BACKGROUND OF THE INVENTION
The present invention generally relates to methods and systems for j
managing air traffic. More particularly, aspects of this invention include methods {
and systems for predicting trajectories of aircraft using models that may be
adapted via tunable parameters. Those parameters may have direct physical j
^ meaning (for example, weight) or they may be abstract, as in the case of the ratio j
of two physical variables such as the ratio of thrust to mass. Accurate trajectory f
prediction is key to a number of air traffic control and trajectory management applications, and the ability to infer parameters helps to improve the level of
prediction accuracy. The trajectory prediction methods and systems are
preferably capable of making use of automation systems of the Air Navigation
System Provider (ANSP) or of the Operations Control Center (OCC).
Trajectory-Based Operations (TBO) is a key component of both the US
Next Generation Air Transport System (NextGen) and Europe's Single European
Sky ATM Research (SESAR). There is a significant amount of effort underway in [
^ both programs to advance this concept. Aircraft trajectory synchronization and
trajectory negotiation are key capabilities in existing TBO concepts, and provide
the framework to improve the efficiency of airspace operations. Trajectory I
synchronization and negotiation implemented in TBO also enable airspace users
(including flight operators (airlines), flight dispatchers, flight deck personnel,
Unmanned Aerial Systems, and military users) to regularly fly trajectories close to
their preferred (user-preferred) trajectories, enabling business objectives,
including fuel and time savings, wind-optimal routing, and direction to go around
weather cells, to be incorporated into TBO concepts. As such, there is a desire
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to generate technologies that support trajectory synchronization and negotiation, which in turn are able to facilitate and accelerate the adoption of TBO.
As used herein, the trajectory of an aircraft is a time-ordered sequence
of three-dimensional positions an aircraft follows from takeoff to landing, and can
be described mathematically by a time-ordered set of trajectory vectors. In
contrast, the flight plan of an aircraft will be referred to as information - either
physical documents or electronic - that is filed by a pilot or a flight dispatcher
A with the local civil aviation authority prior to departure, and include such
information as departure and arrival points, estimated time en route, and other
general information that can be used by air traffic control (ATC) to provide
tracking and routing services. Included in the concept of flight trajectory is that
there is a trajectory path having a centerline, and position and time uncertainties
surrounding this centerline. Trajectory synchronization may be defined as a
process of resolving discrepancies between different representations of an
aircraft's trajectory, such that any remaining differences are operationally
insignificant. What constitutes an operationally insignificant difference depends
on the intended use of the trajectory. Relatively larger differences may be
acceptable for strategic demand estimates, whereas the differences must be
much smaller for use in tactical separation management. i
An overarching goal of TBO is to reduce the uncertainty associated |
with an aircraft's future location through use of an accurate four-dimensional I
trajectory (4DT) in space (latitude, longitude, altitude) and time. The use of i
precise 4DTs resulting from improved trajectory predictions has the ability to f
dramatically reduce the uncertainty of an aircraft's future flight path, including the
ability to predict arrival times at a geographic location (referred to as metering fix, |
arrival fix, or comerpost) for a group of aircraft that are approaching their arrival {
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airport. Such a capability represents a significant change from the present
"clearance-based control" approach (which depends on observations of an
aircraft's current state) to a trajectory-based control approach, with the goal of
allowing an aircraft to fly along a user-preferred trajectory. Thus, a critical
enabler for TBO is not only the availability of an accurate, planned trajectory (or
possibly multiple trajectories) and providing ATC with valuable information to
allow more effective use of airspace, but also more accurate trajectory predictors
that, if used in conjunction with appropriate Decision Support Tools (DSTs),
A would allow ATC to trial-plan different alternative solutions to address requests
filed by airspace users while meeting ATC constraints. Another enabler of TBO
is the ability to exchange data between aircrafts and ground. Several air-ground
communication protocols and avionics performance standards exist or are under
development, for example, controller pilot data link communication (CPDLC) and
automatic dependent surveillance-contract (ADSC) technologies.
