Airport Surface Surveillance Processing Method Based on Grid 5D Data Model

Abstract

The present invention discloses an airport surface surveillance processing method based on a grid 5D data model, and the steps are as follows: adopting a 3D global longitude and latitude subdivision grid method to perform grid modeling and digital encoding on the airport surface and the nearby approach airspace; performing grid modeling and digital numbering on airport elements, and establishing a mapping correspondence relationship with the grid encoding; taking airport elements as units to perform time and service attribute marking assignments, and further extending the grid attributes of airport elements from 3D to 5D; performing fusion and optimization processing on multi-source surveillance data based on the airport grid 5D data. The present invention realizes the refinement, structuring, accuracy, perfection, and partial data setting automation of airport surface data, provides more stable, reliable, and accurate surface target surveillance reference information for tower controllers, and improves surface operation safety and efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of airport surface control, and specifically refers to an airport surface surveillance and processing method based on a grid 5D data model. Background Art

[0002] To ensure the safe, efficient, and orderly operation of aircraft and vehicles on the airport surface, tower controllers need the airport surface control system to provide accurate, stable, and reliable information such as the operation status monitoring and conflict risk warning of targets (aircraft, vehicles) on the airport surface and nearby airspace for safe and efficient command and management. For example, the accuracy, stability, and reliability of the airport surface surveillance data processing of the Advanced Surface Movement Guidance and Control System (A-SMGCS) and the tower control automation system depend largely on the perfection, accuracy, refinement, and structuring of the basic data of the airport surface and nearby airspace, so as to ensure the accuracy of the multi-source surveillance data fusion parameter settings.

[0003] However, at present, the basic data of the airport surface uses manual data extraction based on the airport CAD map and imports it into the system, which has problems such as insufficient fineness and accuracy of the airport basic data, lack of structuring and unity of the data, incomplete data, pure manual production and setting, long time consumption, and easy errors. These problems affect the accuracy, stability, and reliability of the subsequent airport surface surveillance data processing, resulting in poor or incorrect multi-source surveillance data fusion on the surface, and it is difficult to eliminate problems such as false, split, and jump of surface targets, seriously affecting the airport control command of controllers and causing safety problems. Summary of the Invention

[0004] Aiming at the deficiencies of the above-mentioned existing technologies, the purpose of the present invention is to provide an airport surface surveillance and processing method based on a grid 5D data model to solve the problems in the existing technology that the basic data of the airport surface uses manual data extraction based on the airport CAD map and imports it into the system, including insufficient fineness and accuracy of the airport basic data, lack of structuring and unity of the data, incomplete data, pure manual production and setting, long time consumption, easy errors, and the problem of poor or incorrect multi-source surveillance data fusion on the surface.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0006] An airport surface surveillance and processing method based on a grid 5D data model of the present invention comprises the following steps:

[0007] 1) Use a 3D global longitude and latitude dissection grid method to perform grid modeling and grid digital encoding on the airport surface and nearby approach airspace;

[0008] 2) Perform raster modeling and digital numbering on airport elements, store them hierarchically, construct a database table with the airport element number as the primary key. Each airport element consists of several rasters, and a mapping correspondence is established with the raster coding in step 1).

[0009] 3) Mark and assign time and business attributes to the airport element database table in units of airport elements, thereby extending the raster attributes of airport elements from 3D to 5D.

[0010] 4) Based on the 5D data of airport rasters, perform fusion and optimization processing on multi-source surveillance data.

[0011] Further, step 1) specifically includes: dividing the surface area and the approach airspace near the airport into gapless raster models at different scale levels, constructing the surface and airspace raster models, encoding each level of grid, and forming a raster network system framework with a one-to-one mapping between the coding and the grid, thereby forming a new description method for the airport surface and the nearby approach airspace; each raster unit attribute only contains basic 3D data.

