Airplane glide detection method and system based on multi-data source fusion

Abstract

The invention discloses an aircraft glide detection method and system based on multi-data source fusion, and relates to the technical field of aviation flight detection, and the method comprises the following steps: S1, generating an aircraft glide channel according to the precise latitude and longitude coordinates of the entrance and exit of an aircraft runway; according to the aircraft glide detection method and system based on multi-data source fusion, a more accurate aircraft position is generated by fusing ADS-B, millimeter wave radar data and Beidou short message data, then whether the aircraft is accurately located in a glide channel is judged, and an alarm is triggered when the aircraft deviates, so that the monitoring capability and safety in the flight glide stage are improved, and the safety of the aircraft glide detection is improved. According to the aircraft glide detection method and system based on multi-data source fusion, the accuracy and reliability of aircraft position monitoring are remarkably improved through fusion of multiple data sources, accurate deviation warning is achieved based on height range setting of a glide channel, efficient development and integration of the system are achieved through the Java technology, and the adaptability and expansibility of the system are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of aviation flight detection, and in particular to an aircraft descent detection method and system based on multi-data source fusion. Background Art

[0002] In modern aviation operations, the aircraft's descent process is a critical flight phase, directly related to the safety and accuracy of landing. Traditional aircraft descent monitoring mainly relies on a single data source, such as ADS-B (Automatic Dependent Surveillance-Broadcast) data. However, due to the influence of weather, signal interference and equipment failure, there may be insufficient accuracy or incomplete data. The introduction of Beidou short message technology provides more possibilities for improving the reliability and accuracy of aircraft position monitoring.

[0003] Existing technologies typically use ADS-B data alone for glide path detection, lack the ability to fuse multiple data sources, and are unable to fully utilize the advantages of different data sources. In addition, in the detection of altitude deviation based on runway elevation angle, there is still a lack of an accurate alarm trigger mechanism. These problems limit the reliability and applicability of flight glide path monitoring systems. Summary of the Invention

[0004] The present invention aims to provide a method and system for detecting aircraft descents based on the fusion of multiple data sources, thereby resolving the problems in the aforementioned background technology. By fusing ADS-B, millimeter-wave radar data, and Beidou short message data, a more accurate aircraft position is generated, thereby determining whether the aircraft is accurately in the descent path and triggering an alarm in the event of deviation, thereby improving the monitoring capability and safety of the descent phase of flight. By fusing multiple data sources, the accuracy and reliability of aircraft position monitoring are significantly improved. Based on the altitude range setting of the descent path, accurate deviation alarms are achieved. Java technology is used to achieve efficient system development and integration, enhancing the system's adaptability and scalability.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for detecting aircraft descent based on multi-data source fusion, comprising the following steps:

[0006] Step 1: Generate the aircraft glide path based on the precise latitude and longitude coordinates of the runway entrance and exit;

[0007] Step 2: Obtain the aircraft's current location data from ADS-B, millimeter-wave radar, and Beidou short messages, respectively, to obtain three aircraft location data sources;

[0008] Step 3: Fusing the three aircraft position data sources, using preprocessing and Kalman filter algorithm to calculate the real-time position of the aircraft to obtain the real-time position of the aircraft;

[0009] Step 4: Determine whether the real-time position of the aircraft is within the aircraft glide path.

[0010] Furthermore, in step 1, the aircraft glide path is a straight line area with an elevation angle of 3° based on the runway, and the height range of the aircraft glide path is ±50 meters.

[0011] Furthermore, in step 2, the current position data of the aircraft includes longitude information, latitude information, and altitude information.

[0012] Furthermore, in step 4, if the aircraft altitude and position exceed the ±50-meter range of the glide path, a deviation is determined and an alarm is triggered.

[0013] An aircraft descent detection system based on multi-data source fusion, comprising:

[0014] Data receiving module: responsible for receiving the aircraft's current position data;

[0015] Data fusion module: used to fuse information from three aircraft position data sources to generate more accurate real-time aircraft position data;

[0016] Glide path generation module: Based on the latitude and longitude coordinates of the runway entrance and exit, the aircraft glide path is generated by calculating a 3° elevation angle;

[0017] Position determination module: used to compare the aircraft's real-time position data with the aircraft's glide path to determine whether the aircraft has deviated from the path;

[0018] Warning module: used to send out warning signals when the aircraft altitude exceeds the specified range of the glide path;

[0019] Database module: used to store aircraft glide path information and warning records;

[0020] The data fusion module includes a data preprocessing module and a Kalman filter algorithm module, and both the data preprocessing module and the Kalman filter algorithm module are bidirectionally connected by signals.