There exist a number of trajectory modeling and trajectory prediction
frameworks and tools that have been proposed and that are currently in use in
automation systems in air and on the ground, for instance, those described in
WO 2009/042405 A2 entitled "Predicting Aircraft Trajectory," US7248949 entitled
"System and Method for Stochastic Aircraft Flight-Path Modeling," and US
£ 2006/0224318 A1 entitled "Trajectory Prediction." However, these trajectory
modeling and trajectory prediction methods and systems do not disclose any
capabilities for deriving or inferring parameters that are not available or known in
explicit form, yet would be needed by trajectory predictors to achieve a higher
degree of prediction accuracy. Improved prediction accuracies require better
knowledge of the performance characteristics of an aircraft. However, in some
cases, performance information cannot be shared directly with ground
automation because of concerns related to information that is considered
4
strategic and proprietary to the operator. Two typical examples of this category
are aircraft weight and cost index. In other cases, the bandwidth of air-ground
communication systems used to communicate relevant performance parameters
is often constrained.
Other significant gaps remain in implementing TBO, due in part to the
lack of validation activities and benefits assessments. In response, the General
Electric Company and the Lockheed Martin Corporation have created a Joint
A Strategic Research Initiative (JSRI), which aims to generate technologies
intended to accelerate the adoption of TBO in the Air Traffic Management (ATM)
realm. Efforts of the JSRI have included the use of GE's Flight Management
System (FMS) and aircraft expertise and the use of Lockheed Martin's ATC
domain expertise, including the En Route Automation Modernization (ERAM) and
the Common Automated Radar Terminal System (Common ARTS), to explore
and evaluate trajectory negotiation and synchronization concepts. Ground
automation systems typically provide trajectory predictors capable of predicting
the paths of aircraft in time and space, providing information that is required for
planning and performing critical air traffic control and traffic flow management
functions, such as scheduling, conflict prediction, separation management and
conformance monitoring. On board an aircraft, the FMS can use a trajectory for
£ closed-loop guidance by way of the automatic flight control system (AFCS) of the
aircraft. Many modern FMSs are also capable of meeting a required time-ofarrival
(RTA), which may be assigned to an aircraft by ground systems.
Notwithstanding the above technological capabilities, questions remain
related to Trajectory-Based Operations, including the manner in which
parameters needed by trajectory predictors may be obtained from available
information, for instance, from downlinked information, to guarantee an efficient
5 air traffic control process where users meet their business objectives while fully
honoring all ATC objectives (safe separation, traffic flow, etc.). In particular,
there is a need for enabling ground automation systems to increase their
prediction accuracy by having the ability to obtain key parameters used by the
trajectory predictor, for instance, those related to an aircraft's performance.
However, aircraft and engine manufacturers consider detailed aircraft
performance data proprietary and commercially sensitive, which may limit the
availability of detailed and accurate aircraft performance data for ground
A automation systems. Moreover, the aircraft thrust, drag, and fuel flow
characteristics can vary significantly based on the age of the aircraft and time
since maintenance, which ground automation systems will likely not know or be
able to explicitly obtain. In some cases, aircraft performance information, such
as gross weight and cost index, cannot be shared directly with ground automation because of concerns related to information that is considered
strategic and proprietary to the operator. Even if these performance parameters
were shared directly, because the aircraft performance model used by the aircraft
and ground automation systems may be significantly different, they may actually
decrease the accuracy of the ground trajectory prediction if used directly.
In addition to the above, the ability of ground automation systems to
0 increase their prediction accuracy is further complicated by increasing levels of
air traffic combined with the need to support more efficient airspace operations,
the impact of potential revisions in the aircraft flight plan or airspace constraints,
and constraints on bandwidth for communicating relevant performance
parameters.
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BRIEF DESCRIPTION OF THE INVENTION I
The present invention provides a method and system that are suitable
for inferring trajectory predictor parameters and, in some instances, capable of
utilizing available air-ground communication link capabilities, which may include
data link capabilities available as part of planned aviation system enhancements.
This invention also considers current operations in which the utilization of voice
communications is more prevalent. Methods and systems of this invention f
A preferably enable ground automation systems to increase their prediction
accuracy by inferring key parameters used by its trajectory prediction algorithms,
even when the aircraft performance models used by the aircraft and ground
trajectory predictors do not map directly.
