CN114357565A - Airport scene surveillance processing method based on grid 5D data model - Google Patents

Abstract

The invention discloses an airport scene monitoring and processing method based on a grid 5D data model. The steps are as follows: using a 3-dimensional global longitude and latitude grid method to perform grid modeling and digital coding on the airport scene and the nearby approach airspace; Grid modeling and digitizing of airport elements are carried out, and a mapping relationship is established with grid codes; time and business attribute labels are assigned in units of airport elements, and then the grid attributes of airport elements are extended from 3 dimensions to 5 dimensions; Fusion and optimization of multi-source surveillance data from 5-dimensional airport grid data. The invention realizes the refinement, structuring, accuracy and perfection of airport scene data, and automation of partial data setting, provides more stable, reliable and accurate scene target monitoring reference information for tower controllers, and improves the safety and efficiency of scene operation.

Description

Airport scene surveillance processing method based on grid 5D data model

technical field

The invention belongs to the technical field of airport scene control, and specifically refers to an airport scene monitoring and processing method based on a grid 5D data model.

Background technique

In order to ensure the safe, efficient and orderly operation of aircraft and vehicles on the airport surface, the tower controller needs the airport surface control system to provide accurate, stable and reliable airport surface and nearby airspace targets (aircraft, vehicles) operation situation monitoring, conflict risk warning, etc. Information for safe and efficient command and management, such as the Advanced Surface Guidance and Control System (A-SMGCS), the accuracy, stability, and reliability of the airport surface surveillance data processing of the tower control automation system, in addition to high-quality surveillance source signals , to a large extent rely on the completeness, accuracy, refinement and structure of the basic data of the airport scene and nearby airspace to ensure the accuracy of multi-source surveillance data fusion parameter settings.

However, at present, the basic data of the airport scene adopts a data import system based on the CAD map of the airport. There are problems such as insufficient precision and accuracy of the basic airport data, lack of structure and uniformity, incomplete data, purely manual production and setting, time-consuming, and error-prone. , affecting the accuracy, stability, and reliability of subsequent airport surface surveillance data processing, resulting in poor or incorrect fusion of multi-source surveillance data on the surface, and it is difficult to eliminate the problems of false, split, and jumped surface targets, which seriously affects the controller's performance. Airport control and command, causing security problems.

SUMMARY OF THE INVENTION

In view of the above-mentioned deficiencies in the prior art, the object of the present invention is to provide a method for monitoring and processing airport scenes based on a grid 5D data model, so as to solve the problem in the prior art that the basic data of the airport scene adopts a data import system based on the airport CAD map. , there are problems that the basic data of the airport is not precise and accurate, the data lacks structure and uniformity, the data is not perfect, the purely manual production and setting are time-consuming, and it is prone to errors, and the fusion effect of multi-source surveillance data on the scene is not good or wrong. question.

For achieving the above object, the technical scheme adopted in the present invention is as follows:

A kind of airport scene monitoring processing method based on grid 5D data model of the present invention, the steps are as follows:

1) Using the 3D global latitude and longitude grid method to carry out grid modeling and grid digital coding of the airport surface and the nearby approach airspace;

2) Carry out grid modeling and digital numbering of airport elements, and store them in layers to build a database table with the airport element number as the main key. Each airport element is composed of several grids, and establishes a mapping with the grid coding in step 1). Correspondence;

3) The airport element database table is assigned time and business attribute marks with the airport element as a unit, and then the grid attribute of the airport element is extended from 3 dimensions to 5 dimensions;

4) Fusion and optimization of multi-source surveillance data based on 5-dimensional data of airport grid.

Further, the step 1) specifically includes: dividing the approach airspace near the scene and the airport into gapless grid models that are contained in different scale levels, constructing the scene and airspace grid models, and coding the grids of each level. , which constitutes a grid network system framework with one-to-one mapping of coding and grid, and forms a new way of organizing and describing the airport scene and the nearby approach airspace; the attributes of each grid unit only contain basic 3-dimensional data.