[0021] Furthermore, the data receiving module includes a Beidou command machine module, an ADS-B receiver module and a millimeter wave radar receiver module, and the Beidou command machine module, the ADS-B receiver module and the millimeter wave radar receiver module are all bidirectionally connected by signals.

[0022] Furthermore, the database module includes a MySQL module, a Doris module and a redis module, and the MySQL module, the Doris module and the redis module are all bidirectionally connected by signals.

[0023] Furthermore, the output signal of the Beidou command module in the data receiving module is connected to the input signal of the data preprocessing module in the data fusion module, and the output signal of the data preprocessing module in the data fusion module is connected to the input signal of the position judgment module.

[0024] Furthermore, the output terminal signal of the position determination module is connected to the input terminal of the alarm module.

[0025] Furthermore, the output terminal signal of the MySQL module in the database module is connected to the input terminal of the downlink channel generation module, and the output terminal signal of the downlink channel generation module is connected to the input terminal of the position determination module.

[0026] The present invention provides an aircraft descent detection method and system based on multi-data source fusion. It has the following beneficial effects:

[0027] (1) This aircraft descent detection method and system based on multi-data source fusion generates a more accurate aircraft position by fusing ADS-B, millimeter-wave radar data, and Beidou short message data, and then determines whether the aircraft is accurately in the descent channel and triggers an alarm when it deviates, thereby improving the monitoring capability and safety of the flight descent phase.

[0028] (2) The aircraft descent detection method and system based on multi-data source fusion significantly improves the accuracy and reliability of aircraft position monitoring by fusing multiple data sources. Based on the altitude range setting of the descent channel, accurate deviation warning is achieved. Java technology is used to achieve efficient development and integration of the system, thereby enhancing the adaptability and scalability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a flow chart of an aircraft descent detection method and system based on multi-data source fusion according to the present invention;

[0030] Figure 2 This is an overall system diagram of an aircraft descent detection method and system based on multi-data source fusion according to the present invention;

[0031] Figure 3 Schematic diagram of a data receiving module of a method and system for detecting aircraft descent based on fusion of multiple data sources according to the present invention;

[0032] Figure 4 Schematic diagram of a data fusion module of an aircraft descent detection method and system based on multi-data source fusion according to the present invention;

[0033] Figure 5 Schematic diagram of a database module of an aircraft descent detection method and system based on multi-data source fusion according to the present invention;

[0034] Figure 6The figure is a schematic diagram of the architecture of an aircraft descent detection method and system based on multi-data source fusion according to the present invention.

[0035] In the figure: 1. Data receiving module; 2. Data fusion module; 3. Glide path generation module; 4. Position determination module; 5. Alarm module; 6. Database module. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0037] Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, but are not to be construed as limiting the present invention.

[0038] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0039] See also Figure 1-6 The present invention provides a technical solution: a method for detecting aircraft descent based on fusion of multiple data sources, comprising the following steps:

[0040] Step 1: Generate the aircraft glide path based on the precise latitude and longitude coordinates of the runway entrance and exit;

[0041] Step 2: Obtain the aircraft's current location data from ADS-B, millimeter-wave radar, and Beidou short messages, respectively, to obtain three aircraft location data sources;

[0042] Step 3: Fuse the three aircraft position data sources, use preprocessing and Kalman filter algorithm to calculate the real-time position of the aircraft, and obtain the real-time position of the aircraft;

[0043] Step 4: Determine whether the aircraft's real-time position is within the aircraft's glide path.

[0044] Specifically, in step 1, the aircraft glide path is a straight line area with an elevation angle of 3° based on the runway, and the height range of the aircraft glide path is ±50 meters.

[0045] Specifically, in step 2, the current position data of the aircraft includes longitude information, latitude information, and altitude information.

[0046] Specifically, in step 4, if the aircraft altitude and position exceed the ±50-meter range of the glide path, it is determined to be a deviation and an alarm is triggered.

[0047] An aircraft descent detection system based on multi-data source fusion, comprising:

[0048] Data receiving module 1: responsible for receiving the aircraft's current position data;

[0049] Data fusion module 2: used to fuse information from three aircraft position data sources to generate more accurate real-time aircraft position data;

[0050] Glide path generation module 3: Based on the latitude and longitude coordinates of the runway entrance and exit, the aircraft glide path is generated by calculating a 3° elevation angle;

[0051] Position determination module 4: used to compare the aircraft's real-time position data with the aircraft's glide path to determine whether the aircraft has deviated from the path;

[0052] Alarm module 5: used to send out an alarm signal when the aircraft altitude exceeds the specified range of the glide path;

[0053] Database module 6: used to store aircraft glide path information and warning records;

[0054] The data fusion module 2 includes a data preprocessing module and a Kalman filter algorithm module, and there is a bidirectional signal connection between the data preprocessing module and the Kalman filter algorithm module.

