Track fusion method and device based on ADS-B data

Abstract

The track fusion method and device based on ADS-B data can provide more accurate real-time track data for air traffic control field aircraft, greatly improving the safety and accuracy of airspace and airport. It includes: (1) reading the track information of the previous several points of the corresponding flight from the memory; (2) ADS-B data preprocessing, deleting abnormal track data in ADS-B; (3) radar data preprocessing, deleting abnormal track data in radar; (4) judging the same source track data, if the same source track data, executing step (5), otherwise executing step (6); (5) saving and outputting the current track data as the track fusion result; (6) processing different source data; (7) saving the fused track data to the memory.

Description

Technical Field

[0001] This invention relates to the technical field of civil aviation and general aviation flight, and in particular to a flight path fusion method based on ADS-B data, and also to a flight path fusion device based on ADS-B data. Background Technology

[0002] Aircraft ADS-B data is widely used in civil airspace management due to its fast update rate, high positioning accuracy, and low operating cost. Radar track data, currently the main module for track data processing, cannot cover vast ocean areas, deserts, and remote mountainous regions, and its track positioning accuracy and real-time performance are inferior to ADS-B data. With increasingly busy air traffic and passenger throughput, the real-time performance and accuracy of aircraft track data are becoming increasingly important. Furthermore, current track data, primarily based on radar data, no longer meets the requirements of the current air traffic control field. Summary of the Invention

[0003] To overcome the shortcomings of existing technologies, the technical problem to be solved by this invention is to provide a track fusion method based on ADS-B data, which can provide more accurate real-time track data for aircraft in the air traffic control field, and greatly improve the safety and accuracy of airspace and airports.

[0004] The technical solution of this invention is: a track fusion method based on ADS-B data, which includes the following steps: (1) Read the preceding flight path information of the corresponding flight from memory; (2) ADS-B data preprocessing, deleting abnormal track data in ADS-B; (3) Radar data preprocessing, deleting abnormal flight track data in the radar; (4) Determine if the data are from the same source. If they are from the same source, proceed to step (5); otherwise, proceed to step (6). (5) The current track data is saved and output as the track fusion result; (6) If the data is from a different source than the previous point, then linear fusion / filtered fusion is performed based on the number of data points from the same source in the memory database. If the number of points from the same source is less than 2, then the current point is saved as the fusion result and output. If there are 2 points from the same source, then linear prediction is performed using the first 2 points, and the prediction result is then fused with the current point using weights. The fusion result is saved and output. If there are 3 points from the same source, then filtered fusion prediction is performed using the first 3 points, and the prediction result is then fused with the current point using weights. The fusion result and the trajectory model status are saved to the memory database. (7) Save the fused track data to memory.

[0005] This invention proposes an adaptive model fusion algorithm that uses ADS-B track data as the primary source and radar data as a secondary source. The algorithm includes filtering effective points from existing track data, storing track status and fusion results, selecting different fusion strategies based on track data types, and obtaining the final accurate track fusion result. This provides more accurate real-time track data for aircraft in the air traffic control field, greatly improving the safety and accuracy of airspace and airports.

[0006] A track fusion device based on ADS-B data is also provided, which includes: The read module is configured to read the preceding flight path information of the corresponding flight from memory; The ADS-B data preprocessing module is configured to perform ADS-B data preprocessing and remove abnormal track data in ADS-B. The radar data preprocessing module is configured to perform radar data preprocessing and remove abnormal track data from the radar. The judgment module is configured to perform the judgment of similar track data. The same-source processing module is configured to save and output the current track data as a track fusion result. The non-same-source processing module is configured to perform linear fusion / filtered fusion based on the number of same-source data points in the memory database when the data is from a different source than the previous point. If there are fewer than 2 same-source points, the current point is saved and output as the fusion result. If there are 2 same-source points, linear prediction is performed using the first 2 points, and the prediction result is then weighted and fused with the current point. The fusion result is saved and output. If there are 3 same-source points, filtered fusion prediction is performed using the first 3 points, and the prediction result is then weighted and fused with the current point. The fusion result and the trajectory model state are saved to the memory database. The save module is configured to save the fused track data to memory. Attached Figure Description

[0007] Figure 1 The diagram shown is a flowchart of the ADS-B data track fusion process.

