ADS-B data track smoothing method and system based on adaptive variable structure

Abstract

The present invention relates to the field of aviation supervision technology, and in particular to an ADS-B data track smoothing method and system based on adaptive variable structure, by acquiring equivalent measurement data of ADS-B track data, and removing the trend item of the equivalent measurement data time series based on the spline fitting method and performing error calibration on the equivalent measurement data; constructing a state equation describing the change process of the state vector of an air target based on the constant acceleration of the VTC system, and constructing a variable structure measurement equation for representing the functional relationship between the state vector and the measurement vector and changing with the content of the measurement data based on the equivalent measurement data; performing track smoothing processing on the original track data based on the state equation and using Kalman filtering, and removing invalid data points in the track data through the new information chi-square test during track smoothing processing. The present invention can make the track data smoother, make the smoothed data more complete, and can fully describe the position and speed motion state of the aircraft, and has good application value.

Description

Technical Field

[0001] The present invention relates to the field of aviation supervision technology, and in particular to an ADS-B data track smoothing method and system based on adaptive variable structure. Background Art

[0002] Automatic Dependent Surveillance Broadcast (ADS-B) reports the specific location of the aircraft and the main flight parameters of the current aircraft by sending ADS-B track messages. The information includes the ICAO number, longitude and latitude, altitude, speed, etc. of the aircraft, which facilitates the ground control center and other aircraft to monitor the status of the aircraft. Developed countries such as Europe and the United States regard ADS-B as the core technology of the next generation of air traffic control, and ADS-B is being rapidly deployed and applied around the world. However, in actual data processing, it is found that the received ADS-B track data usually has the following problems: (1) Partial data is missing. The complete track data includes geodetic longitude, geodetic latitude, geodetic altitude, ground speed, climb rate and heading angle, which can fully describe the position and speed motion state of the aircraft. However, in most cases, the data usually only has longitude, latitude and altitude. One to three elements of ground speed, climb rate and heading angular velocity are randomly missing, which cannot fully describe the speed motion state of the aircraft; (2) The data contains noise and its statistical characteristics are unknown, resulting in an insufficiently smooth track; (3) There are obvious bad points in the data, which may be associated with wrong points or solution errors. Therefore, there is an urgent need for a track data smoothing method that can meet the needs of air traffic management applications by fully describing the position and motion state of aerial targets when the original track data is incomplete and the statistical characteristics are unknown. Summary of the invention

[0003] To this end, the present invention provides an ADS-B data track smoothing method and system based on adaptive variable structure, which performs track smoothing on the original track data through equivalent measurement data error calibration and variable structure measurement equation matching with the latitude of real-time equivalent measurement data and introduces Kalman filtering, and uses the new information chi-square test to remove bad points in the track data, so that the track is smoother and meets the dynamic constraints of aerial targets. It has high adaptability and can obtain complete track data to meet the ADS-B monitoring requirements.

[0004] According to the design scheme provided by the present invention, on the one hand, a method for smoothing ADS-B data track based on adaptive variable structure is provided, comprising:

[0005] Obtain equivalent measurement data of ADS-B track data, remove trend items of equivalent measurement data time series based on spline fitting method and perform error calibration on equivalent measurement data, wherein the equivalent measurement data includes the ground-fixed system position vector and the northeast sky system velocity vector corresponding to the original track data of the aerial target, and the original track data includes geodetic longitude, geodetic latitude, geodetic altitude, ground speed, climb rate and heading angle;

[0006] According to the constant acceleration of the VTC system, a state equation describing the change process of the state vector of the aerial target is constructed, and based on the equivalent measurement data, a variable structure measurement equation is constructed to express the functional relationship between the state vector and the measurement vector and changes with the content of the measurement data, wherein the state vector includes the position vector of the earth-fixed system, the velocity vector and the acceleration of the VTC system;

[0007] The original track data is smoothed based on the state equation and the Kalman filter is used. In the track smoothing process, invalid data points in the track data are eliminated through the new information chi-square test.

