Collaborative data fusion method of ADS-B, 5G, ad hoc network and Remote ID based on Kalman filter

Abstract

The present invention discloses a collaborative data fusion method of ADS‑B, 5G, ad hoc network and Remote ID based on Kalman filtering. From the field perspective, the ADS‑B messages, 5G messages, ad hoc network messages and Remote ID messages received in real time are fused through an improved Kalman filtering method, and the fused data is corrected in combination with the real-time communication quality. The obtained S-class messages are pushed to the track display terminal in the local area network in real time through the MQTT protocol to monitor the real-time situation of the UAV. The present invention can efficiently establish real-time communication between the UAV and the ground terminal, and perform effective data fusion to assist the ground terminal in obtaining the flight situation information of the UAV, thereby realizing the monitoring and management of the UAV. In this way, the flight safety of the airspace is guaranteed and the airspace traffic management is strengthened.

Description

Technical Field

[0001] The present invention relates to the technical field of drone safety supervision, and in particular to a collaborative data fusion method of ADS-B, 5G, ad hoc network and Remote ID based on Kalman filtering. Background Art

[0002] With the rapid development of aviation technology, drones have been widely promoted. Due to the characteristics of drones such as reliability, flexibility, strong adaptability and high efficiency, they are widely used in agriculture, aerial photography, logistics, monitoring, rescue, forestry, environmental protection and other fields. However, with the increase in the number of drones, flight safety has become an issue that cannot be ignored. Therefore, it is necessary to strengthen flight traffic control and low-altitude safety supervision to ensure that the flight of drones does not affect public safety and personal privacy. Deploying ADS-B equipment, 5G equipment, Remote ID equipment, and self-organizing network equipment for drones can effectively solve the above problems.

[0003] ADS-B is an avionics device used for automatic wireless communication between aircraft and between aircraft and ground control centers. The technology provides real-time flight position data through satellite navigation systems and broadcasts this data for monitoring by other aircraft and ground control centers. ADS-B can improve flight safety, reduce the risk of mid-air collisions, improve flight efficiency, and provide pilots with more comprehensive flight information. This technology has become an important monitoring and communication tool in the field of modern civil aviation. However, ADS-B operates in a specific frequency band, and the single use of ADS-B has the problem of spectrum blocking.

[0004] Remote ID refers to a technology in drone systems that aims to identify and monitor drones by transmitting the drone's identity and flight information to ground stations or other equipment using a communication link. The purpose of Remote ID is to ensure that drone activities in the air can be identified and tracked by ground operators, authorities, and other aircraft. This technology can provide critical flight information such as the drone's location, speed, altitude, and related identity information. The remote identification system can send this data to ground control stations, aviation management agencies, and other relevant parties through wireless communication networks. However, Remote ID has data security and cost issues. The sole use of Remote ID may cause information leakage and excessive costs.

[0005] An ad hoc network is a temporary network established by a group of mobile devices through wireless communication, without the support of any infrastructure or central node. Ad hoc networks are usually managed and controlled in a distributed manner, and nodes collaborate to complete the network's establishment, maintenance, and routing. Nodes can join or leave the network at any time and any place, and the network topology will change dynamically. This adaptability makes ad hoc networks widely used in scenarios that require high temporariness, dynamism, and flexibility. After the drone is equipped with an ad hoc network module, a real-time local area network can be temporarily built, and all terminals can communicate in the network.

[0006] Through the 5G network, drones can transmit flight data in real time, including key information such as speed, longitude, latitude, altitude, direction and identity code. These data can be received and processed by the ground control center and other aircraft, so as to achieve accurate monitoring and management of the flight status of drones. Its high bandwidth and low latency characteristics are essential for flight safety, air traffic management, coordination between aircraft, monitoring and rescue applications. Therefore, the application of 5G in the field of drone communications provides strong support for the intelligent flight, cluster management and complex task execution of drones, and promotes the widespread application and development of drone technology in various fields. Transmitting the flight status of drones through the 5G network can effectively overcome the problems of spectrum blocking, data security and excessive cost. However, the single use of 5G network as a drone monitoring network is difficult to integrate with the existing civil aviation monitoring system, which brings certain technical obstacles to air traffic controllers.

