An aircraft mass estimation method based on intent level

Abstract

The application provides an aircraft mass estimation method based on an intended height. Wind speed of each track point and the influence of wind on aircraft mass estimation are calculated by interpolation; the height difference between barometric height and intended height is compared with the threshold value to filter abnormal track points affecting mass estimation; a mass sequence is set, mass error under different masses is calculated, and the mass with the minimum error is taken as the estimated mass of the aircraft; a height layer sequence is set, the estimated mass of the aircraft under different height layers is calculated, data verification is performed, abnormal mass is removed, and the mean value of the normal mass is taken as the stable estimated mass of the aircraft. The application fully considers the influence of wind on aircraft mass estimation, filters track points according to intended height and height buffer time, removes abnormal track points affecting the mass result, and obtains more stable aircraft estimated mass through implementation of aircraft mass estimation at multiple height layers. The application has wide application prospects in the field of flight four-dimensional trajectory prediction.

Description

Technical Field

[0001] This invention relates to an aircraft mass estimation method, and more particularly to an aircraft mass estimation method based on intended altitude. Background Technology

[0002] Aircraft trajectory prediction has always been a key issue in many airborne and ground applications in air transport. With the promotion of the trajectory-based operation concept, current air traffic management and control systems are shifting towards trajectory-based operation, and high-precision prediction of aircraft trajectories has become a critical issue in air traffic management and control.

[0003] Currently, most methods for trajectory prediction are based on point mass models and aerodynamics. These methods simulate the aircraft as a point with mass, apply Newton's second law to analyze the forces acting on the aircraft, and use a set of differential equations to link the aircraft's thrust, drag, gravity, and inertial acceleration. Knowing the aircraft's initial state (mass, current thrust setting, position, velocity, tilt, etc.), atmospheric conditions (wind, temperature), and the aircraft's intentions (thrust curve, velocity curve, flight path), the future velocity and altitude are obtained through integral calculations. Among these, the mass parameter is a key parameter affecting the accuracy of trajectory prediction. Because many airlines consider aircraft mass an important competitive parameter and do not allow the onboard system to transmit mass information, the ground system cannot obtain accurate aircraft takeoff mass. Therefore, the current common practice is to use the reference mass provided by BADA or to estimate the mass using the least squares method based on preceding track points as the aircraft mass. The reference mass provided by BADA is a fixed value determined by the aircraft type and cannot dynamically reflect the mass deviation between different aircraft. The estimated mass calculated by the least squares method is greatly affected by the preceding waypoints, and the result fluctuates significantly, failing to provide stable aircraft mass. Summary of the Invention

[0004] Purpose of the invention: The technical problem to be solved by the present invention is to provide an aircraft mass estimation method based on the intended altitude, which addresses the shortcomings of the prior art.

[0005] To address the aforementioned technical problems, this invention discloses an aircraft mass estimation method based on intended altitude, comprising the following steps:

[0006] Step 1: Analyze the S-mode radar data of the target aircraft to obtain the dataset TP = {tp} of the aircraft at each waypoint. i ,i=1,2,...,N}, where tp i This is the data for the i-th waypoint;

[0007] Analyzing the meteorological data, we obtain the meteorological data set WD = {wind}j ,j=1,2,...,M}, where, wind j Represents the j-th wind vector;

[0008] Step 2, interpolate and calculate waypoint data tp i Wind speed components in the U and V directions in the corresponding UV coordinate system (wind_u) i and wind_v i ;

[0009] Step 3, set the height threshold η h Calculate the altitude difference Δh between the barometric altitude Hp and the intended altitude MCP in the waypoint data, and compare the altitude difference Δh with the altitude threshold η. h Add a buffer height label flag to the size;

[0010] Step 4: Set the buffer time Δt, and update the track point dataset TR according to the buffer time;

[0011] Step 5: Decompose the vacuum velocity TAS into the U and V directions and calculate the impact of wind on the target aircraft;

[0012] Step 6: Set up the mass sequence, calculate the power, Q value, and mean square error (error) of the aircraft per unit mass for different masses, and compare the magnitudes of the mean square error to obtain the estimated mass of the target aircraft. est ;

[0013] Step 7: Set the altitude layer sequence, filter the waypoint data in each altitude layer, and calculate the estimated aircraft mass corresponding to different altitude layers. fl ;

[0014] Step 8, Obtain the final estimated mass of the target aircraft: Estimate the mass of the aircraft at all altitude levels. fl Numerical verification is performed to remove outliers, resulting in a valid mass sequence. The average mass of the valid masses is then calculated as the final estimated mass of the target aircraft.