[0008] As the ADS-B data track smoothing method based on adaptive variable structure of the present invention, further, obtaining equivalent measurement data of ADS-B track data includes:

[0009] For the original track data of aerial targets, the geodetic longitude, geodetic latitude and geodetic altitude are converted into the position vector of the earth-fixed system; the ground speed, climb rate and heading angle are converted into the velocity vector of the local northeast sky system.

[0010] As the ADS-B data track smoothing method based on adaptive variable structure of the present invention, further, based on the spline fitting method, the trend item of the equivalent measurement data time series is removed and the error of the equivalent measurement data is calibrated, which includes:

[0011] Based on the cubic spline model to represent the equivalent measurement data time series, the track fitting problem is transformed into the problem of estimating the spline coefficients of the cubic spline model.

[0012] According to the equivalent measurement data and the cubic spline model, the spline coefficient estimate is obtained by the least squares method, the equivalent measurement data estimate is obtained based on the spline coefficient estimate and the cubic spline model, and the spline fitting error is obtained according to the equivalent measurement data and the equivalent measurement data estimate. The spline fitting error is used to represent the degree of conformity between the spline fitting curve and the measurement data curve.

[0013] As the ADS-B data track smoothing method based on adaptive variable structure of the present invention, further, the state equation describing the change process of the air target state vector at time k+1 constructed based on the constant acceleration of the VTC system is expressed as: Among them, r ECF 、vECF They represent the position and velocity of the ground-fixed system, respectively. VTC represents the VTC acceleration, represents the coordinate transformation matrix from the VTC system to the ground fixed system, T is the measurement interval, I n×n is the n-order unit matrix, O m×n is an m-row n-column zero matrix, w k is the process noise at time k.

[0014] As the ADS-B data track smoothing method based on adaptive variable structure of the present invention, further, a variable structure measurement equation for representing the functional relationship between the state vector and the measurement vector and varying with the content of the measurement data is constructed based on equivalent measurement data, comprising:

[0015] According to the known quantities of the original track data, measurement equations under different track content structures are respectively constructed to obtain measurement equations under corresponding structures according to the changes in the measurement data content at different times, wherein the different track content structures include: an original track data structure in which the geodetic longitude, geodetic latitude and geodetic altitude are all known, an original track data structure in which the geodetic longitude, geodetic latitude, geodetic altitude and ground speed are all known, an original track data structure in which the geodetic longitude, geodetic latitude, geodetic altitude and climb rate are all known, an original track data structure in which the geodetic longitude, geodetic latitude, geodetic altitude, ground speed and heading angle are all known, an original track data structure in which the geodetic longitude, geodetic latitude, geodetic altitude, ground speed and climb rate are all known, and an original track data structure in which the geodetic longitude, geodetic latitude, geodetic altitude, ground speed and climb rate are all known.

[0016] As the ADS-B data track smoothing method based on adaptive variable structure of the present invention, further, based on the state equation and using Kalman filtering to perform track smoothing processing on the original track data, it includes:

[0017] According to the track data at historical moments and based on the state transfer matrix and the uniform motion model, the track state estimate of the aerial target at future moments is predicted and the state covariance is calculated; based on the state estimate of the aerial target at future moments and using the variable structure measurement equation, the track measurement estimate of the aerial target at future moments is predicted, and the innovation and innovation covariance of the current future moment are calculated according to the measurement estimate and the equivalent measurement data, and based on the innovation and innovation covariance, it is judged whether the equivalent measurement data at the current future moment is valid. If valid, the state equation is used to predict the state and covariance of the current future moment based on the track state at the previous moment, and the gain matrix is ​​used to update the track state and covariance at the current future moment. If invalid, the invalid data points in the track data are eliminated through the innovation chi-square test point, and the track state and covariance at the current future moment are updated based on the state prediction value of the uniform speed model.

[0018] As the ADS-B data track smoothing method based on adaptive variable structure of the present invention, further, judging whether the equivalent measurement data in the current future time is valid based on the innovation and the innovation covariance includes:

[0019] First, the Mahalanobis distance of the new information at the current future moment is calculated based on the new information and the new information covariance, and it is determined whether the Mahalanobis distance is greater than a given threshold. If so, the equivalent measurement data at the current future moment is deemed invalid; otherwise, the equivalent measurement data at the current future moment is deemed valid.