[0007] Data fusion refers to the integration of data from multiple interrelated information sources into a more comprehensive, accurate, reliable and timely information source through appropriate processing methods. For example, the invention with publication number CN115440095B discloses a ground terminal for an aviation multi-mode fusion communication system, and the invention with publication number CN115440096A discloses an aviation multi-mode fusion communication system, etc. However, the existing data is either presented directly or with data of a certain communication method specified as a standard, or is presented after calculating the reliability with extremely complex algorithms (such as neural networks, etc.). The former has poor accuracy, and the latter is difficult to achieve real-time requirements and requires high computing power. The data generated by the current distributed multi-type devices with a wide range of sources and different formats will greatly limit the sharing and effective use of information. Summary of the invention

[0008] Aiming at the technical problem that the data from various sources and formats generated by distributed multiple types of equipment will greatly limit the sharing and effective use of information, the purpose of this invention is to provide a collaborative data fusion method of ADS-B, 5G, ad hoc network and Remote ID based on Kalman filtering, which can efficiently establish real-time communication between drones and ground terminals, and perform effective data fusion to assist the ground terminal in obtaining the flight situation information of drones, and then realize the monitoring and management of drones. In this way, the flight safety of airspace can be guaranteed and the airspace traffic management can be strengthened.

[0009] In order to achieve the above technical objectives, the technical solution adopted by the present invention is:

[0010] A collaborative data fusion method for ADS-B, 5G, ad hoc network and Remote ID based on Kalman filtering, the collaborative data fusion method comprising the following steps:

[0011] Connect the positioning receiving antenna to the positioning module. The positioning module transmits the UAV positioning data sent by the positioning receiving antenna to ADS-B Out as the first data. After ADS-B Out encapsulates and decodes the first data, it is broadcasted to the receiving antenna by the ADS-B antenna. The receiving antenna is connected to the RTL-SDR, and the ADS-B message corresponding to the first data is transmitted to the PC for decoding and saved locally.

[0012] The first Raspberry Pi is used to read the flight control information of the drone, and the information is transmitted to the 5G module as the second data. The 5G module encapsulates and decodes the information, and then transmits the information to the receiving antenna through the 5G network. The receiving antenna is connected to the 5G receiving module, and the 5G message corresponding to the second data is transmitted to the PC for decoding and saved locally.

[0013] The second Raspberry Pi is used to read the UAV flight control information, which is used as the third data. After processing, it is transmitted to the self-organizing network module through the network port, and then transmitted to the self-organizing network receiving end of the ground station through the self-organizing network. The self-organizing network receiving end of the ground station transmits the self-organizing network message corresponding to the third data to the PC for data processing;

[0014] Build the drone Remote ID transmitter: Mount the DroneBeacon on the drone, which has a built-in positioning data source. The acquired drone positioning information is used as the fourth data and transmitted to the DroneScout module of the Remote ID receiving end of the ground station via Bluetooth or WiFi. The DroneScout module receives the Remote ID message corresponding to the fourth data and transmits it to the PC through the switch for local storage.

[0015] The received ADS-B messages, 5G messages, ad hoc network messages and Remote ID messages are merged into a new message structure using Kalman filtering, which is defined as a Class S message.

[0016] After all S-type messages are corrected, they are pushed to the track display terminal in the local area network in real time through the MQTT protocol to monitor the situation of the drone.

[0017] Furthermore, the first data includes mission number, mode, response, flight number, altitude, speed, heading, latitude, longitude, signal strength, number of messages and time.

[0018] Furthermore, the second data includes sensor ID, signal strength, timestamp, channel, communication type, status, horizontal speed, vertical speed, latitude, longitude, altitude, height, horizontal accuracy, vertical accuracy, air pressure accuracy, speed accuracy and timestamp accuracy.

[0019] Furthermore, the third data includes aircraft type, flight mode, voltage, current, remaining power, number of satellite searches, roll angle, pitch angle, yaw angle, roll angular velocity, pitch angular velocity, yaw angular velocity, latitude, longitude, altitude, relative altitude, X-direction speed, Y-direction speed, Z-direction speed, GPS reliability, next waypoint distance, airspeed, ground speed and climb rate.

[0020] Furthermore, the fourth data includes connection status, flight mode, GPS status, next waypoint distance, voltage, remaining power, flight speed, roll angle, pitch angle, yaw angle, latitude, longitude and altitude.