The scene and nearby approach airspace coding adopts Geo SOT quaternary one-dimensional coding, and adopts Z-curve sequence coding. The specific implementation rule of scene and airspace coding is to divide the coding area into 4 equal parts, and the latitude and longitude spanned by the 4 sub-areas are all the same. The coding of the sub-regions continues to increase the coding according to the Z sequence of the corresponding quadrant; the rasterized modeling of the scene and airspace in the global latitude range of -60°～60° and the longitude range of 0°～360°, the size of the area is 1°×1° The sub-range space is expanded to 64′×64′, and the sub-range space is expanded to 64″×64″ when the area size is 1′×1′; the scene and coding are continuously subdivided according to the above rules, and the corresponding area is also reduced accordingly; Airport The nearby approach airspace is divided into 16 layers of horizontal grids and 8 layers of height grids; the airport scene is divided into 32 layers of horizontal grids, and the area corresponding to the 32nd layer code is a square with a side length of 1.5cm near the equator.

Further, for a single airport, only the surface area of the airport is 5km×5km, the airspace near the airport is 20km×10km, and the height is 0-600m.

Further, in the step 2), satellite image data is used to carry out actual survey and measurement coordinate calibration, to realize the digitization of the airport map, and to include the airport elements: the runway, taxiway, apron, and parameter setting area of the airport, and to carry out grid modeling and Digitize the numbers and store them in layers to build a database table with the airport element number as the main key. Each airport element is composed of several grids, and a mapping relationship is established with the grid code in step 1).

Further, the hierarchical organization storage in the step 2) is subdivided into the minimum processing unit required by the system.

Further, the five dimensions in the step 3) include: longitude X, latitude Y, altitude H, time T, and service attribute F.

Further, the step 3) specifically includes:

31) Time assignment of airport elements: according to the data received and processed, the airport elements are automatically adjusted and assigned to the time dynamically, or the assignment is manually set; the start and end times are set for the elements with known effective time; the start and end times are set for the elements with unknown effective time. , when triggered, enable and disable changes;

32) Assignment of service attributes of airport elements: According to the needs of airport surface surveillance services, each airport element has different service attributes, which can be set manually or automatically.

Further, the step 32) specifically includes:

Assign weighting coefficients to the fusion parameters of each single surveillance source participating in the fusion calculation of multi-source surveillance data on the airport surface; assign values to the fusion parameter setting area of the airport surface and nearby approach airspace, including the types of surveillance sources involved in each area, the types of surveillance sources involved in the fusion The weighting coefficient of the source participating in the fusion; the monitoring data quality analysis method is used to count and analyze the content, format, true north loss, true north time deviation, and position offset of the data item, and the weight of each monitoring source participating in the fusion is automatically assigned. , the higher the quality of the monitoring source, the larger the weighting coefficient, the lower the quality, the smaller the weighting coefficient, and the coefficient is 0, that is, it does not participate in the fusion.

Further, the step 4) specifically includes: based on the 5-dimensional grid data of the airport scene and the nearby approach airspace fusion parameter setting area in step 32), to realize the assignment of the fusion weight of each monitoring source participating in the fusion.

Further, the step 4) specifically also includes:

41) Set up regional grid 5-dimensional data based on the fusion parameters of the airport scene and the nearby approach airspace, including the longitude X, latitude Y, altitude H, time T, service attribute F of each grid, and according to the target type, location, altitude Dynamically adjust the size of the fusion window;

42) When the position difference, velocity difference, and height difference reported by each monitoring source are all smaller than the fusion window, compare the consistency of the fusion factors of the monitoring sources simultaneously, and perform factor-weighted fusion by each monitoring source fusion coefficient based on the grid 5-dimensional data;

43) When calculating the comprehensive track position, the correlation between the reported position of the signal source and the airport element in the 5-dimensional data of the airport grid will be judged in real time, combined with the geographic data of the airport element and the movement trend of the historical track, in order to ensure the authenticity of the track. On the premise of and maneuverability, error correction and jitter suppression are performed on the small amplitude position error reported by the signal source to improve the stability and smoothness of the comprehensive track.