[0020] Furthermore, the present invention also provides an ADS-B data track smoothing system based on adaptive variable structure, comprising: a data acquisition module, a state analysis module and a track smoothing module, wherein:

[0021] A data acquisition module is used to acquire equivalent measurement data of ADS-B track data, and remove the trend item of the equivalent measurement data time series based on the spline fitting method and perform error calibration on the equivalent measurement data, wherein the equivalent measurement data includes the ground-fixed system position vector and the northeast sky system velocity vector corresponding to the original track data of the aerial target, and the original track data includes the geodetic longitude, geodetic latitude, geodetic altitude, ground speed, climb rate and heading angle;

[0022] A state analysis module is used to construct a state equation describing the change process of the state vector of the aerial target based on the constant acceleration of the VTC system, and to construct a variable structure measurement equation based on equivalent measurement data, which is used to represent the functional relationship between the state vector and the measurement vector and changes with the content of the measurement data, wherein the state vector includes the position vector of the earth-fixed system, the velocity vector and the acceleration of the VTC system;

[0023] The track smoothing module is used to perform track smoothing on the original track data based on the state equation and using Kalman filtering, and to eliminate invalid data points in the track data through the new information chi-square test during track smoothing.

[0024] Beneficial effects of the present invention:

[0025] The invention converts the geodetic latitude and longitude of the original track data into the Earth Centered Fixed (ECF) position vector, and converts the ground speed, climb rate and heading angle into the local East North Up (ENU) velocity vector as equivalent measurement data; uses spline fitting to remove the trend item of the equivalent measurement data, calibrates the remaining noise error, and obtains the statistical characteristics such as the variance of the noise; constructs a uniform acceleration model described by the acceleration of the VTC coordinate system as a state equation, and a variable structure measurement equation matching the latitude of the real-time equivalent measurement data, and introduces an extended Kalman filter based on the state equation and the variable structure measurement equation to smooth the track of the ADS-B original track data; uses the chi-square test of the new information to eliminate bad points, and finally uses the estimated aircraft ground The fixed position and velocity vectors are converted into the standard ADS-B track data format, which can effectively remove the bad points of the original ADS-B track data, making the track data smoother and in line with the dynamic constraints of the aircraft. Most importantly, for the original ADS-B track data with any missing speed information, the smoothed data contains the aircraft's geodetic longitude, geodetic latitude, geodetic altitude, ground speed, climb rate and heading angle information, which can fully describe the aircraft's position and speed motion state, and can be used for track prediction of aerial targets and ADS-B system monitoring, with good application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a schematic diagram of the ADS-B data track smoothing process based on the adaptive variable structure in the embodiment;

[0027] Figure 2 This is a schematic diagram of some ADS-B track data of an aircraft in the embodiment;

[0028] Figure 3 Schematic diagram of the track smoothing result in the embodiment. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solutions and advantages of the present invention clearer and more understandable, the present invention is further described in detail below in conjunction with the accompanying drawings and technical solutions.

[0030] In view of the fact that the existing track data described in the background technology is incomplete, contains noise and has unknown statistical characteristics, and has obvious bad points, the embodiments of the present invention refer to Figure 1 As shown, a method for smoothing ADS-B data track based on adaptive variable structure is provided, which includes the following contents:

[0031] S101. Acquire equivalent measurement data of the ADS-B track data, remove trend items of the equivalent measurement data time series based on the spline fitting method, and perform error calibration on the equivalent measurement data, wherein the equivalent measurement data includes the earth-fixed system position vector and the northeast sky system velocity vector corresponding to the original track data of the aerial target, and the original track data includes the geodetic longitude, geodetic latitude, geodetic altitude, ground speed, climb rate, and heading angle.

[0032] The equivalent measurement data of ADS-B track data may include:

[0033] For the original track data of aerial targets, the geodetic longitude, geodetic latitude and geodetic altitude are converted into the position vector of the earth-fixed system; the ground speed, climb rate and heading angle are converted into the velocity vector of the local northeast sky system.