[0008] Figure 2 The diagram shown is a schematic of the trajectory data structure.

[0009] Figure 3 The diagram shown is a schematic of the track status data structure.

[0010] Figure 4 The diagram shows the ADS-B track data preprocessing flowchart.

[0011] Figure 5 The diagram shown is a flowchart of radar track data preprocessing.

[0012] Figure 6 The diagram shown is a flowchart for determining data from the same source.

[0013] Figure 7 The diagram shows the process of fusing track data from the same source.

[0014] Figure 8 The diagram shows the process of fusing non-homogeneous track data. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0016] To make the description of this disclosure more detailed and complete, illustrative descriptions of embodiments and specific examples of the present invention are provided below; however, these are not the only forms of implementing or utilizing the specific examples of the present invention. The embodiments cover features of multiple specific examples and methods and steps for constructing and operating these specific examples, and their order. However, other specific examples may also be used to achieve the same or equivalent functions and order of steps.

[0017] like Figure 1 As shown, this track fusion method based on ADS-B data includes the following steps: (1) Read the preceding flight path information of the corresponding flight from memory; (2) ADS-B data preprocessing, deleting abnormal track data in ADS-B; (3) Radar data preprocessing, deleting abnormal flight track data in the radar; (4) Determine if the data are from the same source. If they are from the same source, proceed to step (5); otherwise, proceed to step (6). (5) The current track data is saved and output as the track fusion result; (6) If the data is from a different source than the previous point, then linear fusion / filtered fusion is performed based on the number of data points from the same source in the memory database. If the number of points from the same source is less than 2, then the current point is saved as the fusion result and output. If there are 2 points from the same source, then linear prediction is performed using the first 2 points, and the prediction result is then fused with the current point using weights. The fusion result is saved and output. If there are 3 points from the same source, then filtered fusion prediction is performed using the first 3 points, and the prediction result is then fused with the current point using weights. The fusion result and the trajectory model status are saved to the memory database. (7) Save the fused track data to memory.

[0018] This invention proposes an adaptive model fusion algorithm that uses ADS-B track data as the primary source and radar data as a secondary source. The algorithm includes filtering effective points from existing track data, storing track status and fusion results, selecting different fusion strategies based on track data types, and obtaining the final accurate track fusion result. This provides more accurate real-time track data for aircraft in the air traffic control field, greatly improving the safety and accuracy of airspace and airports.

[0019] This invention fuses ADS-B and radar track data from different data sources to create a track data fusion system that uses ADS-B track data as the primary source and radar data as a secondary source. This system has a fast track data processing speed, a high update frequency, and higher track data accuracy, making it suitable for various application scenarios in aviation management.

[0020] Figure 2 and Figure 3 The diagram shows the structure of historical track data and track status data stored in memory in this invention. Data reading is as shown in step (1) of the track fusion flowchart, and data storage is as shown in step (7) of the track fusion flowchart.

[0021] Historical flight track data information such as Figure 2 As shown, each aircraft contains multiple data items, and the specific information for each data item is as follows:

[0022] Current track data: Collected current track data information, including data source and latitude and longitude location information.

[0023] Last Update Time: The timestamp of the last time this aircraft updated its location.

[0024] Previous result data: The aircraft's last updated track data, including data source and latitude / longitude location information.

[0025] Radar Data Stack: The temporary storage location for radar track data received by the aircraft. This data contains information on all radar data items. Track point data stored in this stack is last-in, first-out.

[0026] ADS-B Data Stack: The temporary storage location for ADS-B track data received by the aircraft. This data includes information on all ADS-B data items. The track point data stored in this stack is first-in, last-out.

[0027] Preferably, such as Figure 3 As shown, in step (1), the track information includes the track data information of the first 3 points of the aircraft, the IMM filtering state matrix, the IMM error matrix and the IMM mixing probability matrix during the flight of the aircraft track. In order to save space, the structure data are all stored in JSON string format.