[0055] Specifically, the data receiving module 1 includes a Beidou command machine module, an ADS-B receiver module and a millimeter wave radar receiver module, and the Beidou command machine module, the ADS-B receiver module and the millimeter wave radar receiver module are all bidirectionally connected.

[0056] Specifically, the database module 6 includes a MySQL module, a Doris module and a redis module, and the MySQL module, the Doris module and the redis module are all bidirectionally connected by signals.

[0057] Specifically, the output signal of the Beidou command module in the data receiving module 1 is connected to the input of the data preprocessing module in the data fusion module 2, and the output signal of the data preprocessing module in the data fusion module 2 is connected to the input of the position judgment module 4.

[0058] Specifically, the output terminal signal of the position determination module 4 is connected to the input terminal of the alarm module 5 .

[0059] Specifically, the output terminal signal of the MySQL module in the database module 6 is connected to the input terminal of the downlink channel generation module 3 , and the output terminal signal of the downlink channel generation module 3 is connected to the input terminal of the position determination module 4 .

[0060] An aircraft descent detection method and system based on multi-data source fusion:

[0061] 1. Formation of a descending channel

[0062] Upon system startup, the system first reads the configured runway entrance and exit coordinates from the database. For example, the entrance coordinates are [109.710, 31.060] and the exit coordinates are [109.715, 31.065]. Based on these coordinates, the system uses geometric calculations to generate a straight line through the runway. This line is then extended 20 kilometers to either end of the runway. Another line is then drawn from either the entrance or exit coordinates at an elevation angle of 3°. This line forms the projected glide path. Furthermore, this path is extended by 50 meters in elevation above and below, and its horizontal width is the runway width, thus constructing a three-dimensional glide path.

[0063] In the Java implementation, the Coordinate utility class was used to encapsulate point coordinates, and an open-source geometry library (such as JTS) was used to calculate the channel region boundaries. The generated glide path was stored in memory as a data structure for subsequent real-time analysis.

[0064] 2. Data Fusion

[0065] Data acquisition: The data receiving module extracts aircraft position information (including longitude, latitude and altitude) from the ADS-B receiving server and the millimeter wave radar receiving server through the TCP protocol, and obtains information from another data source of the aircraft through the Beidou command aircraft.

[0066] The data fusion process includes the following steps:

[0067] 1. Data Preprocessing

[0068] Since the data refresh frequencies and timestamps of different data sources may differ, an interpolation algorithm is first used to perform time alignment so that the data points of each data source are unified to the same timestamp.

[0069] Set a unified timestamp t. For a data source's location information (Lat, Lon, Alt), if there is a time gap between the latest data point t1 and the previous data point t0, use linear interpolation. This method ensures that timestamps from all data sources are aligned, improving the accuracy of data fusion. Use a sliding time window (e.g., a 1-second window) to filter and align data, ensuring even data distribution and avoiding information loss or data mutation.

[0070] 2. Data Fusion

[0071] After completing the time alignment, the Kalman filter algorithm is used to fuse the ADS-B, millimeter-wave radar and Beidou data to improve the accuracy and stability of the aircraft position information.

[0072] 3. System Implementation

[0073] When implementing the Java code, ConcurrentHashMap is used in the DataFusionService class to store the latest data from each data source to ensure thread safety.

[0074] ScheduledExecutorService is used for scheduled task scheduling, data interpolation and time alignment, and Apache Commons Math or JKalman is used for Kalman filter calculations.

[0075] Compared with the traditional weighted average algorithm, the improved data fusion method has the advantages of stronger noise resistance, higher fusion accuracy and support for future state prediction. It can make predictions based on current trends, achieve early warning and improve safety.

[0076] 3. Position determination and alarm

[0077] The fused aircraft position is used by the Position Determination Module to monitor the aircraft's status within the glide path in real time. The data fusion module obtains the aircraft's latest position, projects its longitude and latitude onto the glide path, and calculates the elevation difference. If the calculated altitude deviates from the glide path (by more than ±50 meters), an alarm is triggered, and the abnormality information is recorded and transmitted to the monitoring center via the alarm module.

[0078] For example, when the aircraft's altitude drops below 60 meters below the elevation line, the system calls the alarm interface, triggering a real-time notification. The alarm logic polls the fused position via Java's ScheduledExecutorService, ensuring high real-time performance.

[0079] 4. System Architecture Implementation

[0080] Backend implementation:

[0081] The system uses the Spring Boot framework and integrates multiple modules, including location fusion, region determination, and alarm processing. MySQL is used to store runway information and alarm records. Real-time data stream processing is performed by Spring WebFlux to improve the efficiency of message push.