[0034] Since the geodetic longitude, latitude and heading angle may change suddenly, and the rate of change is usually greater than the rectangular coordinate coefficient value, the geodetic longitude and latitude are converted into the earth-fixed system position vector, and the ground speed, climb rate and heading angle are converted into the local northeast sky system velocity vector. The earth-fixed system position vector and the northeast sky system velocity vector are defined as equivalent measurement data. After conversion, the data continuity is good. The conversion method is as follows:

[0035] Assume that the coordinates of the Earth Fixed Frame (ECF) are (X, Y, Z), and the coordinates in the geodetic coordinate system are (geodetic longitude L, geodetic latitude B, geodetic height H). The process of converting from the geodetic coordinate system to the Earth Fixed Frame can be expressed as:

[0036]

[0037] Where N is the radius of curvature of the yoke, and

[0038]

[0039] Where a e is the semi-major axis of the Earth, and e is the eccentricity of the Earth.

[0040] Let ground speed be v d , the climbing rate is v h , the heading angle is σ, then the velocity vector of the Northeastern Universe (ENU) can be expressed as

[0041]

[0042] Specifically, based on the spline fitting method, the trend term of the equivalent measurement data time series is removed and the error of the equivalent measurement data is calibrated, which can be designed to include the following contents:

[0043] Based on the cubic spline model to represent the equivalent measurement data time series, the track fitting problem is transformed into the problem of estimating the spline coefficients of the cubic spline model.

[0044] According to the equivalent measurement data and the cubic spline model, the spline coefficient estimate is obtained by the least squares method, the equivalent measurement data estimate is obtained based on the spline coefficient estimate and the cubic spline model, and the spline fitting error is obtained according to the equivalent measurement data and the equivalent measurement data estimate. The spline fitting error is used to represent the degree of conformity between the spline fitting curve and the measurement data curve.

[0045] The equivalent measurement data is the position or velocity in the rectangular coordinate system, which has better continuity and smoothness than the angle data. The trend term of the data time series can be obtained by cubic spline fitting. The relationship between the measurement data series and the cubic spline model can be written as a matrix form of the measurement equation

[0046] Z=Bb+ε (4)

[0047] In the formula, Z is the measured data sequence, B is the cubic spline model, b is the spline coefficient to be estimated, and ε is the data noise with unknown statistical characteristics. In this way, the track fitting problem is transformed into the estimation problem of the parameter b to be estimated. The coefficient vector b can be estimated by the least squares algorithm:

[0048]

[0049] The corresponding estimated covariance matrix can be expressed as

[0050]

[0051] The estimation of equivalent measurement data can be expressed as

[0052]

[0053] Defining spline fitting error

[0054]

[0055] For any continuous curve, the spline fitting error is related to the number of nodes. Starting from 1 node, as the number of nodes increases, the error decreases rapidly, and the spline fitting curve is more and more consistent with the measured data curve. However, as the number of nodes gradually increases, the spline fitting error tends to be constant due to the noise in the measured data. Therefore, the minimum number of nodes for the spline fitting error to tend to be constant can be taken as the desired value.

[0056] The specific calculation method is: starting from the number of nodes k set to 1, calculate the corresponding spline fitting models respectively, and then calculate the spline fitting error; increase the node value one by one, subtract the spline fitting error corresponding to the number of k+1 nodes from the spline fitting error corresponding to the number of k nodes, and when the difference is less than a certain threshold, the number of nodes is the required one.

[0057] S102. Construct a state equation describing the change process of the state vector of the aerial target based on the constant acceleration of the VTC system, and construct a variable structure measurement equation based on the equivalent measurement data, which is used to represent the functional relationship between the state vector and the measurement vector and changes with the content of the measurement data, wherein the state vector includes the earth-fixed system position vector, velocity vector and VTC system acceleration.

