A method and device for air traffic control data fusion based on differential thinking

Abstract

The present application relates to an air traffic control data fusion method and device based on differential thinking, which belongs to the field of track data fusion and is used to solve the problem of large computational complexity and high algorithmic difficulty of the current track fusion algorithm. The method includes: obtaining different track data of the same aircraft, wherein the different track data come from different detection devices; in a preset coordinate system, according to each of the track data, determining a corresponding function image; in the preset coordinate system, according to a preset differential element, segmenting multiple function images to obtain multiple fusion areas, each of the fusion areas has an intersection area with at least one of the function images; respectively determining the coordinates corresponding to the track data contained in each of the fusion areas; for each of the fusion areas, according to the coordinates, performing data fusion on the corresponding track data. The technical solution provided by the present application can reduce the computational complexity and algorithmic difficulty, thereby improving the applicability of the method.

Description

Technical Field

[0001] The present application belongs to the field of track data fusion, and in particular, relates to an air traffic control data fusion method and device based on differential thinking. Background Art

[0002] Currently, there are various means of obtaining tracks, which leads to great differences in position format, accuracy, time and period. Therefore, it is urgent to carry out track fusion processing in a targeted manner according to specific needs, so as to further improve the track accuracy of the target and make the positioning result more accurate, which is extremely necessary.

[0003] In the prior art, conventional steps such as standard time alignment, space alignment, track interpolation, and track fusion are adopted.

[0004] However, from a technical point of view, time alignment is very difficult, which increases the computational workload and algorithmic difficulty. Summary of the invention

[0005] In view of the above analysis, this application aims to propose an air traffic control data fusion method and device based on differential thinking, which can reduce the amount of calculation and algorithm difficulty, thereby improving the applicability of the method.

[0006] The purpose of this application is mainly achieved through the following technical solutions:

[0007] On the one hand, the present application provides an air traffic control data fusion method based on differential thinking, including:

[0008] Acquire different track data of the same aircraft, wherein the different track data come from different detection devices;

[0009] In a preset coordinate system, according to each of the track data, a corresponding function graph is determined;

[0010] In the preset coordinate system, the plurality of function images are segmented according to preset differential elements to obtain a plurality of fusion regions, each of the fusion regions having an intersection region with at least one of the function images;

[0011] Respectively determining the coordinates corresponding to the track data contained in each of the fusion areas;

[0012] For each of the fusion areas, data fusion is performed on the corresponding track data according to the coordinates.

[0013] Furthermore, the obtaining of the track data of the same aircraft includes:

[0014] receiving at least one dimension identifier of an external input;

[0015] According to the at least one dimension identifier, corresponding track data is acquired from each of the detection devices.

[0016] Furthermore, the preset coordinate system is a rectangular coordinate system;

[0017] Before determining a plurality of function images in the preset coordinate system according to the track data, the method further comprises:

[0018] The initial coordinate system of the track data is converted into a rectangular coordinate system.

[0019] Furthermore, before segmenting the plurality of function images according to a preset differential element in the preset coordinate system, the method further comprises:

[0020] Determine the function image corresponding to the track data with the highest accuracy as the main function image;

[0021] Determine the starting point coordinates and the ending point coordinates of the main function image;

[0022] Determine a plurality of insertion points according to the coordinates of the starting point and the coordinates of the ending point, wherein the plurality of insertion points are evenly distributed between the starting point and the ending point;

[0023] The preset differential element is determined according to the multiple insertion points.

[0024] Furthermore, the determining of the preset differential element further includes:

[0025] Determine a first boundary function image and a second boundary function image according to the plurality of function images, wherein the plurality of function images are distributed between the first boundary function image and the second boundary function image;

[0026] The preset differential element is determined by using the first boundary function image, the second boundary function image and two adjacent insertion points.

[0027] Furthermore, for each of the fusion areas, data fusion is performed on the corresponding track data according to the coordinates, including:

[0028] In the fusion area, determining fusion coordinates according to the preset coordinate system;

[0029] The fused coordinates are converted into corresponding track fusion data.