According to a first aspect of the invention, the method includes
receiving trajectory prediction information regarding an aircraft, and then using |
this information to infer (extract) trajectory predictor parameters of the aircraft
that are otherwise unknown to a ground automation system. In preferred ?
embodiments of the invention, the trajectory predictor parameters can then be applied to one or more trajectory predictors of the ground automation system to
predict a trajectory of the aircraft.
•
According to a preferred aspect of the invention, parameter estimation |
techniques, such as Bayesian inference, may be applied to recursively improve j
t
prior information about the unknown trajectory predictor parameters. Trajectory I
predictor parameters of an aircraft can be estimated by comparing trajectory prediction information predicted for the aircraft (for example, from an accurate t
model normally available from an aircraft's onboard trajectory predictor) to a set f
of trajectory prediction information generated by another trajectory predictor. The
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set of trajectory prediction information can be generated by varying the
parameter inputs to be estimated over likely values, after which the parameter
estimates can be updated based upon the comparison. Hence, previous
knowledge about the unknown trajectory predictor parameters, even though
riddled with high uncertainty, may be used if these techniques are applied.
Another preferred aspect of the invention involves the use of a probability density
function (PSD) and an update process to estimate and refine the estimate of the
trajectory predictor parameters of the aircraft.
Other aspects of the invention include systems adapted to carry out
the methods and steps described above.
A technical effect of the invention is the ability to infer trajectory
predictor parameters of an aircraft to significantly improve the accuracy of
ground-based trajectory predictors. While the use of surveillance and measured
data relating to the performance of an aircraft can be incorporated into the method described above for the purpose of predicting the aircraft's trajectory, the
present invention does not solely rely on the use of surveillance and measured [
data, as has been the case with prior art systems and methods that attempt to |
predict aircraft trajectories. In any event, the ability to significantly improve the I
4 ) accuracy of ground-based trajectory predictors with this invention can then be [
translated into better planning capabilities, especially during the stages of flight j
which require better knowledge of those parameters, for instance while executing
Continuous Descent Arrivals (CDAs). Other potential advantages enabled by the j
parameter inference process of this invention include reduced bandwidth utilization of air-ground communication systems and an improved capability for predicting costs associated with specific maneuvers, which may enable ATC
systems to generate maneuver advisories with consideration of cost incurred by
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the aircraft.
Other aspects and advantages of this invention will be better
appreciated from the following detailed description. I
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram of a parameter inference process for
^ predicting four-dimensional trajectories of aircraft within an airspace in
accordance with a preferred aspect of this invention.
FIG. 2 is a graph containing three curves that evidence a dependency
of the along-route distance of an aircraft corresponding to the aircraft's top of
climb (T/C) point on the takeoff weight of an aircraft.
FIG. 3 qualitatively depicts a parameter update process that can be
employed by the invention.
DETAILED DESCRIPTION OF THE INVENTION
A The invention describes methods and systems for inferring aircraft
performance parameters that are otherwise unknown to ground automation
systems. The performance parameters are preferably derived from aircraft state I
data and trajectory intent information provided by the aircraft operator via a [
communication link, which may be voice and/or data. In particular, methods and systems of this invention may utilize data link capabilities if available, including [
those data link capabilities that may be available as part of planned aviation ;
I
system enhancements. Methods and systems of this invention may also [
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consider current operations where the utilization of voice communications is
more prevalent, in which case useful information may include key trajectory
change points commonly transmitted by pilots via voice, such as the location of
the Top of Descent (ToD) point with respect to the metering fix or the location of
the Top of Climb with respect to the wheels-off point. In addition, surveillance
information may be used to improve the inference process. The inferred
parameters are employed for modeling aircraft behavior using ground automation
systems for such purposes as trajectory prediction, trial planning, and predicting
^ aircraft operational costs.
As previously discussed, Air Traffic Management (ATM) techniques
rely on the projection of an aircraft's state into the future in four dimensions -
latitude, longitude, altitude and time (4DT). The 4DT of an aircraft may be used
to detect potential problems with the aircraft's planned flight, such as a predicted loss of separation standards between multiple aircraft, and potential problems
concerning the ability of assigned air traffic control resources to safely handle a
large number of aircraft in a given airspace. When such problems are detected,
the present invention can be employed to infer otherwise unknown aircraft
performance parameters, from which one or more trial or "what if trajectories can
be predicted for an aircraft and used to evaluate the impact of potential
A modifications to the flight plan or trajectory, to determine whether those other
4DTs may be capable of alleviating the particular problem in a safe and efficient
manner. The inferred aircraft performance parameters allow ground automation •
systems to improve the accuracy of the performance models of the aircraft
beyond what is otherwise available and commonly used, which allows air traffic
control to more accurately perform trajectory predictions and trial planning.