[0012] The coding of the surface area and the nearby approach airspace adopts the Geo SOT quaternary one-dimensional coding and the Z-curve sequential coding. The specific implementation rule for the coding of the surface area and the airspace is to divide the coding area into 4 equal parts. The longitude and latitude spanned by the 4 sub-areas are the same, and the coding of the 4 sub-areas continues to increase according to the Z-order of the corresponding quadrants; for the surface area and the airspace with a global latitude range of -60° to 60° and a longitude range of 0° to 360°, raster modeling is performed. In the sub-range space with a size of 1°×1°, it is extended to 64′×64′, and in the sub-range space with a size of 1′×1′, it is extended to 64″×64″; the surface area and the coding are continuously subdivided according to the above rules, and the corresponding area is also reduced accordingly; the approach airspace near the airport is divided into 16 horizontal grids and 8 height grids; the airport surface is divided into 32 horizontal grids, and the area corresponding to the 32nd level coding is a square with a side length of 1.5 cm near the equator.

[0013] Further, for a single airport, only intercept the airport surface area of 5 km×5 km, the nearby airspace of 20 km×10 km, and the height of 0 - 600 m.

[0014] Further, in step 2), satellite image data is used for actual survey and measurement coordinate calibration to realize the digitalization of the airport map. The airport elements include: the runway, taxiway, apron, and parameter setting area of the airport. Perform raster modeling and digital numbering, store them hierarchically, construct a database table with the airport element number as the primary key. Each airport element consists of several rasters, and a mapping correspondence is established with the raster coding in step 1).

[0015] Further, the hierarchical organization storage in step 2) is subdivided into the smallest unit required for system processing.

[0016] Further, the five dimensions in step 3) include: longitude X, latitude Y, altitude H, time T, and service attribute F.

[0017] Further, step 3) specifically includes:

[0018] 31) Airport element time assignment: Automatically perform dynamic time adjustment and assignment for airport elements according to the received and processed data, or manually set the assignment; set start and end times for elements with known effective times; set start times for elements with unknown effective times, and when triggered, perform enable / disable changes;

[0019] 32) Airport element service attribute assignment: According to the needs of airport surface surveillance services, each airport element has different service attributes, which can be set manually or automatically.

[0020] Further, step 32) specifically includes:

[0021] Assign weighted coefficients to the fusion parameters of each single surveillance source participating in the multi-source surveillance data fusion calculation of the airport surface; assign values to the areas where the fusion parameters of the airport surface and the nearby approach airspace are set, including the types of surveillance sources participating in the fusion in each area and the weighted coefficients of each surveillance source participating in the fusion; use the surveillance data quality analysis method to perform statistics and analysis on the content, format, north loss, north time deviation, and position offset of the data items, and automatically assign weights to the participation of each surveillance source in the fusion. The higher the quality of the surveillance source, the larger the weighted coefficient, and the lower the quality, the smaller the weighted coefficient. If the coefficient is 0, it means not participating in the fusion.

[0022] Further, step 4) specifically includes: Based on the grid five-dimensional data of the area where the fusion parameters of the airport surface and the nearby approach airspace are set in step 32), assign fusion weights to each surveillance source participating in the fusion.

[0023] Further, step 4) specifically also includes:

[0024] 41) Based on the grid five-dimensional data of the area where the fusion parameters of the airport surface and the nearby approach airspace are set, including the longitude X, latitude Y, altitude H, time T, and service attribute F of each grid, and dynamically adjust the size of the fusion window according to the target type, position, and altitude;

[0025] 42) When the position difference, speed difference, and altitude difference reported by each surveillance source are all less than the fusion window, and at the same time, compare the consistency of the fusion factors of the surveillance sources, perform factor weighted fusion through the fusion coefficients of each surveillance source based on the grid five-dimensional data;

[0026] 43) When calculating the integrated track position, the correlation between the signal source reported position and the airport element in the airport grid 5D data will be determined in real time. Combined with the airport element geographic data and the movement trend of the historical track, small position errors reported by the signal source will be corrected and jitter suppressed while ensuring the authenticity and maneuverability of the track, so as to improve the stability and smoothness of the integrated track.