[0021] Furthermore, the process of fusing the received ADS-B message, 5G message, ad hoc network message and Remote ID message into a new message structure by using Kalman filtering includes the following steps:

[0022] Set the frequency of ADS-B update messages to f1, the frequency of 5G update messages to f2, the frequency of Remote ID update messages to f3, and the frequency of ad hoc network update messages to f4; for S-type messages, set their update frequency to f s ; ω is used to measure the communication quality of various communication methods, which is defined as the ratio of the actual number of received messages to the theoretical number of received messages;

[0023] In a cycle T s =1 / f s During the time:

[0024] The received i ADS-B messages are placed in the local buffer, denoted as A = [A1, A2, ..., A i ], The j received 5G packets are placed in the local buffer, denoted as B = [B1, B2, ..., B j ], The received m Remote ID messages are placed in the local buffer, denoted as C = [C1, C2, ..., C m ], The n received ad hoc network messages are placed in the local buffer, denoted as D = [D1, D2, ..., D n ],

[0025] A cycle T s The received messages A, B, C, and D are averaged to get a unique message And the standard deviation of various types of data related to it;

[0026] The Kalman filter is used to fuse the same type of parameters from different messages, and the fusion data of all types of parameters are obtained and put into the S message in sequence;

[0027] For any type of parameter Ω, the corresponding field data in message A, message B, message C and message D are screened out, and the screened field data are fused to obtain the fused data Ω′ s ;Ω′ s =(1-K1-K2-K3)Ω+K1Ω B +K2Ω C +K3Ω D , where K1, K2, K3 are Kalman gains;

[0028] According to the communication quality of different communication modes, the fused data Ω′ s After correction, the fused data is recorded as Ω s , the specific correction method is:

[0029]

[0030] After simplification:

[0031] Ω s =(1-K1-K2-K3)Ω*R1+K1Ω B *R2+K2Ω C *R3+K3Ω D *R4,

[0032] Among them, R1, R2, R3 and R4 are correction parameters. s Inside, is a constant; μ is the number of correction parameters;

[0033] For the fused data Ωs Find the variance and find the value that makes Var(Ω s ) is the minimum Kalman gain.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The purpose of the present invention is to provide a collaborative data fusion method of ADS-B, 5G, ad hoc network and Remote ID based on Kalman filtering, which can efficiently establish real-time communication between the UAV and the ground end, and perform effective data fusion to assist the ground end in obtaining the flight status information of the UAV, and then realize real-time monitoring of the UAV flight status based on multi-dimensional data fusion, so as to increase the flight safety of the airspace and strengthen the airspace traffic management. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic diagram of the hardware device involved in the present invention;

[0037] Figure 2 ADS-B message data structure diagram of the present invention;

[0038] Figure 3 A schematic diagram of the Remote ID message data structure involved in the present invention;

[0039] Figure 4 This is a schematic diagram of the 5G message data structure involved in the present invention;

[0040] Figure 5 A schematic diagram of the data structure of a self-organizing network message involved in the present invention;

[0041] Figure 6 This is a schematic diagram of multi-mode data fusion based on Kalman filtering involved in the present invention. DETAILED DESCRIPTION

[0042] The embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0043] The embodiment of the present invention discloses a collaborative data fusion method of ADS-B, 5G, ad hoc network and Remote ID based on Kalman filtering, which adopts multimodal equipment to establish communication between the UAV and the ground terminal, so as to realize real-time monitoring of the UAV, and adopts Kalman rate wave to fuse various types of data, so as to integrate multiple types of data into a more comprehensive, accurate, reliable and timely information source.

[0044] The collaborative data fusion method specifically includes the following steps:

[0045] Step 1, build the drone ADS-B transmitter: connect the positioning receiving antenna to the positioning module, the positioning module transmits the data to ADS-B Out, which is packaged and compiled by ADS-B Out and broadcast by the ADS-B antenna;

[0046] Step 2, build the ground station ADS-B receiver: connect the receiving antenna to the RTL-SDR, and after transmitting the data to the PC, it will be decoded and saved locally;

[0047] Step 3, build the drone 5G transmitter: use the Raspberry Pi to read the drone flight control information, and the Raspberry Pi transmits the data to the 5G module, which encapsulates and compiles it and transmits it through the 5G network;

[0048] Step 4: Build the 5G receiving end of the ground station: connect the receiving antenna to the 5G receiving module, and after transmitting the data to the PC, it will be decoded and saved locally;

[0049] Step 5, build the drone self-organizing network transmitter: use the Raspberry Pi to read the drone flight control information, process it and transmit it to the self-organizing network module through the network port, and then transmit it through the self-organizing network;