[0015] Furthermore, the data tp of the i-th waypoint mentioned in step 1 i , which is a vector, as follows:

[0016] tp i =[t i ,hp i ,mcp i ,tas i ,θ i ]

[0017] Among them, t ihp i mcp i tas i and θ i These represent the time, barometric altitude, intended altitude, vacuum speed, and heading angle corresponding to the i-th waypoint, respectively, and N is the total number of waypoints.

[0018] Furthermore, the j-th wind vector mentioned in step 1... j The details are as follows:

[0019] wind vector j It consists of components in two directions, U and V, in the UV coordinate system, and is denoted as:

[0020] wind j =(wind_u j ,wind_v j )

[0021] Here, the wind vector in each direction is a four-dimensional vector based on time and space, denoted as:

[0022] wind_u j =(t j ,l j ,lon j ,lat j )

[0023]

[0024] Among them, t j l j lon j and lat j These represent the timestamp, altitude layer, longitude, and latitude corresponding to the j-th wind vector, respectively.

[0025] Furthermore, the interpolation calculation of waypoint data tp described in step 2... i Wind speed components in the U and V directions in the corresponding UV coordinate system (wind_u) i and wind_v i The specific method is as follows:

[0026] Step 2.1, obtain the waypoint data tp i In time t i The most recent wind vector timestamp t j The corresponding time index is denoted as index. t ;

[0027] Step 2.2, obtain the waypoint data tp i medium-high HP iThe height layer of the nearest wind vector l j The corresponding index, where the most recent upper-level index is denoted as index. h_top The most recent lower-level index is denoted as index. h_bottom ;

[0028] Step 2.3, based on the time index. t with height layer index h_top and index h_bottom This yields the set of wind vectors WD at the current altitude level. l ,as follows:

[0029] WD l ={wind t,l,lon,lat |t=index t ,l∈[index h_top ,index h_bottom ]}

[0030] Iterate through the vectors in the above set to calculate the wind speed at different altitudes. lon,lat The calculation method is as follows:

[0031]

[0032] Where x and y represent the wind vectors at the current altitude level. j Longitude and latitude, D x,y Represents the wind vector j Distance to the i-th waypoint, wind lon,lat Given the wind speed at the i-th trackpoint below the current altitude, calculate the corresponding wind speeds (wind_top) for the upper and lower altitudes using the method described above. lon,lat and wind_bottom lon,lat ;

[0033] Step 2.3, the wind vector of the i-th track point is the vertical wind speed vector (wind_top). lon,lat With wind_bottom lon,lat The mean, denoted as wind. i =(wind_u i ,wind_v i ), where wind_u i and wind_v i This indicates its components in the U and V directions.

[0034] Furthermore, the specific method for adding the buffer height label flag in step 3 is as follows:

[0035] Compare the height difference Δg with the height threshold ηh The size, if Δh < η h If the data is abnormal, it is considered abnormal track altitude data, and a buffer altitude label with a flag of 0 is added; otherwise, it is considered normal track altitude data, and a buffer altitude label with a flag of 1 is added.

[0036] Furthermore, in step 4, the data set TR of the waypoints is updated according to the buffer time Δt. The specific method is as follows:

[0037] Step 4.1, set the forward buffer time Δt forward Find the track point data tp in the track dataset where the buffered altitude label is 0. i Move the track point forward by Δt forward From the given data, we obtain a new dataset of waypoints TP′={tp i ,i=1,2,...,N′}, and N′≤N;

[0038] Step 4.2, set the backward buffer time Δt backward Find the track point tp with a buffered altitude label of 0 in the new track dataset TP′. i Remove the waypoint and move it backward by Δt backward From the given data, we obtain a new dataset of waypoints TP″={tp i ,i=1,2,...,N″}, and N″≤N′≤N.