[0028] Preferably, such as Figure 4 As shown, step (2) includes the following sub-steps: (2.1) Determine the call sign field in the ADS-B data. If it is empty, the current waypoint is an abnormal point and should be deleted. Otherwise, proceed to step (2.2). (2.2) Determine the time item in the ADS-B data. If it is empty, the current waypoint is an anomaly and should be deleted. Otherwise, proceed to step (2.3). (2.3) Read the previous output track data from memory. The data is the previous point track data stored in memory; (2.4) Determine the timestamp of the current track ADS-B data. If it is less than or equal to the timestamp of the previous output track data, the current track point is expired data and is considered an abnormal point and deleted. Otherwise, proceed to step (2.5). (2.5) Determine the flight speed value of the current track ADS-B data. If it is 0 or empty, the current track data is considered to be an abnormal point and is deleted. Otherwise, proceed to step (4).

[0029] Preferably, such as Figure 5 As shown, step (3) includes the following sub-steps: (3.1) Determine the time item in the radar data. If it is empty, the current track point is an anomaly and should be deleted. Otherwise, proceed to step (3.2). (3.2) Determine the flight altitude item in the radar data. If it is 0, the current track point is an anomaly and should be deleted. Otherwise, proceed to step (3.3). (3.3) Determine the flight speed in the radar data. If it is less than the flight threshold of 100km / h, the current track point is an anomaly and should be deleted. Otherwise, proceed to step (3.4). (3.4) Read the previous output track data from memory. The data is the previous point track data stored in memory; (3.5) Determine the timestamp of the current track radar data. If it is less than or equal to the timestamp of the previous output track data, the current track point is expired data and is considered an abnormal point and deleted. Otherwise, proceed to step (4).

[0030] Preferably, such as Figure 6 As shown, step (4) includes the following sub-steps: (4.1) Read the latest ADS-B track data of the aircraft currently in process from the memory data; (4.2) Determine whether the current aircraft has ADS-B track data; if the data read is empty, the aircraft has no latest ADS-B track data, then proceed to step (4.4), otherwise proceed to step (4.3). (4.3) The aircraft currently has the latest ADS-B track data, which identifies the current data source as an ADS-B data source; (4.4) Read the track data in the memory data radar stack. The data is stored in the radar data stack in memory and the current data source is identified as the radar data source.

[0031] Preferably, such as Figure 7 As shown, step (5) includes the following sub-steps: (5.1) Read the continuous track data of the current aircraft track status data from the memory data, which is a string track point in JSON format, with a maximum of 3 track points; (5.2) Determine the number of consecutive track data points. If it is less than 3 track points, proceed to step (5.3); otherwise, proceed to step (5.4). (5.3) Delete the first point in the three points of the continuous track data; (5.4) Save the current track data as the last point of the continuous track data; (5.5) The current track data is output as the result of data fusion from the same source.

[0032] Preferably, such as Figure 8 As shown, step (6) includes the following sub-steps: (6.1) Read the continuous track data of the current aircraft track status data from the memory data, which is a string track point in JSON format, with a maximum of 3 track points; (6.2) Determine the number of consecutive track data points. If it is less than or equal to 1 track point, proceed to step (6.3); otherwise, proceed to step (6.4). (6.3) Save the current track data as the last point of the continuous track data; (6.4) Determine the number of consecutive track data points. If it is equal to 3 track points, proceed to step (6.6); otherwise, proceed to step (6.5). (6.5) If the number of consecutive track points is less than 3, perform linear prediction and use the track data of the first 2 points to perform linear prediction of the track point at the current time. (6.6) When the number of consecutive track points is equal to 3, perform IMM filtering prediction. Use the track data of the first 3 points to perform track point filtering prediction at the current time point. The IMM state matrix, IMM error matrix and IMM mixed probability matrix generated during the filtering process are saved to the track state data in JSON format. (6.7) The linear prediction / filter prediction results are fused with the current track data with weights, where the weight of ADS-B data is 0.6 and the weight of radar data is 0.4. The fusion result is output as the final result of the aircraft track.

[0033] This step completes the data fusion of different source track data. With ADS-B data as the main source, it ensures the stability of track output during data switching, preventing sudden deviations from the previous trajectory, and making it more consistent with the actual flight track of the aircraft, thereby improving the accuracy and stability of aircraft track monitoring.