[0030] On the other hand, the embodiment of the present application provides an air traffic control data fusion device based on differential thinking, including: an acquisition module, a function image generation module, a differential module and a fusion module;

[0031] The acquisition module is used to acquire the track data of the same aircraft, and the track data comes from different detection devices;

[0032] The function image generation module is used to determine a plurality of function images in a preset coordinate system according to the track data, each of the function images corresponding to a track data of the detection device;

[0033] The differential module is used to segment the multiple function images in the preset coordinate system according to the preset differential element to obtain a fusion area, wherein the fusion area has an intersection area with at least one of the function images; and respectively determine the coordinates corresponding to the track data contained in each of the fusion areas;

[0034] The fusion module is used to perform data fusion on the corresponding track data for each fusion area according to the coordinates.

[0035] Furthermore, the differential module is also used to determine the function image corresponding to the track data with the highest accuracy as the main function image; determine the starting point coordinates and the ending point coordinates of the main function image; determine multiple insertion points based on the starting point coordinates and the ending point coordinates, and the multiple insertion points are evenly distributed between the starting point and the ending point; determine the preset differential element based on the multiple insertion points and the multiple function images; the function image generation module is also used to convert the initial coordinate system of the track data into a rectangular coordinate system.

[0036] Furthermore, the differential module is also used to determine a first boundary function image and a second boundary function image based on the multiple function images, and the multiple function images are distributed between the first boundary function image and the second boundary function image; and to determine the preset differential element using the first boundary function image, the second boundary function image and two adjacent insertion points.

[0037] Furthermore, the fusion module is used to determine fusion coordinates in the fusion area according to the preset coordinate system; and convert the fusion coordinates into corresponding track fusion data.

[0038] Compared with the prior art, this application can achieve at least one of the following technical effects:

[0039] 1. In the preset coordinate system, the track data is converted into a function graph to unify the data format and provide a basis for the subsequent determination of the relationship between the data. In the preset coordinate system, the correspondence between different track data is established by differentiation, and the track data is fused using this correspondence. In summary, when performing data fusion, the present application uses differentiation instead of precise time calibration, and the construction of differentiation does not require complex algorithms and a large number of calculations. Therefore, the technical solution provided by the present application can reduce the amount of calculation and the difficulty of the algorithm, thereby achieving the applicability of the improvement method.

[0040] 2. In order to improve the accuracy of data fusion, the function image corresponding to the track data with the highest accuracy is used as the main function image, and the differential element is determined by the main function image, thereby improving the accuracy of the differential.

[0041] 3. The differential element is constructed by the first boundary function image, the second boundary function image and two adjacent insertion points to facilitate controlling the range of the fusion area and eliminating the interference of impurity data, thereby further reducing the amount of calculation and improving the calculation accuracy.

[0042] 4. When acquiring track data, it can be screened from the database according to preset dimensions. Since the correspondence between track data established based on differentiation can exist independently of the dimensions, the solution provided in this application is suitable for data fusion in various dimensions.

[0043] Other features and advantages of the present application will be described in the subsequent description, and some will become apparent from the description, or will be understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are only for the purpose of illustrating specific embodiments and are not to be considered as limiting the present application. The same reference symbols denote the same components throughout the accompanying drawings.

[0045] Figure 1 A flowchart of an air traffic control data fusion method based on differential thinking provided in an embodiment of the present application;

[0046] Figure 2 A schematic diagram of the positional relationship between a differential element and a function graph is provided for an embodiment of the present application;

[0047] Figure 3 Another schematic diagram of the positional relationship between a differential element and a function graph is provided for an embodiment of the present application;

[0048] Figure 4 A schematic diagram of the structure of a differential element provided in an embodiment of the present application;

[0049] Figure 5A schematic diagram of the positional relationship between the first boundary function image, the second boundary function image and the function image corresponding to the track data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] The preferred embodiments of the present application are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of the present application and are used together with the embodiments of the present application to illustrate the principles of the present application, and are not used to limit the scope of the present application.