Notably, predictor methods and systems with access to such performance models increase the accuracy of the predicted trajectory and allow the
10 J
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incorporation of aircraft operational cost considerations in the trial planning I
process.
FIG. 1 schematically represents a parameter inference process and J
system according to one aspect of the present invention. In this diagram, all
blocks show functions that may be performed on a ground system. For example, I
they could reside at an air traffic control center or at an airline operations center.
The ground system receives information from the aircraft related to the predicted
^ trajectory. If this information comes directly from the aircraft, the information may
be transmitted via a data transmission link, such as ADS-C (Automatic
Dependence Surveillance Contract). The elements of the transmitted data may
be obtained from the "Trajectory Intent Bus" of the Flight Management Computer
(FMC), defined in the standard ARINC702A-3. It is also foreseeable that this
information may originate at the airline operations center, in which case the
information may be communicated to air traffic control via a ground-based
network similar to those already in use for collaborative air traffic control ;
purposes and for filing flight plans. Furthermore, information may also be
transmitted via voice communications, in which case data may comprise some
elements that define the aircraft trajectory, examples of which are: a Required
Time of Arrival (RTA) at the metering fix keyed into the FMC, a trajectory change A point (Top of Climb, Top of Descent, etc.) or parameters keyed into the Mode
Control Panel. The information itself may be divided into two groups: 1) inputs to
the trajectory prediction process (u ), such as speed schedules, assumed winds,
T A
etc., and 2) outputs, more specifically the predicted vertical profile ( c) or some
of its elements. The vertical profile or some of its elements used in the
parameter inference process are assumed to be constructed using detailed f
information about performance-related parameters that are often not known by |
the ground automation system and thus need to be inferred. The extraction of I
1 1 (
the vertical profile information is represented by a dedicated block in the diagram.
Alternatively, this step may be performed by the aircraft, in which case the i
vertical profile would be provided directly to the ground automation system. The
downlinked vertical profile may be represented by a set of » three-dimensional
points, consisting of time, along-route distance and altitude.
TA = [XA =(t,,dj,h,);j = l...nl
The parameters that need to be inferred are initialized in a process
^ represented by the block "Parameter Initialization." In the parameter inference
process all parameters are represented by a probability density function (PDF),
which could be of any nature (Gaussian, uniform, etc.). Furthermore, in one
particular instantiation of the method presented in this invention, the PDF may be
approximated by random samples, also known as "particles." Hence, parameters
may be initialized as a particle ensemble 0o, also referred to as "belief,"
according to:
0o = {<0i.wj);i = l...Ns}
Each of the N* random samples constitutes a hypothesis as to what
the parameters (^e ) of the system could be, associated with a weight proportional
A to their probability (wo). For instance, for the parameter take-off mass m ,
depending on the type of aircraft, the aircraft mass can only have a specific
range of values specified by the manufacturer, for example, between m*m and *»MAX. If at the beginning of the process this range is the only information
available to the parameter inference process, and if take-off mass was the only
parameter to be inferred, the samples of the PDF would be distributed according
to a uniform distribution spanning all the possible values within that range: I
(•eto^U(m4,MIN.miMAX) . |n this illustrative example, weights of the particles 12 I
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would be initialized with the value Ns conforming to the uniform distribution. As I
shown in FIG. 1, other sources of information, such as the flight plan, may be also used to initialize the PDF associated with aircraft mass, assigning higher probability to values that would better match flight length and fuel reserve
regulations. Statistical information collected over time could be also used to •
initiate the process. These parameters become part of the aircraft performance
model that can be used by the ground-based trajectory predictor.
W The trajectory predictor itself, which runs in fast-time mode, is used in
the parameter inference process. First, it generates a set of trajectories TGNDJI
corresponding to all samples in the belief 0k. ©k denotes the state of the
estimation at the k th step of the inference process. The weighting function
w = fw(0) computes weights for each trajectory TGNDjtin the ensemble TGNDJI.