[0039] Furthermore, the specific process for calculating the impact of wind on the target aircraft as described in step 5 is as follows:

[0040] Step 5.1: In the new waypoint dataset TP″, decompose the waypoint data tp i vacuum velocity tas i To reach the U and V directions, the method is as follows:

[0041]

[0042] Among them, vacuum velocity tas i The component in the U direction is denoted as tas_u i Vacuum speed tas i The component in the V direction is denoted as tas_v i ;

[0043] Step 5.2, calculate the effect of wind on the target aircraft. i The method is as follows:

[0044] effect i =(wind_u i+1 -wind_u i )tas_ui +(wind_v i+1 -wind_v i )tas_v i

[0045] Among them, wind_u i+1 and wind_u i For the current aircraft in t i+1 and t i The wind speed component in the U direction at time t, wind_v i+1 and wind_v i For the current aircraft in t i+1 and t i The wind speed component in the V direction at time t.

[0046] Furthermore, step 6 involves obtaining the estimated mass of the target aircraft. est The specific process is as follows:

[0047] Step 6.1: Obtain the minimum mass of the target aircraft based on the BADA performance model. min With maximum mass max In the mass range [mass min ×η min ,mass max ×η max [Internal linear interpolation yields K quality values, where η] min and η max These are the upper and lower mass thresholds, respectively, resulting in the mass sequence MA = {mass}. k ,k=1,2,...K}, where mass k This represents the k-th mass in the sequence;

[0048] Step 6.2: Traverse the mass sequence, calculate the power and Q value corresponding to each mass in the mass sequence, and calculate the trackpoint data tp according to the BADA performance model. i The corresponding power and Q value are denoted as power. i (mass k ) and Q i (mass k );

[0049] Step 6.3, calculate the k-th mass. k The mean square error of all track points at that time k The method is as follows:

[0050]

[0051] Among them, tpi ∈TR″, we get mass k The corresponding mean squared error sequence ERROR = {error k ,k=1,2,...,K}, where error k The minimum value corresponds to the estimated mass of the target aircraft. est .

[0052] Furthermore, step 7 involves calculating the estimated aircraft mass corresponding to different altitude levels. fl The specific method is as follows:

[0053] Step 7.1, set the lowest flight altitude level fl of the aircraft. low With the lowest flight altitude layer fl high In the height layer interval [fl low ,fl high By linearly interpolating Z height layer values ​​within the range, the height layer sequence FL = {fl} is obtained. z ,z=1,2,...Z}, where fl z This represents the z-th height layer;

[0054] Step 7.2: Traverse the altitude layer sequence and compare the waypoint data tp. i medium-high HP i With height layer fl z Find the index of the nearest waypoint at the current altitude. fl ;

[0055] Step 7.3, calculate the waypoint index. fl The first k mass k The mean square error of all corresponding track points k The method is as follows:

[0056]

[0057] Obtain the estimated mass of the aircraft at the corresponding altitude level. fl And the estimated mass set M = {mass} of aircraft at all altitude levels fl ,fl∈FL}.

[0058] Furthermore, the specific process for obtaining the final estimated mass of the target aircraft in step 8 is as follows:

[0059] Step 8.1, estimate the mass set M = {mass} of the aircraft. fl Perform a validity check on each value in the range ,fl∈FL}, including: removing empty values, deleting values ​​that exceed [mass min ,massmax The values ​​within the mass range are used to obtain the effective estimated mass set M′={mass fl ,fl∈FL′};

[0060] Step 8.2, calculate the average mass of all masses in the effective estimated mass set M′, mass = average{mass} fl |fl∈FL′}, the average mass is the final estimated mass of the target aircraft.

[0061] Beneficial effects:

[0062] This invention fully considers the impact of wind speed on aircraft mass estimation results. By filtering waypoints based on the intended altitude and altitude buffer time, abnormal waypoints that affect the mass estimation results can be eliminated. By implementing multi-altitude-level aircraft mass estimation, a more stable estimated aircraft mass can be obtained. Attached Figure Description

[0063] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0064] Figure 1 This is a flowchart of an aircraft mass estimation method based on intended altitude according to the present invention.

[0065] Figure 2 This is a schematic diagram of the error curve for estimating the mass of an aircraft, which is an embodiment of the present invention.