[0051] In the prior art, standard time alignment and space alignment are used to fuse track data. Time alignment and space alignment require precise measurement of time and position. However, the more precise the measurement, the more easily the credibility of the measurement result is affected by deviation. For example, when the measurement accuracy is minutes, the relative error caused by a 1-second error is one-sixtieth, and when the measurement accuracy is seconds, the relative error is 1, which is 59 times the difference. Therefore, the prior art needs to set many restrictions when performing time calibration and space calibration, and combine various algorithms to ensure the accuracy of data fusion, thereby increasing the amount of calculation and algorithm difficulty.

[0052] In addition, for aircraft with relatively fixed or even predictable flight paths, the accuracy of data fusion is not high. In the above scenario, the existing technology can hardly achieve anything except increasing the consumption of computing resources.

[0053] In view of the above technical problems, the present application provides an air traffic control data fusion method based on differential thinking, comprising the following steps:

[0054] Step 1: Obtain different track data of the same aircraft.

[0055] In the embodiment of the present application, different track data come from different detection devices, which include: various radars and sensors for obtaining position information, such as ADS-B, surveillance radar 1, and surveillance radar 2, wherein each surveillance radar is set at a different position of the aircraft.

[0056] For the same aircraft, the dimension of track fusion is usually time, airspace range, speed or a combination thereof. For example, the flight trajectory of an aircraft in a certain period of time, in a certain airspace, or at a certain speed is determined by data fusion. In an embodiment of the present application, when acquiring track data, at least one dimension identifier of external input is received; according to the at least one dimension identifier, the corresponding track data is acquired from each detection device respectively to ensure that the acquired track data are all in the same dimension.

[0057] Step 2: In a preset coordinate system, determine multiple function images based on the track data.

[0058] In an embodiment of the present application, in order to establish the relationship between the track data through differentiation, the track data is first converted into a function image in a preset coordinate system. Preferably, the preset coordinate system is a rectangular coordinate system. In actual use, the coordinate system of most track data is a polar coordinate system or a longitude and latitude coordinate system, so before step 2, it is also necessary to convert the initial coordinate system of the track data into a rectangular coordinate system. It should be noted that in the embodiment of the present application, the track data is the data points collected by each device, and the function image is the track line corresponding to the corresponding track data.

[0059] Specifically, taking the three-dimensional coordinate system as an example, the method of converting polar coordinates into rectangular coordinates is:

[0060]

[0061]

[0062] z=rcosθ

[0063] Specifically, the method of converting longitude and latitude coordinates into rectangular coordinates is: from the track data, determine any point as the origin, and then determine the coordinates of other points in the rectangular coordinate system according to the coordinate conversion formula. Taking the three-dimensional coordinate system as an example, take point A as the coordinate origin (0,0,0), and obtain the coordinates of point B (x, y, z) according to the coordinate conversion formula. The coordinate conversion formula is:

[0064] x=111.32*cos(latA*π\180)*(longB-longA)

[0065] y=110.946*(latB-latA)

[0066] z=z-0

[0067] Among them, latB and latA represent the latitude of point A and point B, and longB and longA represent the longitude of point A and point B.

[0068] After the coordinate system conversion is completed, the corresponding function image is obtained according to the track data, wherein each function image corresponds to the track data of a detection device.

[0069] Step 3: In a preset coordinate system, multiple function images are segmented according to preset differential elements to obtain a fusion area.

[0070] In the embodiment of the present application, the differential element is the area corresponding to the smallest segmentation interval during differentiation, and the fusion area and at least one function image have an intersection area. It should be noted that in a rectangular coordinate system, the function image of the track data is usually linear, and when the linear function image enters or passes through the differential element, the area where the differential element is located is determined to be the fusion area.

[0071] In the embodiment of the present application, the differential element can be set according to the x-axis scale of the rectangular coordinate system. However, the trend and direction of the function graph cannot be determined. Therefore, setting the differential element according to the x-axis scale of the rectangular coordinate system will result in only one track data in the fusion area, that is, the fusion result of the area is the measurement value of the device. However, different detection devices have different accuracies. If the device has the lowest accuracy, it will inevitably reduce the overall accuracy of the fusion result.