There are several alternatives for weight calculation, one of which involves
assigning a probabilistic interpretation to the downlinked trajectory used as
TA
reference ( c). The calculated weight is then proportional to the probability of [
T i TA •
trajectory points in ^GND* being in c. In one case, when single trajectory points
are processed one at a time, the weight of each particle "i" may be calculated as:
( c)
Alternatively, trajectory points may be calculated all at once. Hence,
weights would be proportional to the total probability of all n trajectory points in
?•
TGNDJI being in c: f
I
l
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Wk«npH««>*kGl\:} i
pfv' g T. 1
[0025] One possibility for computing *• CND*k k* involves assuming a
Gaussian spread around the trajectory A/c, defining: a distance metric
V GND*k' A/C; (distance from point XGND,JJI to trajectory A/c), and a measure of
spread <* . Then:
Actual weights can be computed by normalizing w'k
To speed up computations alternative distributions such as the
triangular distribution could be used to determine particle weights.
A The next step in the parameter estimation process involves
determining the updated parameter belief from previously calculated weights and
belief. In the diagram, this step is shown as "Parameter Update Process."
Following on the illustrative example using a particle representation of belief, this
step may be performed applying importance resampling, which consists of
generating a new set of particles ©k by drawing samples from the original set
©k-i with a probability proportional to their weight wh. The process of constant
refinement of the parameters to be estimated is continued as updated predictions
14 I
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are obtained from the aircraft, and/or as surveillance and measured data
(measured track and state data) of the aircraft become available.
FIG. 3 depicts in a qualitative manner the parameter update process
starting from a sampled uniform distribution and arriving at a unimodal
distribution, from which the most likely estimate could be derived as well as a
measure of confidence. Major steps of the parameter inference process such as
weighting and resampling may be observed from this diagram.
w It is important to note that parameters do not have to be
unidimensional. The use of the take-off mass of the aircraft as the main
parameter to be inferred is just for illustration. Extending the vector of
parameters to be estimated to include takeoff mass and, for instance, cost index
ka is simple. Analogously, Monte Carlo sequential estimation can be used to
illustrate the parameter inference process. Alternatively, another Bayesian
estimation-type of technique that uses a different representation of belief could
be applied, for example histograms, grids, or even parametric representations
(e.g.: Gaussian) instead of particles, when appropriate. I
The parameter inference process and system represented in FIG. 1
addresses issues arising from the fact that, in practice, many aircraft are unable
w to provide some or all of the data required to accurately predict their 4DT
trajectories because the aircraft are not properly equipped or, for businessrelated
reasons, flight operators have imposed restraints as to what information I
can be shared by the aircraft. Under such circumstances, the parameter
inference process and system represented in FIG. 1 can be used by an ATC
system to compute and infer some or all of the data relating to aircraft
performance parameters required for accurate trajectory prediction. Because
fuel-optimal speeds and in particular the predicted 4DT are dependent on data
15
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relating to aircraft performance parameters to which the ATC system does not
have access (such as aircraft mass, engine rating, and engine life), certain data
that can be provided by appropriately equipped aircraft are expected to be more
accurate than data inferred or otherwise generated by the ATC system.
Therefore, the parameter inference process and system is preferably adapted to
take certain steps to enable the ATC system to more accurately infer data
relating to aircraft performance characteristics that will assist the ATC system in
predicting other aircraft performance data, including fuel-optimal speeds,
^ predicted 4DT, and factors that influence them when this data is not provided
from the aircraft itself. As explained below, the aircraft performance parameters
of interest will be derived in part from aircraft state data and trajectory intent
information typically included with data provided by the aircraft via a
communication datalink or via voice. Optionally or in addition, surveillance
information can also be used to improve the inference process. The inferred
parameters are then used to model the behavior of the aircraft by the ATC
system, specifically for trajectory prediction purposes, trial planning, and
estimating operational costs associated with different trial plans or trajectory
maneuvers.