[0082] When an alarm is triggered, a message is pushed to the monitoring terminal using WebSocket. The message content includes the aircraft's current position, deviation altitude, and the identifier of the specific runway glide path, allowing operators to respond quickly.

[0083] 5. System operation process

[0084] (1) When the system is initialized, the glide path of all runways is loaded and generated.

[0085] (2) The data receiving module continuously monitors ADS-B, millimeter wave radar and Beidou data sources.

[0086] (3). The data fusion module periodically fuses the current aircraft position information.

[0087] (4) The position determination module determines whether the aircraft has deviated from the glide path and calls the alarm module.

[0088] (5) The monitoring terminal receives the alarm information and displays the deviation status in real time. The operator takes action according to the prompt.

[0089] Although the present invention is described herein with reference to the illustrative embodiments thereof, the above embodiments are merely preferred embodiments of the present invention, and the embodiments of the present invention are not limited thereto. It should be understood that those skilled in the art can devise many other modifications and embodiments, which will fall within the scope of the principles and spirit disclosed in this application.

[0090] The above are only preferred embodiments of the present invention. It should be pointed out that those skilled in the art can make several modifications and improvements without departing from the inventive concept of the present invention, and these all fall within the scope of protection of the present invention.

Claims

1. A method for detecting aircraft descent based on fusion of multiple data sources, characterized in that: The following steps are involved: S1: Generate the aircraft glide path based on the precise latitude and longitude coordinates of the runway entrance and exit; S2: Obtain the aircraft's current position data from ADS-B, millimeter-wave radar, and Beidou short messages, obtaining three aircraft position data sources; S3: fusing the three aircraft position data sources, using preprocessing and Kalman filter algorithm to calculate the real-time position of the aircraft, and obtaining the real-time position of the aircraft; S4: Determine whether the real-time position of the aircraft is within the aircraft glide path.

2. The method for detecting aircraft descent based on multi-data source fusion according to claim 1, characterized in that: In step S1 , the aircraft glide path is a straight line area with an elevation angle of 3° based on the runway, and the height range of the aircraft glide path is ±50 meters.

3. The method for detecting aircraft descent based on multi-data source fusion according to claim 1, characterized in that: In step S2, the current position data of the aircraft includes longitude information, latitude information, and altitude information.

4. The method for detecting aircraft descent based on multi-data source fusion according to claim 1, characterized in that: In step S4, if the aircraft altitude and position exceed the ±50-meter range of the glide path, deviation is determined and an alarm is triggered.

5. An aircraft descent detection system based on multi-data source fusion, characterized in that: include: Data receiving module (1): responsible for receiving the aircraft's current position data; Data fusion module (2): used to fuse the information of three aircraft position data sources to generate more accurate real-time aircraft position data; Glide path generation module (3): Based on the latitude and longitude coordinates of the runway entrance and exit, the aircraft glide path is generated by calculating a 3° elevation angle; Position determination module (4): used to compare the real-time position data of the aircraft with the aircraft's glide path to determine whether the aircraft has deviated from the path; Alarm module (5): used to send out an alarm signal when the aircraft altitude exceeds the prescribed range of the glide path; Database module (6): used to store aircraft glide path information and warning records; The data fusion module (2) comprises a data preprocessing module and a Kalman filter algorithm module, and both the data preprocessing module and the Kalman filter algorithm module are bidirectionally connected by signals.

6. The aircraft descent detection system based on multi-data source fusion according to claim 1, characterized in that: The data receiving module (1) comprises a Beidou command machine module, an ADS-B receiver module and a millimeter wave radar receiver module, and the Beidou command machine module, the ADS-B receiver module and the millimeter wave radar receiver module are all bidirectionally connected.

7. The aircraft descent detection system based on multi-data source fusion according to claim 1, characterized in that: The database module (6) includes a MySQL module, a Doris module and a redis module, and the MySQL module, the Doris module and the redis module are all bidirectionally connected.

8. The aircraft descent detection system based on multi-data source fusion according to claim 1, characterized in that: The output signal of the Beidou command module in the data receiving module (1) is connected to the input of the data preprocessing module in the data fusion module (2), and the output signal of the data preprocessing module in the data fusion module (2) is connected to the input of the position determination module (4).

9. The aircraft descent detection system based on multi-data source fusion according to claim 1, characterized in that: The output terminal signal of the position determination module (4) is connected to the input terminal of the alarm module (5).

10. The aircraft descent detection system based on multi-data source fusion according to claim 1, characterized in that: The output signal of the MySQL module in the database module (6) is connected to the input signal of the down-slope channel generation module (3), and the output signal of the down-slope channel generation module (3) is connected to the input signal of the position determination module (4).

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