[0072] Therefore, in the embodiment of the present application, the function image corresponding to the track data with the highest weight during data fusion is selected as the main function image; the starting point coordinates and the ending point coordinates of the main function image are determined; according to the starting point coordinates and the ending point coordinates, multiple insertion points are determined, and the multiple insertion points are evenly distributed between the starting point and the ending point; according to the multiple insertion points and the segmentation interval of the preset differential element, the preset differential element is determined. In this way, as many function images as possible can pass through or enter the fusion area. It should be noted that in the embodiment of the present application, the differential element specifically refers to the segmentation interval used to segment the function image, and the endpoints of the segmentation interval are interpolation points.

[0073] Specifically, let the coordinates of point A be the starting point coordinates, and the coordinates of point B be the ending coordinates. Perform uniform natural number series interpolation between A and B. Assuming that n-1 insertion points need to be inserted, that is, the line end between point A and point B is divided into n segments, then the coordinates of the first interpolation point to the (n-1)th interpolation point are: Then the segmentation interval corresponding to the preset differential element is arrive The value of m is 1, 2, 3...n-1.

[0074] In addition, in order to balance the calculation accuracy and efficiency, the interpolation method between A and B can also be adjusted. Specifically, an odd-numbered column is uniformly interpolated between A and B. Assuming that n-1 interpolation points need to be inserted, the coordinates of the first interpolation point to the (n-1)th interpolation point are: Then the segmentation interval corresponding to the preset differential element is arrive

[0075] Perform uniform interpolation of even columns between A and B. Assuming that n-1 interpolation points need to be inserted, the coordinates of the first interpolation point to the (n-1)th interpolation point are: Then the segmentation interval corresponding to the preset differential element is arrive

[0076] In the embodiment of the present application, the aircraft will shake and fly in a curve during the flight, so the function graph of the track data will definitely not be a straight line. At the same time, each device is installed in a different position, resulting in obvious gaps between the function graphs. Combining the above two points, it can be seen that the number of function graphs intersecting with each differential element is different, which will affect the data fusion accuracy of each differential element.

[0077] Specifically, Figure 2 As shown, function image A represents the track data collected by device A, and function image B represents the track data collected by device B. Since the aircraft floats up and down during the flight, function image A and function image B are both broken lines. The dotted areas 1-3 represent three differential elements respectively. Among them, the heights of differential elements 1-3 are the same, differential element 1 intersects with 2 function images, differential element 2 intersects with 1 function image, and differential element 3 intersects with 0 function images. Obviously, the accuracy of the fusion results of the three differential elements is inconsistent. If the heights of differential elements 1-3 are set to be inconsistent in order to improve the calculation accuracy, such as Figure 3 As shown in the figure, since there are hundreds or thousands of differential elements, the different heights of the differential units will inevitably lead to a sharp increase in the amount of calculation in the process of generating differential elements.

[0078] In order to balance the calculation accuracy and the amount of calculation, the embodiment of the present application determines the first boundary function image and the second boundary function image based on multiple function images. Among them, the multiple function images are distributed between the first boundary function image and the second boundary function image. The preset differential element is determined by using the first boundary function image, the second boundary function image and two adjacent insertion points. In this way, the size of each differential element is the same, and each differential element can intersect with all function images, thereby achieving the purpose of balancing the calculation accuracy and the amount of calculation.

[0079] It should be noted that, since the shaking amplitude of the aircraft is known and the position of each detection device on the aircraft is known, the maximum distance of the track deviation can be calculated in advance, and then in the preset coordinate system, the positions of the first boundary function image and the second boundary function image are determined according to the maximum distance of the track deviation and the distribution of the function image. Preferably, the first boundary function image and the second boundary function image are two straight lines. For example, it is assumed that the track of the aircraft coincides with the axis of symmetry of the aircraft shape at any time, the maximum deviation S1 of the aircraft from the preset track is 500m, the distance S2 between device A and the axis of symmetry is 50m, and the distance S2 between device A and the axis of symmetry is 30m. Then, the maximum track deviation distance of device A is 550m, the maximum track deviation distance of device B is 530m, and the maximum track deviation distance of the aircraft is 550m. After conversion into a rectangular coordinate system, such as Figure 5As shown, according to the function image of device A and the function image of device B as well as S1 and S2, the image L of the symmetry axis of the aircraft is determined, and then according to the maximum distance of the aircraft's track deviation, the first boundary function image T1 and the second boundary function image T2 are determined on both sides of the symmetry axis respectively, wherein Figure 5 In the coordinate system, the vertical distance between L and T1 represents 550m, and the vertical distance between L and T2 represents 550m.