[0027] Furthermore, the monitoring sources involved in the fusion include: surface surveillance radar (SMR), multi-point location tracking (MLAT), automatic dependent surveillance-broadcast (ADS-B), and air traffic control radar.

[0028] Furthermore, the fusion factors of the monitoring source include: reporting track number, address code, and secondary code.

[0029] Beneficial effects of the present invention:

[0030] The present invention realizes the fine 5-dimensional (longitude, latitude, altitude, time, business attributes) rasterization of the airport scene based on the grid 5-dimensional data model, realizes the refinement, structuring, accuracy, completeness and automation of partial data setting of the airport scene data, and optimizes the fusion processing of multi-source surveillance data based on the grid 5-dimensional data, improves the accuracy, stability and reliability of the comprehensive track after the fusion of multi-source surveillance data, provides controllers with accurate, stable and reliable reference information for monitoring the operation status of the airport scene and nearby approach airspace targets, and improves the safety and efficiency of airport scene operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0032] In order to facilitate the understanding of those skilled in the art, the present invention is further described below in conjunction with embodiments and drawings. The contents mentioned in the implementation modes are not intended to limit the present invention.

[0033] Reference Figure 1 As shown, a method for airport scene monitoring and processing based on a grid 5D data model of the present invention comprises the following steps:

[0034] 1) Use the 3D global latitude and longitude gridding (GeoSOT-3D) method to perform grid modeling and grid digital coding on the airport surface and nearby approach airspace;

[0035] Among them, the specific steps of step 1) include: dividing the airport surface and the nearby approach airspace into a gapless grid model with different scale levels, constructing an airport and airspace grid model, encoding each level of grid, and forming a grid network system framework with a one-to-one mapping between the encoding and the grid, thus forming a new description method for the airport surface and the nearby approach airspace; at this time, the attributes of each grid unit only include the basic three-dimensional data (longitude X, latitude Y, altitude H).

[0036] In the example, the encoding of the surface and the nearby approach airspace adopts the Geo SOT quaternary one-dimensional encoding and the Z-curve sequential encoding. The specific implementation rule of the airport and airspace encoding is to divide the encoding area into four equal parts. The longitude and latitude spanned by the four sub-areas are the same, and the encoding of the four sub-areas continues to increase according to the Z-order of the corresponding quadrants; for the airport and airspace with a global latitude range of -60° to 60° and a longitude range of 0° to 360°, grid modeling is carried out. In the sub-range space with a size of 1°×1°, it is extended to 64′×64′, and in the sub-range space with a size of 1′×1′, it is extended to 64″×64″; the airport and airspace encoding is continuously subdivided according to the above rules, and the corresponding area is also reduced accordingly; the nearby approach airspace of the airport is divided into 16 horizontal grids and 8 height grids; the airport surface is divided into 32 horizontal grids, and the area corresponding to the 32nd level encoding is a square with a side length of 1.5 cm near the equator.

[0037] In the example, for a single airport, only the range of 5 km×5 km of the airport surface and 20 km (in the runway extension direction)×10 km of the nearby airspace of the airport with a height of 0 - 600 m are intercepted to save the storage space of the database.

[0038] 2) Use satellite image data for actual survey and measurement coordinate calibration to digitalize the airport map. Grid modeling and digital numbering are carried out for airport elements such as the runway, taxiway, apron, and parameter setting areas (including the airport surface and airspace) of the airport, and they are hierarchically organized and stored to construct a database table with the airport element number as the primary key. Each airport element consists of several grids, which establish a mapping correspondence with the grid encoding in step 1).

[0039] Specifically, in step 2), satellite image data is used for actual survey and measurement coordinate calibration to digitalize the airport map. The airport elements include: the runway, taxiway, apron, and parameter setting areas (including the airport surface and airspace) of the airport, and grid modeling and digital numbering are carried out.