[0058] In order to facilitate the understanding of the motion characteristics of aerial targets, the state vector X is defined as the ground-fixed position r ECF =[X YZ] T ,speed and VTC acceleration a VTC =[a V a T a C ] T Composition, that is

[0059]

[0060] Assuming that within a measurement interval, the change of the state vector is a process in which the acceleration of the VTC system is constant, the discretized state equation is:

[0061]

[0062] In the formula is the coordinate transformation matrix from VTC system to ground fixed system, T is the measurement interval, I n×n is the n-order unit matrix, O m×n is an m-row n-column zero matrix, w k is the process noise, which is an unknown zero-mean Gaussian white noise.

[0063] Among them, a variable structure measurement equation is constructed based on equivalent measurement data to represent the functional relationship between the state vector and the measurement vector and changes with the content of the measurement data. It can be designed to include the following contents:

[0064] According to the known quantities of the original track data, measurement equations under different track content structures are respectively constructed to obtain measurement equations under corresponding structures according to the changes in the measurement data content at different times, wherein the different track content structures include: an original track data structure in which the geodetic longitude, geodetic latitude and geodetic altitude are all known, an original track data structure in which the geodetic longitude, geodetic latitude, geodetic altitude and ground speed are all known, an original track data structure in which the geodetic longitude, geodetic latitude, geodetic altitude and climb rate are all known, an original track data structure in which the geodetic longitude, geodetic latitude, geodetic altitude, ground speed and heading angle are all known, an original track data structure in which the geodetic longitude, geodetic latitude, geodetic altitude, ground speed and climb rate are all known, and an original track data structure in which the geodetic longitude, geodetic latitude, geodetic altitude, ground speed and climb rate are all known.

[0065] The measurement equation needs to give the functional relationship between the state vector and the measurement vector. For ADS-B data, the original measurement data includes geodetic longitude, geodetic latitude, geodetic altitude, ground speed, climb rate and heading angle, of which the last three may be missing. Therefore, the latitude and specific expression of the measurement equation will change with the content of the measurement data. According to the actual received ADS-B data content structure, the measurement equation that changes with the measurement data content can be divided into the following 6 cases:

[0066] (1) Longitude, latitude and altitude are known

[0067] The equivalent measurement data is the fixed position vector of the ground. At this time, the measurement vector and measurement equation can be expressed as

[0068]

[0069] Where H1=[I 3×3 O 3×3 O 3×3 ].

[0070] (2) Longitude, latitude, altitude and ground speed are known

[0071] The equivalent measurement data are the ground fixed position vector and ground velocity. At this time, the measurement vector and measurement equation can be expressed as:

[0072]

[0073] Where v ENU (i), i=1,2,3 represent the x, y and z axis components of the velocity vector of the northeast sky system respectively, is the coordinate transformation matrix from the Earth-fixed system to the local Northeast Sky system

[0074]

[0075] (3) Given longitude, latitude, altitude and climb rate

[0076] The equivalent measurement data are the fixed position vector and the climbing rate. At this time, the measurement vector and measurement equation can be expressed as:

[0077]

[0078] (4) Known longitude, latitude, altitude, ground speed and heading angle

[0079] The equivalent measurement data are the x-axis and y-axis components of the earth-fixed position vector and the northeast sky velocity vector. At this time, the measurement vector and measurement equation can be expressed as:

[0080]

[0081] (5) Given longitude, latitude, altitude, ground speed and climb rate

[0082] The equivalent measurement data are the fixed position vector, ground speed and climb rate. At this time, the measurement vector and measurement equation are:

[0083]

[0084] (6) Known longitude, latitude, altitude, ground speed, climb rate and heading angle

[0085] The equivalent measurement data are the x-, y- and z-axis components of the earth-fixed position vector and the northeast sky velocity vector. At this time, the measurement vector and measurement equation can be expressed as:

[0086]

[0087] S103, performing track smoothing processing on the original track data based on the state equation and using Kalman filtering, and removing invalid data points in the track data through the new information chi-square test during the track smoothing processing.