Further, the surveillance sources participating in the fusion include: Surface Surveillance Radar (SMR), Multi-Point Relative Positioning (MLAT), Automatic Dependent Surveillance-Broadcast (ADS-B), and air traffic radar.

Further, the fusion factor of the monitoring source includes: report track number, address code, and secondary code.

Beneficial effects of the present invention:

Based on the grid 5-dimensional data model, the invention realizes the fine 5-dimensional (longitude, latitude, height, time, business attributes) gridization of the airport scene, and realizes the refinement, structuring, accuracy, perfection and partial data setting of the airport scene data. Automatic, and based on the grid 5-dimensional data fusion optimization processing of multi-source surveillance data, improve the accuracy, stability and reliability of the comprehensive track after the fusion of multi-source surveillance data, and provide controllers with accurate, stable and reliable airports The surface and nearby approach airspace target operation situation monitoring reference information to improve the safety and efficiency of airport surface operation.

Description of drawings

Figure 1 is a flow chart of the method of the present invention.

Detailed ways

In order to facilitate the understanding of those skilled in the art, the present invention will be further described below with reference to the embodiments and the accompanying drawings, and the contents mentioned in the embodiments are not intended to limit the present invention.

Referring to Fig. 1, a kind of airport scene monitoring processing method based on grid 5D data model of the present invention, the steps are as follows:

1) Using the 3D Global Longitude and Latitude Grid (GeoSOT-3D) method to carry out grid modeling and grid digital coding of the airport surface and the nearby approach airspace;

Wherein, the step 1) specifically includes: dividing the airport scene and the nearby approach airspace into gapless grid models that contain different scale levels, constructing the airport and airspace grid models, and coding the grids of each level, It forms a grid network system framework with one-to-one mapping of coding and grid, and forms a new way of organizing and describing the airport scene and the nearby approach airspace; at this time, the attributes of each grid unit only contain basic 3-dimensional data (longitude X, Latitude Y, altitude H).

In the example, Geo SOT quaternary one-dimensional coding is used for the coding of the scene and the nearby approach airspace, and Z-curve sequence coding is used. In the same way, the coding of the four sub-regions continues to increase the coding according to the Z sequence of the corresponding quadrants; the rasterized modeling of the airports and airspaces with a global latitude range of -60° to 60° and a longitude range of 0° to 360° is carried out, and the area size is 1°. The sub-range space of ×1° is expanded to 64′×64′, and the sub-range space of the size of 1′×1′ is expanded to 64″×64″; the airport and airspace coding is continuously subdivided according to the above rules, and the corresponding area is also corresponding The approach airspace near the airport is divided into 16 layers of horizontal grids and 8 layers of height grids; the airport scene is divided into 32 layers of horizontal grids, and the area corresponding to the 32nd layer code is a square with a side length of 1.5cm near the equator.

In the example, for a single airport, only the airport surface area is 5km × 5km, the airspace near the airport is 20km (runway extension line) × 10km, and the height is 0-600m to save database storage space.

2) Use satellite image data for actual survey and measurement coordinate calibration, realize airport map digitization, and conduct grid modeling and digitization of airport elements such as runways, taxiways, apron, parameter setting areas (including airport scene and airspace), etc. number, and organize storage in layers, build a database table with the airport element number as the main key, each airport element is composed of several grids, and establishes a mapping relationship with the grid code in step 1);

Specifically, in the step 2), satellite image data is used to perform actual survey and measurement coordinate calibration, to realize the digitization of the airport map, and the airport elements include: the runway, taxiway, apron, and parameter setting area of the airport (including the airport scene and airspace) , for raster modeling and digitizing numbering.

3) The airport element database table is assigned time and business attribute marks with the airport element as a unit, and then the grid attribute of the airport element is extended from 3 dimensions to 5 dimensions;

The five dimensions include: longitude X, latitude Y, altitude H, time T, and service attribute F.