[0066] Figure 3 This is a schematic diagram of the error curve for estimating the mass of a multi-altitude aircraft, which is an embodiment of the present invention. Detailed Implementation

[0067] Combination Figure 1 The present invention provides a method for estimating aircraft mass based on intended altitude, comprising the following steps:

[0068] Step 1: Analyze the aircraft's Mode S radar data to obtain the dataset TP = {tp} for each track point. i Let tp be the i-th waypoint, i = 1, 2, ..., N. i Vector tp i =[t i ,hp i ,mcp i ,tas i ,θ i ], t i hp i mcp i tas i θi These represent the time, altitude, intended altitude, vacuum speed, and heading angle corresponding to the current waypoint, respectively, with N being the total number of waypoints; analyze the meteorological data to obtain the meteorological data set WD = {wind}. j ,j=1,2,...,M}, where, wind j Let M represent the j-th wind vector, where M is the total number of meteorological data points, and wind vector is the number of wind vectors. j It consists of two components, one due east (U) and one due north (V), and is denoted as wind. j =(wind_u j ,wind_v j The wind vector in each direction is a four-dimensional vector based on time and space, denoted as wind_u. j =(t j ,l j ,lon j ,lat j ), wind_v j =(t j ,l j ,lon j ,lat j ), t j l j lon j lat j These represent the timestamp, altitude layer, longitude, and latitude corresponding to the j-th wind vector, respectively.

[0069] Step 2: Interpolate and calculate waypoint tp i The corresponding wind speed components in the U and V directions, wind_u i wind_v i The interpolation method is as follows:

[0070] Step 2.1, obtain the current track point tp i Time t i The most recent wind vector timestamp t j The corresponding index is denoted as index. t ;

[0071] Step 2.2, obtain the current track point tp i HP i The height layer of the nearest wind vector l j The corresponding index, denoted as the nearest upper-level index, is denoted as index. h_top The nearest lower-level index is denoted as index. h_bottom ;

[0072] Step 2.3, based on the time index. t with height layer indexh_top index h_bottom This yields the set of wind vectors WD at the current altitude level. l ={wind t,l,lon,lat |t=index t ,l∈[index h_top ,index h_bottom The calculation method is as follows: Traverse each vector in the set to calculate the wind speed at each altitude level.

[0073]

[0074] Where x and y represent the wind vectors at the current altitude level. j longitude and latitude, D x,y Represents the wind vector j with trackpoint tp i distance, wind lon,lat The current flight path point tp at the current altitude i Calculate the wind speed at the location, and the corresponding wind speeds at the upper and lower levels, respectively (wind_top). lon,lat ,wind_bottom lon,lat ;

[0075] Step 2.3, current waypoint tp i The wind vector is the vertical wind speed vector, wind_top. lon,lat With wind_bottom lon,lat The mean, denoted as wind. i =(wind_u i ,wind_v i ).

[0076] Step 3: Set an appropriate height threshold η h Calculate the difference Δh between the barometric altitude Hp and the intended altitude MCP, and compare the altitude difference Δh with the altitude threshold η. h To determine the size of the buffer height, add a buffer height label (flag). The method for adding the buffer height label is as follows:

[0077] Step 3.1: Compare the height difference Δh with the height threshold η h The size, if Δh < η h For abnormal flight path altitude data, add a buffer altitude label of 0;

[0078] Step 3.2: Compare the height difference Δh with the height threshold η h The size, if Δh ≥ η h Add a buffer altitude label 1 to the normal track altitude data.

[0079] Step 4: Set the buffer time Δt, and update the waypoint dataset TR according to the buffer time. The method for updating the waypoint dataset TR is as follows:

[0080] Step 4.1, set the forward buffer time Δt forward Find the track point tp in the track dataset with a buffered altitude label of 0. i Remove trackpoint tp i Forward Δt forward From the given data, we obtain a new dataset of waypoints TP′={tp i Let the integers be i = 1, 2, ..., N′, and let N′ ≤ N;

[0081] Step 4.2, set the backward buffer time Δt backward Find the track point tp in the track dataset with a buffered altitude label of 0. i Remove trackpoint tp i backward Δt backward From the given data, we obtain a new dataset of waypoints TP″={tp i ,i=1,2,...,N″}, and N″≤N′≤N.