[0088] Specifically, the track smoothing process of the original track data is performed based on the state equation and using Kalman filtering, which may include:

[0089] According to the track data at historical moments and based on the state transfer matrix and the uniform motion model, the track state estimate of the aerial target at future moments is predicted and the state covariance is calculated; based on the state estimate of the aerial target at future moments and using the variable structure measurement equation, the track measurement estimate of the aerial target at future moments is predicted, and the innovation and innovation covariance of the current future moment are calculated according to the measurement estimate and the equivalent measurement data, and based on the innovation and innovation covariance, it is judged whether the equivalent measurement data at the current future moment is valid. If valid, the state equation is used to predict the state and covariance of the current future moment based on the track state at the previous moment, and the gain matrix is ​​used to update the track state and covariance at the current future moment. If invalid, the invalid data points in the track data are eliminated through the innovation chi-square test point, and the track state and covariance at the current future moment are updated based on the state prediction value of the uniform speed model.

[0090] Among them, the Mahalanobis distance of the new information at the current future moment can be calculated based on the new information and the new information covariance, and it can be determined whether the Mahalanobis distance is greater than a given threshold. If it is greater, the equivalent measurement data at the current future moment is deemed invalid; otherwise, the equivalent measurement data at the current future moment is deemed valid.

[0091] When the state estimate at time k is obtained and covariance estimation After that, first determine whether the measured data at time k+1 is a bad pixel. The specific method is as follows: (Indicates taking the first 6 rows of data), (Indicates taking the first 6×6 groups of data in the matrix) and performing state prediction based on the uniform speed model

[0092]

[0093] In the formula is the state transfer matrix, covariance prediction

[0094]

[0095] Then measure the prediction

[0096]

[0097] Where h i (·), i = 1, 2, 3, ···, 6 is the measurement equation. The specific expression can be found in the above variable structure measurement equation. The specific expression form is determined by the content of the measurement data. By defining the measurement matrix

[0098]

[0099] Calculate innovation

[0100]

[0101] The innovation covariance is

[0102]

[0103] If the Mahalanobis distance of the new information is greater than the given threshold γ

[0104]

[0105] It is considered that the measured data y k+1 Invalid, that is, a bad point, the state update value is constructed using the state prediction value based on the uniform speed model. The specific update process can be expressed as:

[0106]

[0107] If the Mahalanobis distance of the new information is not greater than the given threshold, the measurement data is considered valid and can be filtered and smoothed. The specific filtering and smoothing steps can be described as follows:

[0108] (1) State prediction

[0109]

[0110] In the formula, function f k (·) is the state prediction formula (10).

[0111] (2) Covariance prediction

[0112]

[0113] In the formula, is the state transfer matrix, Q k+1 is the process noise covariance matrix, set to

[0114]

[0115] In the formula, τ m is the maneuvering time constant, a max is the target maximum acceleration.

[0116] (3) Gain matrix calculation

[0117]

[0118] (4) State estimation update

[0119]

[0120] (5) Covariance update

[0121]

[0122] Further, based on the above method, an embodiment of the present invention also provides an ADS-B data track smoothing system based on adaptive variable structure, comprising: a data acquisition module, a state analysis module and a track smoothing module, wherein:

[0123] A data acquisition module is used to acquire equivalent measurement data of ADS-B track data, and remove the trend item of the equivalent measurement data time series based on the spline fitting method and perform error calibration on the equivalent measurement data, wherein the equivalent measurement data includes the ground-fixed system position vector and the northeast sky system velocity vector corresponding to the original track data of the aerial target, and the original track data includes the geodetic longitude, geodetic latitude, geodetic altitude, ground speed, climb rate and heading angle;

[0124] A state analysis module is used to construct a state equation describing the change process of the state vector of the aerial target based on the constant acceleration of the VTC system, and to construct a variable structure measurement equation based on equivalent measurement data, which is used to represent the functional relationship between the state vector and the measurement vector and changes with the content of the measurement data, wherein the state vector includes the position vector of the earth-fixed system, the velocity vector and the acceleration of the VTC system;

[0125] The track smoothing module is used to perform track smoothing on the original track data based on the state equation and using Kalman filtering, and to eliminate invalid data points in the track data through the new information chi-square test during track smoothing.

[0126] In order to verify the effectiveness of this solution, the following is a further explanation based on experimental data:

[0127] Based on the specific implementation formula algorithms in the embodiments of this case, a section of ADS-B track data of a certain aircraft is smoothed, and some of the data is as follows: Figure 2 shown. Figure 2 It can be seen that the original track data usually randomly lacks some velocity component data. Therefore, the latitude and specific expression of the measurement equation need to be adaptively changed according to the content structure of the track data.