In order to predict the trajectory of an aircraft, the ATC system must
A rely on a performance model of the aircraft that can be used to generate the
current planned 4DT of the aircraft and/or various "what if 4DTs representing
unintentional changes in the flight plan for the aircraft. Such ground-based
trajectory predictions are largely physics-based and utilize a model of the
aircraft's performance, which includes various parameters and possibly
associated uncertainties. Some parameters that are considered to be general to
the type of aircraft under consideration may be obtained from manufacturers' specifications or from commercially available performance data. Other specific
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parameters that tend to be more variable may also be known, for example, they
may be included in the filed flight plan or provided directly by the aircraft
operator. However, other parameters are not provided directly and must be
inferred by the ATC system from information obtained from the aircraft and
optionally, from surveillance information. The manner in which these parameters
can be inferred is discussed below.
Aircraft performance parameters such as engine thrust, aerodynamic
^ drag, fuel flow, etc., are commonly used for trajectory prediction. Furthermore,
these parameters are the primary influences on the vertical (altitude) profile and
speed of an aircraft. Thus, performance parameter inference has the greatest
relevance to the vertical portion of the 4DT of an aircraft. However, the aircraft
thrust, drag, and fuel flow characteristics can vary significantly based on the age
of the aircraft and time since maintenance, which the ATC system will not likely
know. In some cases, airline performance information such as gross weight and
cost index cannot be shared directly with ground automation because of
concerns related to information that is considered strategic and proprietary to the
operator.
In view of the above, a parameter initialization process is required for
A the inference process of this invention. It has been determined that thrust during j
the climb phase of an aircraft may be assumed to be known within a certain
range, with variations subject mainly to derated power settings. This uncertainty
may be taken into account by actually defining a statistical model for thrust which
considers three different derating settings. FIG. 2 plots three curves expressing
the dependency of the along route distance (T/C Dist) corresponding to the top of
climb (T/C) point as a function of takeoff weight (TWO). The calculations
represented by FIG. 2 have been performed with a simulated Flight Management
17
System (FMS). The curves represent three possibilities of specific climb modes:
"Maximum Climb," "Climb Derate 1" and Climb Derate 2," as specified in the
information entered into an aircraft's FMS. As observed from FIG. 2, there is a
direct dependency between the distance to top of climb and TOW up to a certain
value of TOW. For a given T/C Dist prediction, and in case that the climb mode
is not known, there is a range of possible TOW values. Uncertainty in the T/C
Dist estimate also generates additional uncertainty in the TOW. For example,
around the middle of the curve, uncertainty in T/C Dist of 5nmi translates into an
^ uncertainty of 6klb in TOW, considering unknown climb mode. A weight range is
also known from the aircraft manufacturer specifications, which may be further
enhanced with knowledge originating from the filed flight plan and from applicable regulations (distance between airports, distance to alternate airport,
minimum reserves, etc.).
Additional inputs to the prediction model but needed for the inference
process, including aircraft speeds, assumed wind speeds and roll angles, can be
derived from lateral profile information and used to predict a vertical profile for the
aircraft. Such inputs can be downlinked from an aircraft, and can typically be
obtained from information already available in modern flight management
systems (ARINC 702A), for example, in the so-called intent bus. Downlinked
A information may be partitioned into two major pieces: inputs to the trajectory
predictor; and predicted vertical profile.
In view of the above, the present invention is able to use knowledge of
an aircraft's predicted trajectory during takeoff and climb to infer the takeoff
weight (mass) of the aircraft. If an estimate of the aircraft's fuel flow is available,
this can be used to predict the weight of the aircraft during its subsequent »
operation, including its approach to a metering fix. Subsequent surveillance and
18
measured data, for example, track and state data including measurements of the
aircraft state (such as speeds and rate of climb or descent) relative to the
predicted trajectory can be used to refine the estimate of the fuel flow and
predicted weight. The weight of the aircraft can then be used to infer additional
data relating to aircraft performance parameters, such as the minimum fuel-cost
speed and predicted trajectory parameters of the aircraft, since they are known to
depend on the mass of the aircraft. As an example, the weight of the aircraft is
inferred by correlating the takeoff weight of the aircraft to the distance to the top
^ of climb that occurred during takeoff. A plurality of generation steps can then be
used to predict a vertical profile of the aircraft during and following takeoff. Each
generation step comprises comparing the predicted altitude of the aircraft
obtained from one of the generation steps with a current altitude of the aircraft i
reported by the aircraft. The difference between the current and predicted
altitudes is then used to generate a new set of inferred parameters based on
prior information (in the first cycle) or based on previous inference results.