[0082] Step 5: Decompose the vacuum velocity TAS into the U and V directions, and calculate the impact of wind on the aircraft. The process of calculating the impact of wind on the aircraft is as follows:

[0083] Step 5.1, decompose the waypoints tp i The vacuum velocity TAS in the U and V directions is calculated as follows:

[0084]

[0085] Among them, vacuum velocity tas i The component in the U direction is denoted as tas_u i Vacuum speed tas i The component in the V direction is denoted as tas_v i tas i trackpoint tp i vacuum velocity, θ i trackpoint tp i The heading angle;

[0086] Step 5.2, calculate the impact of wind on the aircraft, as follows:

[0087] effect i =(wind_u i+1 -wind_u i )tas_u i +(wind_v i+1 -wind_v i)tas_v i

[0088] Among them, wind_u i+1 ,wind_u i For aircraft in t i+1 t i Wind speed in the U direction at any given time, wind_v i+1 wind_v i For aircraft in t i+1 t i Wind speed in the V direction at any given time.

[0089] Step 6: Set up a mass sequence, calculate the power, Q value, and mean square error of power and Q value per unit mass for aircraft at different masses, and compare the error values ​​to obtain the estimated mass of the aircraft. The specific process for calculating the aircraft mass is as follows:

[0090] Step 6.1: Obtain the minimum mass of the aircraft based on the aircraft performance model. min With maximum mass max In the mass range [mass min ×η min ,mass max ×η max [Internal linear interpolation of K quality values, η] min With η max These are the upper and lower mass thresholds, respectively, resulting in the mass sequence MA = {mass}. k ,k=1,2,...K};

[0091] In some embodiments, the above-mentioned aircraft performance model can be implemented using the BADA model or other models. The BADA (Base of aircraft data) model is a set of aircraft performance models and related databases published by the European Union Control Organization.

[0092] Step 6.2: Traverse the quality sequence and calculate each mass value. k Based on the BADA performance model, the corresponding power (power) and Q value are used to calculate the track point (tp). i The corresponding power and Q value are denoted as power. i (mass k ) and Q i (mass k );

[0093] Step 6.3, calculate mass k The mean square error of all track points is calculated as follows:

[0094]

[0095] Among them, tp i ∈TR″, we get mass k The corresponding mean squared error sequence ERROR = {error k error k The minimum value corresponds to the estimated mass of the aircraft. est .

[0096] Step 7: Set the altitude layer sequence, filter the trackpoint data for each altitude layer, and calculate the estimated aircraft mass corresponding to different altitude layers. fl Calculate the estimated mass of the aircraft at different altitude levels. fl The method is as follows:

[0097] Step 7.1, set the lowest flight altitude level fl of the aircraft. low With the lowest flight altitude layer fl high In the height layer interval [fl low ,fl high By linearly interpolating Z height layer values ​​within the range, a height layer sequence Fl = {fl} is obtained. z ,z=1,2,...Z};

[0098] Step 7.2: Traverse the altitude layer sequence and compare the waypoints tp. i HP i With height layer fl z Find the index of the nearest waypoint at the current altitude. fl ;

[0099] Step 7.3, calculate the index. fl premas k The mean square error of all corresponding track points is calculated as follows:

[0100]

[0101] Obtain the estimated mass of the aircraft at the corresponding altitude level. fl And the estimated mass set M = {mass} of aircraft at all altitude levels fl ,fl∈FL}.

[0102] Step 8: Estimate the mass of aircraft at all altitude levels. fl Numerical verification is performed to remove outliers, resulting in a valid mass sequence. The average mass of the valid masses is then calculated as the final estimated mass of the aircraft. The specific process is as follows:

[0103] Step 8.1, estimate the mass set M = {mass} of the aircraft. fl Perform a validity check on each value in the range ,fl∈FL}, remove empty values, and remove values ​​that exceed [mass min ,mass max The values ​​within the mass range are used to obtain the effective estimated mass set M′={mass fl ,fl∈Fl′};

[0104] Step 8.2, calculate the average mass of all masses in the mass set M′, mass = average{mass fl If |fl∈FL′}, then the average mass is the final estimated mass of the aircraft.

[0105] The following is combined with Figures 2 to 3 The invention is further illustrated through simulation experiments and their effect evaluation.