Specifically, the step 3) specifically includes:

31) Time assignment of airport elements: according to the data received and processed, the airport elements are automatically adjusted and assigned to the time dynamically, or the assignment is manually set; the start and end times are set for the elements with known effective time; the start and end times are set for the elements with unknown effective time. , when triggered, enable and disable changes. Manual settings can be set by means of data management man-machine interface settings, airport digital map layer selection, mouse selection of elements, and drawing scope.

32) Assignment of business attributes of airport elements: According to the needs of airport scene monitoring and processing business, each airport element has different business attributes, which can be set manually or automatically.

Specifically, the step 32) specifically includes:

Assign weighting coefficients to the fusion parameters of each single surveillance source participating in the fusion calculation of multi-source surveillance data on the airport surface; assign values to the fusion parameter setting area of the airport surface and nearby approach airspace, including the types of surveillance sources involved in each area, the types of surveillance sources involved in the fusion The weighting coefficient of the source participating in the fusion; the monitoring data quality analysis method is used to count and analyze the content, format, true north loss, true north time deviation, and position offset of the data item, and the weight of each monitoring source participating in the fusion is automatically assigned. , the higher the quality of the monitoring source, the larger the weighting coefficient, the lower the quality, the smaller the weighting coefficient, the coefficient is 0, that is, it does not participate in the fusion; manual settings can be set manually by means of data management man-machine interface settings and airport digital map drawing to set fusion areas. parameter.

4) Fusion and optimization of multi-source surveillance data based on 5-dimensional airport grid data;

Wherein, the step 4) specifically includes: based on the 5-dimensional grid data of the airport scene and the nearby approach airspace fusion parameter setting area in step 32), to realize the surface surveillance radar (SMR), multi-point correlation of each participating fusion Assignment of fusion weights of surveillance sources such as positioning (MLAT), automatic dependent surveillance-broadcast (ADS-B), air traffic control radar, etc.; create conditions for the improvement of multi-source surveillance fusion technology and the improvement of track quality after fusion; adopt grid-based 5 The dimensional data variable window fusion mechanism realizes the refinement of multi-source surveillance fusion processing, making the integrated track after fusion more stable and smooth, and reducing the occurrence of target splitting and target falsehood.

In the example, it also includes:

41) Due to the lack of basic data and lack of refinement in traditional multi-source surveillance data fusion processing, a fixed fusion window is used, and the fusion window cannot be adjusted dynamically according to the actual operation situation, resulting in the integrated track jitter, lost points, false targets, etc. after fusion. Target splitting occurs; 5-dimensional data of regional grids are set based on the fusion parameters of the airport surface and the nearby approach airspace, including the longitude X, latitude Y, altitude H, time T, service attribute F (type of monitoring source, weighting coefficient), and dynamically adjust the size of the fusion window according to the target type, location, and height;

42) When the position difference, velocity difference, and height difference reported by each monitoring source are smaller than the fusion window, compare the consistency of the fusion factors such as track number, address code, secondary code, etc. reported by the monitoring source, through the grid based 5-dimensional data. The fusion coefficients of each monitoring source are factor-weighted fusion;

43) When calculating the comprehensive track position, the correlation between the reported position of the signal source and the airport elements (such as runway centerline, taxiway centerline, lawn, etc.) in the 5-dimensional data of the airport grid will be judged in real time, combined with the geographic data of the airport elements And the movement trend of the historical track, on the premise of ensuring the authenticity and maneuverability of the track, the small amplitude position error reported by the signal source is corrected and the jitter is suppressed to improve the stability and smoothness of the comprehensive track.

There are many specific application ways of the present invention, and the above are only the preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, without departing from the principle of the present invention, several improvements can be made. These Improvements should also be considered as the protection scope of the present invention.