When obtained from an aircraft, new information can be used to update the latest
inferred parameters in a sequential process. The latest inferred parameters are I
then fed into the aircraft performance model used by the trajectory predictor.
While the invention has been described in terms of specific
£ embodiments, it is apparent that other forms could be adopted by one skilled in
the art. For example, the functions of components of the parameter inference
system and process could be performed by different components capable of a
similar (though not necessarily equivalent) function. Therefore, the scope of the
invention is to be limited only by the following claims.

WE CLAIM:
1. A method of inferring aircraft performance parameters capable of
being used by a trajectory predictor to predict trajectories of an aircraft, the
method comprising:
receiving trajectory prediction information regarding an aircraft; and
then
using the trajectory prediction information to infer trajectory predictor
^ parameters of the aircraft that are otherwise unknown to a ground automation
system.
2. The method of claim 1, wherein the trajectory prediction information
regarding the aircraft is transmitted from the aircraft.
3. The method of claim 2, wherein the receiving step comprises the
use of a communication link between the aircraft and the ground automation
system.
4. The method of claim 1, wherein the trajectory prediction information
comprises a relative location of at least one trajectory change point of the aircraft.
5. The method of claim 4, wherein the aircraft performance
parameters comprise takeoff weight of the aircraft inferred from the relative
location of the at least one trajectory change point, and the at least one trajectory
change comprises at least one of the top of climb or top of descent.
6. The method of claim 1, the method further comprising applying the
trajectory predictor parameters to one or more trajectory predictors of the ground
20
automation system to predict a trajectory of tlie aircraft.
7. Tlie metliod of claim 1, wherein tlie using step comprises
estimating at least one of the trajectory predictor parameters of the aircraft by
comparing the trajectory prediction information of the aircraft to a set of trajectory
prediction information that was generated with a trajectory predictor by varying
the trajectory predictor parameters of the aircraft over likely values, and then
updating the at least one trajectory predictor parameter based on the
^ comparison.
8. The method of claim 1, wherein the using step further comprises
using surveillance and measured data of the aircraft to infer the trajectory
predictor parameters of the aircraft.
9. The method of claim 1, wherein the using step further comprises
the use of a probability density function and updating process to estimate and
refine the trajectory predictor parameters of the aircraft.
10. A system for inferring aircraft performance parameters used by a
trajectory predictor to predict trajectories of the aircraft, the system comprising:
^ means for receiving trajectory prediction information regarding an
aircraft; and
means for using the trajectory prediction information regarding the
aircraft to infer trajectory prediction parameters of the aircraft that are otherwise
unknown to a ground automation system.
11. The system of claim 10, further comprising means for transmitting
the trajectory prediction information regarding the aircraft from the aircraft.
21
12. The system of claim 11, wherein the receiving means comprises a
communication linl^ between the aircraft and the ground automation system.
13. The system of claim 10, wherein the trajectory prediction
information comprises a relative location of at least one trajectory change point of
the aircraft.
^ 14. The system of claim 13, wherein the aircraft performance
parameters comprise takeoff weight of the aircraft inferred from the relative
location of the at least one trajectory change point.
15. The system of claim 10, the system further comprising means for
applying the aircraft performance parameters to one or more trajectory predictors
of the ground automation system to predict a trajectory of the aircraft.
16. The system of claim 10, wherein the using means comprises
means estimating at least one of the trajectory predictor parameters of the
aircraft by comparing the trajectory prediction information of the aircraft to a set
of trajectory prediction information that was generated with a trajectory predictor
A by varying the trajectory predictor parameters of the aircraft over likely values,
and means for updating the at least one trajectory predictor parameter based on
the comparison.
17. The system of claim 10, wherein the using means further
comprises means for receiving and using surveillance and measured data of the
aircraft to infer the trajectory predictor parameters of the aircraft.
22
18. The system of claim 10, wherein the using means further
comprises means for performing a probability density function and updating
process to estimate and refine the trajectory predictor parameters of the aircraft.