[0106] In this embodiment, as Figure 2 and Figure 3 The curves shown are the error curves for estimated aircraft mass and the error curves for estimated aircraft mass at multiple altitudes, respectively. The experimental objective is to calculate a stable estimated aircraft mass using an aircraft mass estimation method based on the intended altitude. Figure 2 The triangle in the corresponding mass error curve indicates that the aircraft's error is minimized when the mass at the horizontal coordinate corresponding to the triangle position is selected. Therefore, the mass at the triangle position is the estimated mass of the aircraft. Figure 3 This describes the error curves for estimating the mass of aircraft at multiple altitude levels. The mass at the x-axis corresponding to the triangle position on each curve is the estimated mass of the aircraft at that altitude level. Figure 3 This indicates that selecting waypoints in different altitude ranges yields different estimated aircraft masses. Figure 3 The mass circled in the Chinese box represents an abnormal mass that exceeds the upper and lower limits. After removing this abnormal value, the average of the estimated masses at other altitude levels is calculated to obtain a stable estimated aircraft mass. This result shows that the method of the present invention can eliminate abnormal track points that affect the mass estimation results, and obtain a more stable estimated aircraft mass by implementing multi-altitude-level aircraft mass estimation.

[0107] In its specific implementation, this application provides a computer storage medium and a corresponding data processing unit. The computer storage medium is capable of storing a computer program, which, when executed by the data processing unit, can run the invention's content regarding an aircraft mass estimation method based on intended altitude, as well as some or all of the steps in various embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0108] Those skilled in the art will clearly understand that the technical solutions in the embodiments of the present invention can be implemented using computer programs and their corresponding general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of computer programs, i.e., software products. These computer program software products can be stored in a storage medium and include several instructions to cause a device containing a data processing unit (which may be a personal computer, server, microcontroller, MCU, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.

[0109] This invention provides an idea and method for aircraft mass estimation based on intended altitude. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. A method for estimating aircraft mass based on intended altitude, characterized in that, Includes the following steps: Step 1: Analyze the target aircraft's Mode S radar data to obtain the dataset of the aircraft at each waypoint. ,in, For the first Data for each waypoint; Analyze meteorological data to obtain a meteorological data set. ,in, Represents the j-th wind vector; Step 2: Interpolate and calculate waypoint data Wind speed components in the U and V directions in the corresponding UV coordinate system and ; Step 3, set the height threshold Calculate the barometric altitude in the waypoint data Height difference from the intended height MCP Compare the height difference With height threshold Size, add a buffer height label ; Step 4, Set the buffer time The data set TR of waypoints is updated according to the buffer time; Step 5: Decompose the vacuum velocity TAS into the U and V directions and calculate the impact of wind on the target aircraft; Step 6: Set up a mass sequence, calculate the power, Q value, and mean square error (error) of the aircraft per unit mass for different masses, and compare the magnitudes of the mean square error (error) to obtain the estimated mass of the target aircraft. ; Step 7: Set the altitude layer sequence, filter the waypoint data in each altitude layer, and calculate the estimated aircraft mass corresponding to different altitude layers. ; Step 8, Obtain the final estimated mass of the target aircraft: Estimate the mass of the aircraft at all altitude levels. Numerical verification is performed to remove outliers and obtain a valid mass sequence. The average mass of the valid masses is then calculated as the final estimated mass of the target aircraft. In step 4, based on the buffer time The method for updating the waypoint dataset TR is as follows: Step 4.1, Set the forward buffer time Find the track point data in the track dataset with a buffered altitude label of 0. Remove the waypoint and move forward. From the data, we obtain a new dataset of waypoints. ,and ; Step 4.2, Set the back buffer time Find new track datasets Waypoints with a buffer altitude label of 0 Remove the waypoint and move backward. From the data, we obtain a new dataset of waypoints. ,and ; The specific process for calculating the impact of wind on the target aircraft in step 5 is as follows: Step 5.1, in the new waypoint dataset In the middle, decompose the waypoint data Vacuum speed in To reach the U and V directions, the method is as follows: ； Among them, vacuum speed The component in the U direction is denoted as vacuum speed The component in the V direction is denoted as ; Step 5.2, calculate the impact of wind on the target aircraft. The method is as follows: ； in, and For current aircraft in and The wind speed component in the U direction at any given time. and For current aircraft in and The wind speed component in the V direction at time t; Step 6 describes obtaining the estimated mass of the target aircraft. The specific process is as follows: Step 6.1: Obtain the minimum mass of the target aircraft based on the BADA performance model. With maximum mass In the quality range K quality values ​​are obtained through internal linear interpolation, where, and These are the upper and lower quality thresholds, respectively, which yield the quality sequence. ,in, Indicates the first in the sequence One quality; Step 6.2: Traverse the mass sequence, calculate the power and Q value for each mass in the mass sequence, and calculate the waypoint data according to the BADA performance model. The corresponding power and Q value are denoted as: and ; Step 6.3, calculate the first... quality Mean square error of all track points The method is as follows: ； in, ,get The corresponding mean square error sequence ,in, The mass corresponding to the minimum value is the estimated mass of the target aircraft. ; Step 7 describes calculating the estimated aircraft mass corresponding to different altitude levels. The specific method is as follows: Step 7.1: Set the lowest flight altitude layer for the aircraft. With the lowest flight altitude layer In the high-altitude zone By interpolating Z height layer values ​​linearly, a height layer sequence is obtained. ,in, Indicates the first One height layer; Step 7.2: Traverse the altitude layer sequence and compare the waypoint data. medium height With height layer Find the trackpoint index corresponding to the nearest altitude. ; Step 7.3, Calculate the waypoint index forward quality The mean square error of all corresponding track points The method is as follows: ； Obtain the estimated mass of the aircraft at the corresponding altitude level. and the estimated mass set of aircraft at all altitude levels. ; The specific process for obtaining the final estimated mass of the target aircraft in step 8 is as follows: Step 8.1, estimate the mass set of the aircraft. Each value in the data is validated for validity, including: removing empty values ​​and deleting values ​​that exceed the limit. The values ​​within the mass range yield the effective estimated mass set of the aircraft. ; Step 8.2, calculate the effective estimated mass set. The average mass of all masses The average mass This is the final estimated mass of the target aircraft.