[0128] The smoothing result is as follows Figure 3 As shown in the figure, (a), (b), (c), (d) and (e) are the smoothed longitude and latitude, altitude, ground speed, climb rate and heading angle respectively. It can be seen from the figure that (1) the smoothed longitude and latitude data are highly consistent with the original data, which on the one hand shows the correctness of the original data and the correlation between position and speed that satisfies the dynamic constraints, and on the other hand verifies the accuracy and effectiveness of the scheme in this case; (2) the smoothed result is smoother than the original data, and the amplitude of some mutation points is significantly reduced; (3) the original data does not contain heading angle data, but through track smoothing, a high-precision heading angle change curve can be obtained, which completes the aircraft's speed motion state information.

[0129] The above experimental data can further verify that the solution in this case can effectively remove bad points in the original ADS-B track data, improve the smoothing effect of the track data, comply with the dynamic constraints of the aircraft, and complete the incomplete original track data to fully describe the position and speed motion state of the aerial target, thereby ensuring the effectiveness of aerial target supervision.

[0130] Unless otherwise specifically stated, the relative steps, numerical expressions and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0131] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0132] The units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person of ordinary skill in the art may use different methods to implement the described functions for each specific application, but such implementation is not considered to be beyond the scope of the present invention.

[0133] Those skilled in the art will appreciate that all or part of the steps in the above method can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a disk or an optical disk. Optionally, all or part of the steps in the above embodiment can also be implemented using one or more integrated circuits, and accordingly, each module / unit in the above embodiment can be implemented in the form of hardware or in the form of software function modules. The present invention is not limited to any specific form of combination of hardware and software.

[0134] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for smoothing ADS-B data tracks based on adaptive variable structure, characterized in that: Include: Obtain equivalent measurement data of ADS-B track data, remove trend items of equivalent measurement data time series based on spline fitting method and perform error calibration on equivalent measurement data, wherein the equivalent measurement data includes the ground-fixed system position vector and the northeast sky system velocity vector corresponding to the original track data of the aerial target, and the original track data includes geodetic longitude, geodetic latitude, geodetic altitude, ground speed, climb rate and heading angle; The state equation describing the change process of the state vector of the aerial target is constructed based on the constant acceleration of the VTC system, and the variable structure measurement equation used to express the functional relationship between the state vector and the measurement vector and changing with the content of the measurement data is constructed based on the equivalent measurement data, wherein the state vector includes the position vector, velocity vector and VTC system acceleration of the ground fixed system; the state equation describing the change process of the state vector of the aerial target at time k+1 constructed based on the constant acceleration of the VTC system is expressed as: r ECF 、v ECF They represent the position and velocity of the ground-fixed system, respectively. VTC represents the VTC acceleration, represents the coordinate transformation matrix from the VTC system to the ground fixed system, T is the measurement interval, I n×n is the n-order unit matrix, O m×n is an m-row n-column zero matrix, w k is the process noise at time k; The original track data is smoothed based on the state equation and the Kalman filter is used, and invalid data points in the track data are eliminated through the new information chi-square test during the track smoothing process, wherein the original track data is smoothed based on the state equation and the Kalman filter, including: predicting the state estimation of the air target track at the future time based on the track data at the historical time and based on the state transfer matrix and the uniform motion model and calculating the state covariance; predicting the air target track measurement at the future time based on the air target state estimation at the future time and using the variable structure measurement equation Estimate, calculate the innovation and innovation covariance of the current future moment according to the measurement estimate and the equivalent measurement data, judge whether the equivalent measurement data in the current future moment is valid based on the innovation and innovation covariance, if valid, use the state equation and predict the state and covariance of the current future moment based on the track state of the previous moment, use the gain matrix to update the track state and covariance of the current future moment, if invalid, use the innovation chi-square test to eliminate invalid data points in the track data, and update the track state and covariance of the current future moment based on the state prediction value of the uniform speed model.