[0034] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium. When executed, the program includes the steps of the methods of the above embodiments. The storage medium can be ROM / RAM, magnetic disk, optical disk, memory card, etc. Therefore, corresponding to the method of the present invention, the present invention also includes a track fusion device based on ADS-B data. This device is typically represented in the form of functional modules corresponding to the steps of the method. The device includes: The read module is configured to read the preceding flight path information of the corresponding flight from memory; The ADS-B data preprocessing module is configured to perform ADS-B data preprocessing and remove abnormal track data in ADS-B. The radar data preprocessing module is configured to perform radar data preprocessing and remove abnormal track data from the radar. The judgment module is configured to perform the judgment of similar track data. The same-source processing module is configured to save and output the current track data as a track fusion result. The non-same-source processing module is configured to perform linear fusion / filtered fusion based on the number of same-source data points in the memory database when the data is from a different source than the previous point. If there are fewer than 2 same-source points, the current point is saved and output as the fusion result. If there are 2 same-source points, linear prediction is performed using the first 2 points, and the prediction result is then weighted and fused with the current point. The fusion result is saved and output. If there are 3 same-source points, filtered fusion prediction is performed using the first 3 points, and the prediction result is then weighted and fused with the current point. The fusion result and the trajectory model state are saved to the memory database. The save module is configured to save the fused track data to memory.

[0035] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A track fusion method based on ADS-B data, characterized in that: It includes the following steps: (1) Read the preceding flight path information of the corresponding flight from memory; (2) ADS-B data preprocessing, deleting abnormal track data in ADS-B; (3) Radar data preprocessing, deleting abnormal flight track data in the radar; (4) Determine if the data are from the same source. If they are from the same source, proceed to step (5); otherwise, proceed to step (6). (5) The current track data point is saved as the track fusion result to the same source track data in the memory database. If the number of track data points in memory is less than 3, the current track data point is directly saved as the latest track data. If the number of track data points is equal to 3, the earlier track points are removed, and the current track data point, i.e. the track fusion point, is saved as the latest track data point. (6) If the data is from a different source than the previous point, then linear fusion / filtered fusion is performed based on the number of track data points from the same source in the memory database. If the number of track data points is less than 2, then the current point is saved as the fusion result and output. If there are 2 track data points, then the first 2 points are used for linear prediction, and the prediction result is then fused with the current point using weights. The fusion result is saved and output. If there are 3 track data points, then the first 3 points are used for filtered fusion prediction, and the prediction result is then fused with the current point using weights. The fusion result and the track model state are saved to the memory database and output. (7) Save track status data to memory; Step (4) includes the following sub-steps: (4.1) Read the latest ADS-B track data of the aircraft currently in process from the memory data; (4.2) Determine whether the current aircraft has ADS-B track data; if the data read is empty, the aircraft has no latest ADS-B track data, then proceed to step (4.4), otherwise proceed to step (4.3). (4.3) The aircraft currently has the latest ADS-B track data, which identifies the current data source as an ADS-B data source; (4.4) Read the track data in the memory data radar stack. The data is stored in the radar data stack in memory and the current data source is identified as the radar data source.

2. The track fusion method based on ADS-B data according to claim 1, characterized in that: In step (1), the track information includes the track data information of the first 3 points of the aircraft, the IMM filter state matrix, the IMM error matrix and the IMM mixed probability matrix during the flight of the aircraft track. In order to save space, the structure data are all stored in JSON string format.

3. The track fusion method based on ADS-B data according to claim 1, characterized in that: Step (2) includes the following sub-steps: (2.1) Determine the call sign field in the ADS-B data. If it is empty, the current waypoint is an abnormal point and should be deleted. Otherwise, proceed to step (2.2). (2.2) Determine the time item in the ADS-B data. If it is empty, the current track point is an abnormal point and should be deleted. Otherwise, proceed to step (2.3). (2.3) Read the previous output track data from memory. The data is the previous point track data stored in memory; (2.4) Determine the timestamp of the current track ADS-B data. If it is less than or equal to the timestamp of the previous output track data, the current track point is expired data and is considered an abnormal point and deleted. Otherwise, proceed to step (2.5). (2.5) Determine the flight speed value of the current track ADS-B data. If it is 0 or empty, the current track data is considered to be an abnormal point and is deleted. Otherwise, proceed to step (4).