2. The aircraft mass estimation method based on intended altitude according to claim 1, characterized in that, The first step described in step 1 Data from each waypoint , which is a vector, as follows: ； in, , , , and Representing the first The time, barometric altitude, intended altitude, vacuum speed, and heading angle corresponding to each waypoint, where N is the total number of waypoints.

3. The aircraft mass estimation method based on intended altitude according to claim 2, characterized in that, The j-th wind vector mentioned in step 1 The details are as follows: Wind vector It consists of components in two directions, U and V, in the UV coordinate system, and is denoted as: ； Here, the wind vector in each direction is a four-dimensional vector based on time and space, denoted as: ； ； in, , , and These represent the timestamp, altitude layer, longitude, and latitude corresponding to the j-th wind vector, respectively.

4. The aircraft mass estimation method based on intended altitude according to claim 3, characterized in that, The interpolation calculation of waypoint data described in step 2 Wind speed components in the U and V directions in the corresponding UV coordinate system and The specific method is as follows: Step 2.1, Obtain waypoint data The time in Recent wind vector timestamp The corresponding time index is denoted as ; Step 2.2, Obtain waypoint data medium height The height layer of the nearest wind vector The corresponding index, where the most recent upper-level index is denoted as... The most recent lower-level index is denoted as ; Step 2.3, based on the time index With height layer index and This yields the set of wind vectors at the current altitude level. ,as follows: ； Iterate through the vectors in the above set to calculate the wind speed at different altitudes. The calculation method is as follows: ； Where x and y represent the wind vectors at the current altitude level. Longitude and latitude Represents wind vector With the Distance between waypoints The first one below the current height layer The wind speed at each trackpoint was calculated using the method described above, corresponding to the wind speeds at different altitude levels. and ; Step 2.3, the The wind vector at each trackpoint is the vertical wind speed vector. and The mean, denoted as ,in, and This indicates its components in the U and V directions.

5. The aircraft mass estimation method based on intentional altitude according to claim 4, characterized in that, Adding a buffer height label as described in step 3 The specific method is as follows: Compare height differences With height threshold The size, if If the data is abnormal, it is considered abnormal flight path altitude data, and a buffer altitude label is added. If the value is 0, it is considered normal track altitude data, and a buffer altitude label is added. The value is 1.

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