2. The ADS-B data track smoothing method based on adaptive variable structure according to claim 1, characterized in that: Get equivalent measurement data of ADS-B track data, including: For the original track data of aerial targets, the geodetic longitude, geodetic latitude and geodetic altitude are converted into the position vector of the earth-fixed system; the ground speed, climb rate and heading angle are converted into the velocity vector of the local northeast sky system.

3. The ADS-B data track smoothing method based on adaptive variable structure according to claim 1, characterized in that: Based on the spline fitting method, the trend term of the equivalent measurement data time series is removed and the error of the equivalent measurement data is calibrated, including: Based on the cubic spline model to represent the equivalent measurement data time series, the track fitting problem is transformed into the problem of estimating the spline coefficients of the cubic spline model. According to the equivalent measurement data and the cubic spline model, the spline coefficient estimate is obtained by the least squares method, the equivalent measurement data estimate is obtained based on the spline coefficient estimate and the cubic spline model, and the spline fitting error is obtained according to the equivalent measurement data and the equivalent measurement data estimate. The spline fitting error is used to represent the degree of conformity between the spline fitting curve and the measurement data curve.

4. The ADS-B data track smoothing method based on adaptive variable structure according to claim 1, characterized in that: A variable structure measurement equation is constructed based on equivalent measurement data to express the functional relationship between the state vector and the measurement vector and changes with the content of the measurement data, including: According to the known quantities of the original track data, measurement equations under different track content structures are respectively constructed to obtain measurement equations under corresponding structures according to the changes in the measurement data content at different times, wherein the different track content structures include: an original track data structure in which the geodetic longitude, geodetic latitude and geodetic altitude are all known, an original track data structure in which the geodetic longitude, geodetic latitude, geodetic altitude and ground speed are all known, an original track data structure in which the geodetic longitude, geodetic latitude, geodetic altitude and climb rate are all known, an original track data structure in which the geodetic longitude, geodetic latitude, geodetic altitude, ground speed and heading angle are all known, an original track data structure in which the geodetic longitude, geodetic latitude, geodetic altitude, ground speed and climb rate are all known, and an original track data structure in which the geodetic longitude, geodetic latitude, geodetic altitude, ground speed and climb rate are all known.

5. The ADS-B data track smoothing method based on adaptive variable structure according to claim 1, characterized in that: Based on the innovation and innovation covariance, determine whether the equivalent measurement data in the current future time is valid, including: First, the Mahalanobis distance of the new information at the current future moment is calculated based on the new information and the new information covariance, and it is determined whether the Mahalanobis distance is greater than a given threshold. If so, the equivalent measurement data at the current future moment is deemed invalid; otherwise, the equivalent measurement data at the current future moment is deemed valid.

6. An ADS-B data track smoothing system based on adaptive variable structure, characterized in that: The method according to claim 1 is implemented, comprising: a data acquisition module, a state analysis module and a track smoothing module, wherein: A data acquisition module is used to acquire equivalent measurement data of ADS-B track data, and remove the trend item of the equivalent measurement data time series based on the spline fitting method and perform error calibration on the equivalent measurement data, wherein the equivalent measurement data includes the ground-fixed system position vector and the northeast sky system velocity vector corresponding to the original track data of the aerial target, and the original track data includes the geodetic longitude, geodetic latitude, geodetic altitude, ground speed, climb rate and heading angle; A state analysis module is used to construct a state equation describing the change process of the state vector of the aerial target based on the constant acceleration of the VTC system, and to construct a variable structure measurement equation based on equivalent measurement data, which is used to represent the functional relationship between the state vector and the measurement vector and changes with the content of the measurement data, wherein the state vector includes the position vector of the earth-fixed system, the velocity vector and the acceleration of the VTC system; The track smoothing module is used to perform track smoothing on the original track data based on the state equation and using Kalman filtering, and to eliminate invalid data points in the track data through the new information chi-square test during track smoothing.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

8. An electronic device, characterized in that: include: at least one processor, and a memory coupled to the at least one processor; The memory stores a computer program, and the computer program can be executed by the at least one processor to implement the method according to any one of claims 1 to 5.

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