[0040] 3) Mark and assign time and business attributes to the airport element database table in units of airport elements, thereby extending the grid attributes of airport elements from three dimensions to five dimensions.

[0041] Among them, the five dimensions include: longitude X, latitude Y, altitude H, time T, and service attribute F.

[0042] Specifically, step 3) specifically includes:

[0043] 31) Airport element time assignment: Automatically perform dynamic time adjustment and assignment for airport elements according to the received and processed data, or manually set the assignment; set start and end times for elements with known effective times; set start times for elements with unknown effective times, and when triggered, perform enable / disable changes. Manual setting can be done through methods such as data manager human-machine interface setting, airport digital map layer selection, mouse selection of elements, and drawing of ranges.

[0044] 32) Airport element service attribute assignment: According to the needs of airport surface surveillance processing operations, each airport element has different service attributes, which can be set manually or automatically.

[0045] Specifically, step 32) specifically includes:

[0046] Assign weight coefficients to the fusion parameters of each single surveillance source participating in the multi-source surveillance data fusion calculation for the airport surface; assign values to the areas where the fusion parameters of the airport surface and the nearby approach airspace are set, including the types of surveillance sources participating in the fusion in each area and the weight coefficients of each surveillance source participating in the fusion; use the surveillance data quality analysis method to perform statistics and analysis on the content, format, north loss, north time deviation, and position offset of the data items, and automatically assign weights to the participation of each surveillance source in the fusion. The higher the quality of the surveillance source, the greater the weight coefficient, and the lower the quality, the smaller the weight coefficient. If the coefficient is 0, it means not participating in the fusion; manual setting can be done through methods such as data manager human-machine interface setting and drawing of fusion areas on the airport digital map to manually set the fusion parameters.

[0047] 4) Perform fusion optimization processing on multi-source surveillance data based on the 5D data of the airport grid;

[0048] Among them, step 4) specifically includes: Based on the grid 5D data of the areas where the fusion parameters of the airport surface and the nearby approach airspace are set in step 32), assign fusion weights to each surveillance source such as surface movement radar (SMR), multilateration (MLAT), automatic dependent surveillance - broadcast (ADS - B), and air traffic control radar participating in the fusion; create conditions for the improvement of multi-source surveillance fusion technology and the enhancement of the quality of the fused track; adopt a variable window fusion mechanism based on the grid 5D data to achieve refined multi-source surveillance fusion processing, making the fused comprehensive track more stable and smooth, and reducing the occurrence of target splitting and false targets.

[0049] In the example, it specifically further includes:

[0050] 41) In traditional multi-source surveillance data fusion processing, due to the lack and lack of refinement of basic data, a fixed fusion window is adopted, and the fusion window cannot be dynamically adjusted according to the actual operation situation, resulting in jitter, point loss, false targets, and target splitting in the fused comprehensive track; based on the 5D data of regional grids of fusion parameters for the airport surface and the nearby approach airspace, including the longitude X, latitude Y, altitude H, time T, and service attribute F (type of surveillance source, weighting coefficient) of each grid, and dynamically adjusting the size of the fusion window according to the target type, position, and altitude;

[0051] 42) When the position difference, speed difference, and altitude difference reported by each surveillance source are all less than the fusion window, and at the same time, the consistency of fusion factors such as the track number, address code, and secondary code reported by the surveillance source is compared, factor weighted fusion is performed through the fusion coefficients of each surveillance source based on the 5D grid data;

[0052] 43) When calculating the position of the comprehensive track, the correlation between the position reported by the signal source and the airport elements in the 5D data of the airport grid (such as the runway center line, taxiway center line, lawn, etc.) will be judged in real time, and combined with the geographical data of the airport elements and the movement trend of the historical track, on the premise of ensuring the authenticity and mobility of the track, small position errors reported by the signal source will be corrected and jitter suppressed to improve the stability and smoothness of the comprehensive track.