Claims (7)

1. an airport scene monitoring processing method based on grid 5D data model, is characterized in that, step is as follows: 1) Using the 3D global latitude and longitude grid method to carry out grid modeling and grid digital coding of the airport surface and the nearby approach airspace; 2) Carry out grid modeling and digital numbering of airport elements, and store them in layers to build a database table with the airport element number as the main key. Each airport element is composed of several grids, and establishes a mapping with the grid coding in step 1). Correspondence; 3) The airport element database table is assigned time and business attribute marks with the airport element as a unit, and then the grid attribute of the airport element is extended from 3 dimensions to 5 dimensions; 4) Fusion and optimization of multi-source surveillance data based on 5-dimensional data of airport grid. 2. the airport scene monitoring processing method based on the grid 5D data model according to claim 1, is characterized in that, described step 1) specifically comprises: the airport scene and the nearby approach airspace are divided into different scale levels containing no Gap grid model, construct the scene and airspace grid model, encode the grid at each level, and form a grid network system framework with one-to-one mapping of coding and grid, forming a new airport scene and nearby approach How the airspace is organized and described; each raster cell attribute contains only the underlying 3D data. 3. the airport scene monitoring processing method based on grid 5D data model according to claim 1, is characterized in that, in described step 2), adopt satellite image data to carry out actual survey and survey coordinate calibration, realize airport map digitization, airport The elements include: airport runways, taxiways, apron, parameter setting area, grid modeling and digital numbering, and hierarchical storage, building a database table with the airport element number as the main key, each airport element is composed of several grids. composition, and establish a mapping corresponding relationship with the grid coding in step 1). 4. the airport scene monitoring processing method based on grid 5D data model according to claim 1, is characterized in that, described step 3) specifically comprises: 31) Time assignment of airport elements: according to the data received and processed, the airport elements are automatically adjusted and assigned to the time dynamically, or the assignment is manually set; the start and end times are set for the elements with known effective time; the start and end times are set for the elements with unknown effective time. , when triggered, enable and disable changes; 32) Assignment of service attributes of airport elements: According to the needs of airport surface surveillance services, each airport element has different service attributes, which can be set manually or automatically. 5. the airport scene monitoring processing method based on grid 5D data model according to claim 4, is characterized in that, described step 32) comprises the following steps: Assign weighting coefficients to the fusion parameters of each single surveillance source participating in the fusion calculation of multi-source surveillance data on the airport surface; assign values to the fusion parameter setting area of the airport surface and nearby approach airspace, including the types of surveillance sources involved in each area, the types of surveillance sources involved in the fusion The weighting coefficient of the source participating in the fusion; the monitoring data quality analysis method is used to count and analyze the content, format, true north loss, true north time deviation, and position offset of the data item, and the weight of each monitoring source participating in the fusion is automatically assigned. , the higher the quality of the monitoring source, the larger the weighting coefficient, the lower the quality, the smaller the weighting coefficient, and the coefficient is 0, that is, it does not participate in the fusion. 6. the airport scene monitoring processing method based on grid 5D data model according to claim 5, is characterized in that, described step 4) specifically comprises: based on the airport scene in step 32) and nearby approach airspace fusion parameter setting The grid 5-dimensional data of the region realizes the assignment of the fusion weight of each monitoring source participating in the fusion. 7. the airport scene monitoring processing method based on grid 5D data model according to claim 6, is characterized in that, described step 4) specifically also comprises: 41) Set up regional grid 5-dimensional data based on the fusion parameters of the airport scene and the nearby approach airspace, including the longitude X, latitude Y, altitude H, time T, service attribute F of each grid, and according to the target type, location, altitude Dynamically adjust the size of the fusion window; 42) When the position difference, velocity difference, and height difference reported by each monitoring source are all smaller than the fusion window, compare the consistency of the fusion factors of the monitoring sources simultaneously, and perform factor-weighted fusion by each monitoring source fusion coefficient based on the grid 5-dimensional data; 43) When calculating the comprehensive track position, the correlation between the reported position of the signal source and the airport element in the 5-dimensional data of the airport grid will be judged in real time, combined with the geographic data of the airport element and the movement trend of the historical track, in order to ensure the authenticity of the track. On the premise of and maneuverability, error correction and jitter suppression are performed on the small amplitude position error reported by the signal source to improve the stability and smoothness of the comprehensive track.

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