[0080] Specifically, the process of determining the preset differential element using the first boundary function image, the second boundary function image and two adjacent insertion points is as follows: Figure 4 As shown, points 1A, 1B, 1C and 1D constitute a rectangular differential element, where straight lines 1A1D are part of the first boundary function image, straight lines 1B1C are part of the second boundary function image, straight line AB is the main function image, and the distance dmax between 1A and 1B is 2 times the maximum track offset. The focus of straight line AB and straight lines 1A1B and 1D1C are two adjacent interpolation points. Under the condition that the coordinates of point A are known to be (0,0) and the coordinates of point B are known to be (x, y), the coordinates of the vertices 1A, 1B, 1C, 1D of the rectangle corresponding to the differential element are:

[0081] 1A coordinates:

[0082] 1B coordinates:

[0083] 1C coordinates:

[0084] 1D coordinates:

[0085] The equations of the lines for each side are:

[0086] Line 1A1D:

[0087] Straight line 1B1C boundary function:

[0088] Line 1A1B boundary function:

[0089] Straight line 1D1C boundary function:

[0090] Among them, m represents the mth interpolation point, and n represents the total number of interpolation points.

[0091] Step 4: Determine the coordinates corresponding to the track data contained in each fusion area.

[0092] In the embodiment of the present application, since the function image is a track line obtained according to the track data, it includes: coordinates corresponding to the track data and coordinates corresponding to the non-track data.

[0093] In this step 4, what needs to be obtained are the coordinates corresponding to the track data contained in each fusion area.

[0094] Step 5: For each fusion area, perform data fusion on the corresponding track data according to the coordinates.

[0095] In an embodiment of the present application, when fusing a certain area, data fusion can be performed according to the weight of each track based on the track data, wherein the weight of each track depends on the accuracy of each track, and can be obtained in advance using an existing weight allocation algorithm, such as the optimal weight allocation principle. It is also possible to first perform data fusion in preset coordinates to obtain fusion coordinates, and then reversely infer the fused track data through the fusion coordinates. Specifically, based on the data points in the fusion area, determine the minimum area circle that can contain all the data points in the fusion area, and the coordinates of the center of the circle are the fusion coordinates. For example, when the points in the fusion area are on the same straight line, a minimum area circle is constructed with the straight line to which the points belong as the diameter. When each point can construct at least one triangle, the circumscribed circle of each triangle is the minimum area circle.

[0096] The embodiment of the present application provides an air traffic control data fusion device based on differential thinking, including: an acquisition module, a function image generation module, a differential module and a fusion module;

[0097] The acquisition module is used to obtain the track data of the same aircraft, and the track data comes from different detection equipment;

[0098] The function image generation module is used to determine a plurality of function images according to the track data in a preset coordinate system, each function image corresponding to the track data of a detection device;

[0099] The differential module is used to segment multiple function images in a preset coordinate system according to a preset differential element to obtain a fusion area, where the fusion area and at least one function image have an intersection area; and respectively determine the coordinates corresponding to the track data contained in each fusion area;

[0100] The fusion module is used to fuse the corresponding track data according to the coordinates for each fusion area.

[0101] In an embodiment of the present application, the differential module is also used to determine that the function image corresponding to the track data with the highest accuracy is the main function image; determine the starting point coordinates and the ending point coordinates of the main function image; determine multiple insertion points based on the starting point coordinates and the ending point coordinates, and the multiple insertion points are evenly distributed between the starting point and the ending point; determine a preset differential element based on the multiple insertion points and the multiple function images; the function image generation module is also used to convert the initial coordinate system of the track data into a rectangular coordinate system.