[0053] There are many specific application ways of the present invention. The above description is only the preferred embodiment of the present invention. It should be pointed out that for those of ordinary skill in the art of this technology, without departing from the principle of the present invention, several improvements can still be made, and these improvements should also be regarded as the protection scope of the present invention.

Claims

1. An airport surface surveillance processing method based on a grid 5D data model, characterized in that, The steps are as follows: 1) Use the 3D global latitude and longitude grid division method to perform grid modeling and grid digital encoding on the airport surface and the nearby approach airspace; 2) Perform grid modeling and digital numbering on airport elements, store them hierarchically, construct a database table with the airport element number as the primary key. Each airport element consists of several grids, and a mapping correspondence is established with the grid encoding in step 1); 3) Mark and assign time and business attributes to the airport element database table in units of airport elements, thereby extending the grid attributes of airport elements from 3D to 5D; 4) Perform fusion and optimization processing on multi-source surveillance data based on the 5D data of airport grids; Step 3) specifically includes: 31) Airport element time assignment: Automatically perform dynamic time adjustment and assignment on airport elements according to the received and processed data, or manually set the assignment; Set the start and end times for elements with known effective times; Set the start time for elements with unknown effective times, and when triggered, perform enable / disable changes; 32) Airport element business attribute assignment: According to the needs of airport surface surveillance operations, each airport element has different business attributes, which can be set manually or automatically; Step 32) includes the following steps: Assign weighted coefficients to the fusion parameters of each single surveillance source participating in the multi-source surveillance data fusion calculation of the airport surface; Assign values to the areas for setting fusion parameters in the airport surface and the nearby approach airspace, including the types of surveillance sources participating in the fusion in each area and the weighted coefficients of each surveillance source participating in the fusion; Use the surveillance data quality analysis method to perform statistics and analysis on the content, format, north loss, north time deviation, and position offset of the data items, and automatically assign weights to the weights of each surveillance source participating in the fusion. The higher the quality of the surveillance source, the larger the weighted coefficient; Step 4) specifically includes: Based on the grid 5D data of the areas for setting fusion parameters in the airport surface and the nearby approach airspace in step 32), assign fusion weights to each surveillance source participating in the fusion; Step 4) specifically also includes: 41) Based on the grid 5D data of the areas for setting fusion parameters in the airport surface and the nearby approach airspace, including the longitude X, latitude Y, height H, time T, and business attribute F of each grid, and dynamically adjust the size of the fusion window according to the target type, position, and height; 42) When the position difference, speed difference, and height difference reported by each surveillance source are all less than the fusion window, and at the same time compare the consistency of the fusion factors of the surveillance sources, perform factor weighted fusion through the fusion coefficients of each surveillance source based on the grid 5D data; 43) When calculating the comprehensive track position, the correlation between the position reported by the signal source and the airport elements in the airport grid 5D data will be judged in real time. Combining the geographical data of airport elements and the movement trend of historical tracks, on the premise of ensuring the authenticity and maneuverability of the track, correct small-scale position errors reported by the signal source and suppress jitter.

2. The airport surface surveillance processing method based on the grid 5D data model according to claim 1, characterized in that, The specific content of step 1) includes: dividing the airport surface and the nearby approach airspace into a gapless grid model with different scale levels, constructing a surface and airspace grid model, encoding each level of grid, forming a grid network system framework with a one-to-one mapping between the encoding and the grid, and forming a new description method for the airport surface and the nearby approach airspace; the attributes of each grid unit only include basic 3D data.

3. The airport surface surveillance processing method based on the grid 5D data model according to claim 1, characterized in that, In step 2), satellite image data is used for actual survey and measurement coordinate calibration to digitalize the airport map. The airport elements include: the runway, taxiway, apron, and parameter setting area of the airport. Grid modeling and digital numbering are carried out, and hierarchical storage is performed. A database table with the airport element number as the primary key is constructed. Each airport element consists of several grids, and a mapping correspondence is established with the grid encoding in step 1).

Images