithm uses a multi-objective cost function to obtain a cost function value that measures the suitability of each candidate aircraft intent.

Description

BACKGROUND INFORMATION[0001]1. Field[0002]The present invention relates to a method of providing data relating to the aircraft intent of an aircraft using observations of the aircraft's trajectory. The inferred aircraft intent may be used for predicting the future trajectory of the aircraft, for use in conflict resolution within air traffic management, or for analyzing air traffic management.[0003]2. Background[0004]The ability to describe, and also to predict, an aircraft's trajectory is useful for many reasons. By trajectory, a four-dimensional description of the aircraft's path is meant. The description may be the evolution of the aircraft's state with time, where the state may include the position of the aircraft's center of mass and other aspects of its motion such as velocity, attitude and weight. In order to predict an aircraft's trajectory unambiguously, one must solve a set of differential equations that model both aircraft behavior and atmospheric conditions.[0005]Aircraft...

AI Technical Summary

Benefits of technology

[0018]For example, analysis of the performance of air traffic management may be performed. This may be done to measure efficiency, in terms of throughput, delays to aircraft, fuel efficiency and noise minimization.

[0022]Preferably, the method includes a random element in how the initial set of candidate aircraft intents is generated. For example, the method may further comprise retrieving a set of bounds. Then, the initial set of candidate aircraft intents may be generated to include randomly-generated values that are constrained to remain within the bounds. For example, the randomly-generated values may correspond to values of airspeeds, rates of climb, bank angles, and high lift device settings. The bounds may provide safe or usual limits to these values, such as limiting the airspeeds to those recommended for the aircraft type or bank angles to a range that ensures passenger comfort. Furthermore, the initial set of aircraft intents may be randomly generated while being guided to provide a broad range of candidate aircraft intents. That is values with broad ranges of variation may be produced to ensure diversity in the initial set of candidate aircraft intents.

[0028]Generating the initial set of candidate aircraft intents may comprise, for each candidate aircraft intent, filling each thread with an instruction such that each thread contains only a single instruction spanning the entire trajectory. The calculated trajectories may be divided into flight segments defined by the instructions. That is, the start and end of the flight segments may be defined by the starts and ends of the instructions. For example, the trajectory may be divided according to the instructions; whenever an instruction in any of the threads ends, a break between flight segments may be provided. Not all instructions need necessarily start and end together. However, the division into flight segments allows each flight segment to be defined by the instructions it contains. The instructions of some threads may not change between consecutive flight segments, although at least one instruction must change between flight segments.

[0034]Next, each iteration of the outer loop performs repeated iterations of the inner loop. Each iteration of the inner loop may comprise evolving the further initial set of candidate aircraft intents to form further evolved sets of candidate aircraft intents. The evolution allows the length of the instructions occupying the final flight segment to vary while keeping the start of each instruction tied to the end of the previous instruction. That is, again and again the instructions are allowed to move away from the end of the observed trajectory to define a new flight segment. In this way, actual flight segments in the observed trajectory may be replicated. For example changes in flight segments corresponding to the pilot switching autopilot guidance modes may be replicated. As before, the evolutionary algorithm may use the multi-objective cost function to obtain a cost function value that measures the goodness of each candidate aircraft intent based upon a comparison of the calculated trajectory calculated from the start of the observed trajectory to the end of the final flight segment with the corresponding portion of the observed trajectory. Alternatively, the comparison may be made of the calculated trajectory for the latest flight segment with the corresponding portion of the observed trajectory.

[0036]The use of flight segments allows another metric to be used when forming the cost function. A metric may be included that rewards candidate aircraft intents with fewer flight segments and penalizes candidate aircraft intents with more flight segments. This helps constrain the evolutionary algorithm that may otherwise excessively segment a trajectory to achieve better and better matches between the calculated trajectory and the observed trajectory.

Problems solved by technology

While aircraft intent data may be provided by the aircraft or the aircraft operator, aircraft intent data is not always readily available to other interested parties.

Use of the first metric will drive the candidate aircraft intents to produce trajectories with the lowest average deviation, but this may produce trajectories with very low deviations in some parts and very high deviations in other parts which may not be desirable.

As each thread is then completely defined, and as the threads together define all degrees of freedom of the aircraft, this method necessarily closes all degrees of freedom of the aircraft throughout the observed trajectory and so gives rise to an unambiguous trajectory.

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