[0102] In an embodiment of the present application, the differential module is also used to determine a first boundary function image and a second boundary function image based on multiple function images, and the multiple function images are distributed between the first boundary function image and the second boundary function image; and a preset differential element is determined using the first boundary function image, the second boundary function image and two adjacent insertion points.

[0103] In the embodiment of the present application, the fusion module is used to determine the fusion coordinates in the fusion area according to a preset coordinate system; and convert the fusion coordinates into corresponding track fusion data.

[0104] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed in the present application should be covered within the protection scope of the present application.

Claims

1. A method for air traffic control data fusion based on differential thinking, characterized in that: include: Acquire different track data of the same aircraft, wherein the different track data come from different detection devices; In a preset coordinate system, according to each of the track data, a corresponding function graph is determined; In the preset coordinate system, the plurality of function images are segmented according to preset differential elements to obtain a plurality of fusion regions, each of the fusion regions having an intersection region with at least one of the function images; Respectively determining the coordinates corresponding to the track data contained in each of the fusion areas; For each of the fusion areas, performing data fusion on the corresponding track data according to the coordinates; Wherein, in the preset coordinate system, before segmenting the plurality of function images according to the preset differential element, the method further comprises: Determine the function image corresponding to the track data with the highest accuracy as the main function image; Determine the starting point coordinates and the ending point coordinates of the main function image; Determine a plurality of insertion points according to the coordinates of the starting point and the coordinates of the ending point, wherein the plurality of insertion points are evenly distributed between the starting point and the ending point; The preset differential element is determined according to the multiple insertion points and the multiple function images.

2. The method according to claim 1, characterized in that: The obtaining of the track data of the same aircraft includes: receiving at least one dimension identifier of external input; According to the at least one dimension identifier, corresponding track data is acquired from each of the detection devices.

3. The method according to claim 1, characterized in that The preset coordinate system is a rectangular coordinate system; Before determining a plurality of function images in the preset coordinate system according to the track data, the method further comprises: The initial coordinate system of the track data is converted into a rectangular coordinate system.

4. The method according to claim 1, characterized in that: The determining of the preset differential element further includes: Determine a first boundary function image and a second boundary function image according to the plurality of function images, wherein the plurality of function images are distributed between the first boundary function image and the second boundary function image; The preset differential element is determined by using the first boundary function image, the second boundary function image and two adjacent insertion points.

5. The method according to any one of claims 1 to 4, characterized in that: For each of the fusion areas, according to the coordinates, data fusion is performed on the corresponding track data, including: In the fusion area, determining fusion coordinates according to the preset coordinate system; The fused coordinates are converted into corresponding track fusion data.

6. An air traffic control data fusion device based on differential thinking, characterized in that: include: Acquisition module, function image generation module, differentiation module and fusion module; The acquisition module is used to acquire the track data of the same aircraft, and the track data comes from different detection devices; The function image generation module is used to determine a plurality of function images in a preset coordinate system according to the track data, each of the function images corresponding to a track data of the detection device; The differential module is used to segment the multiple function images in the preset coordinate system according to the preset differential element to obtain a fusion area, and the fusion area has an intersection area with at least one of the function images; Respectively determining the coordinates corresponding to the track data contained in each of the fusion areas; The fusion module is used for performing data fusion on the corresponding track data for each fusion area according to the coordinates; The differential module is also used to determine the function image corresponding to the track data with the highest accuracy as the main function image; determine the starting point coordinates and the ending point coordinates of the main function image; determine a plurality of insertion points according to the starting point coordinates and the ending point coordinates, and the plurality of insertion points are evenly distributed between the starting point and the ending point; determine the preset differential element according to the plurality of insertion points and the plurality of function images; The function image generation module is also used to convert the initial coordinate system of the track data into a rectangular coordinate system.

7. The device according to claim 6, characterized in that The differential module is also used to determine a first boundary function image and a second boundary function image based on the multiple function images, and the multiple function images are distributed between the first boundary function image and the second boundary function image; and to determine the preset differential element using the first boundary function image, the second boundary function image and two adjacent insertion points.

8. The device according to claim 6, characterized in that The fusion module is used to determine fusion coordinates in the fusion area according to the preset coordinate system; and convert the fusion coordinates into corresponding track fusion data.

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