4. The track fusion method based on ADS-B data according to claim 1, characterized in that: Step (3) includes the following sub-steps: (3.1) Determine the time item in the radar data. If it is empty, the current track point is an anomaly and should be deleted. Otherwise, proceed to step (3.2). (3.2) Determine the flight altitude item in the radar data. If it is 0, the current track point is an anomaly and should be deleted. Otherwise, proceed to step (3.3). (3.3) Determine the flight speed in the radar data. If it is less than the flight threshold of 100km / h, the current track point is an anomaly and should be deleted. Otherwise, proceed to step (3.4). (3.4) Read the previous output track data from memory. The data is the previous point track data stored in memory; (3.5) Determine the timestamp of the current track radar data. If it is less than or equal to the timestamp of the previous output track data, the current track point is expired data and is considered an abnormal point and deleted. Otherwise, proceed to step (4).

5. The track fusion method based on ADS-B data according to claim 1, characterized in that: Step (5) includes the following sub-steps: (5.1) Read the continuous track data from the same source data source as the current aircraft track status data to be processed from the memory data. The track data is a string of track points in JSON format, with a maximum of 3 track points. (5.2) Determine the number of consecutive track data points. If it is greater than 3 track points, proceed to step (5.3); otherwise, proceed to step (5.4). (5.3) Delete the first point in the three points of the continuous track data; (5.4) Save the current track data as the last point of the continuous track data; (5.5) The current track data is output as the result of data fusion from the same source.

6. The track fusion method based on ADS-B data according to claim 1, characterized in that: Step (6) includes the following sub-steps: (6.1) Read the continuous track data of the same source data of the current aircraft track status from the memory data. The track points are in JSON format, with a maximum of 3 track points. (6.2) Determine the number of consecutive track data points. If it is less than or equal to 1 track point, proceed to step (6.3); otherwise, proceed to step (6.4). (6.3) Save the current track data as the last point of the continuous track data; (6.4) Determine the number of consecutive track data points. If it is equal to 3 track points, proceed to step (6.6); otherwise, proceed to step (6.5). (6.5) If the number of consecutive track points is less than 3, perform linear prediction and use the track data of the first 2 points to perform linear prediction of the track point at the current time. (6.6) When the number of consecutive track points is equal to 3, perform IMM filtering prediction. Use the track data of the first 3 points to perform track point filtering prediction at the current time point. The IMM state matrix, IMM error matrix and IMM mixed probability matrix generated during the filtering process are saved to the track state data in JSON format. (6.7) The linear prediction / filter prediction results are fused with the current track data with weights, where the weight of ADS-B data is 0.6 and the weight of radar data is 0.

4. The fusion result is output as the final result of the aircraft track. The fusion result is saved to the last point of the same source track data in memory. If the number of track data points is equal to 3, the earlier track points are removed and the current fusion result is saved as the latest track data point.

7. A trajectory fusion device based on ADS-B data, characterized in that: It is used to perform the method according to claim 1, comprising: The read module is configured to read the preceding flight path information of the corresponding flight from memory; The ADS-B data preprocessing module is configured to perform ADS-B data preprocessing and remove abnormal track data in ADS-B. The radar data preprocessing module is configured to perform radar data preprocessing and remove abnormal track data from the radar. The judgment module is configured to perform the judgment of similar track data. The same-source processing module is configured to save and output the current track data as a track fusion result. The non-same-source processing module is configured to perform linear fusion / filtered fusion based on the number of same-source data points in the memory database when the data is from a different source than the previous point. If there are fewer than 2 same-source points, the current point is saved and output as the fusion result. If there are 2 same-source points, linear prediction is performed using the first 2 points, and the prediction result is then weighted and fused with the current point. The fusion result is saved and output. If there are 3 same-source points, filtered fusion prediction is performed using the first 3 points, and the prediction result is then weighted and fused with the current point. The fusion result and the trajectory model state are saved to the memory database. The save module is configured to save the fused track data to memory.

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