[0050] Step 6, build the ground station self-organizing network receiving end: receive the data on the temporary local area network through the self-organizing network module, transmit the data to the PC for data processing, and save it locally;

[0051] Step 7, build the drone Remote ID transmitter: mount DroneBeacon on the drone, which has a built-in positioning data receiving antenna and broadcast antenna. The drone's positioning information can be transmitted via Bluetooth 4 / Bluetooth 5 / WiFi neighbor awareness networking (NAN) / WiFi beacon;

[0052] Step 8: Build the ground station Remote ID receiver: DroneScout will receive Remote ID messages in various modes, transmit them to the PC through the switch for data processing, and save them locally;

[0053] Step 9: Use Kalman filtering to fuse the received ADS-B messages, 5G messages, ad hoc network messages, and Remote ID messages into a new message structure, which is defined as a Class S message.

[0054] Step 10: Push all S-type messages to the track display terminal in the local area network in real time through the MQTT protocol, so as to efficiently monitor the flight status of the drone.

[0055] In step 1:

[0056] (1) First, connect the positioning module to the PC and use the software GnssToolKit3 to set the information source of the positioning data. The information sources include: Global Positioning System (GPS), Beidou Positioning System (BDS), and GPS+BDS dual mode.

[0057] (2) Connect the positioning module to the antenna to receive positioning data in real time.

[0058] (3) Connect the positioning module to ADS-B Out, and ADS-B Out will start broadcasting the real-time flight information of the drone.

[0059] In step 2:

[0060] (1) Install the dump1090 program on the PC to receive data on the 1090MHz frequency band.

[0061] (2) Connect the PC, RTL-SDR, and receiving antenna to receive ADS-B messages in real time.

[0062] In step 3:

[0063] (1) Connect the Raspberry Pi to the UAV flight control module and read the UAV flight status information from the flight control.

[0064] (2) The flight status information is decoded and input into the 5G module through the network port, and data is transmitted through the Internet.

[0065] In step 4: Connect the 5G module to the PC and receive the drone's flight status information through the Internet.

[0066] In step 5:

[0067] (1) Connect the Raspberry Pi to the UAV flight control module and read the UAV flight status information from the flight control.

[0068] (2) The flight status information is decoded and input into the self-organizing network module through the network port, and the data is transmitted through the local area network.

[0069] In step 6: connect the ad hoc network module to the PC, and receive the UAV flight status information through the local area network.

[0070] In step 7: Mount DroneBeacon on the drone, which has a built-in positioning data source and can broadcast positioning data. Select a specific communication mode, including Bluetooth 4 / Bluetooth 5 / WiFi neighbor awareness networking (NAN) / WiFi beacon.

[0071] In step 8:

[0072] (1) Connect the Remote ID receiving device (DroneScout) to the switch, and then connect the switch to the PC.

[0073] (2) DroneScout will receive Remote ID messages in various modes, decode them and save them to the PC.

[0074] In step 9, multiple types of data are fused based on Kalman filter points, and the fused new message type is defined as Class S. Set the frequency of ADS-B update messages to f1, the frequency of 5G update messages to f2, the frequency of Remote ID update messages to f3, and the frequency of ad hoc network update messages to f4. For Class S messages, set their update frequency to f s ω is used to measure the communication quality of various communication methods, which is defined as the ratio of the actual number of received messages to the theoretical number of received messages. s =1 / f s During the time:

[0075] (1) The i received ADS-B messages are placed in the local buffer, denoted as A = [A1, A2, ..., A i ],

[0076] (2) The j received 5G packets are placed in the local buffer, denoted as B = [B1, B2, ..., B j ],

[0077] (3) The received m Remote ID messages are placed in the local buffer, denoted as C = [C1, C2, ..., C m ],

[0078] (4) The n received ad hoc network messages are placed in the local buffer, denoted as D = [D1, D2, ..., D n ],

[0079] (5) The formats of the four types of messages A, B, C, and D are different, and contain different types of data. Some data of the same type exist in different fields of A, B, C, and D. For specific forms, see Figure 6 A class message contains data A i =(a i , b i , c i , d i , e i ), the data contained in the B-type message is Bj =(α j , β j , a j ,λ j , b j , c j , e j ), the C-type message contains data C m =(a m , b m , e m , β m ), the D-type message contains data D n =(δ n ,θ n ,λ n , e n ). It should be noted that lowercase English letters and Greek letters are used here to represent different types of data, which is only for the purpose of explaining the principle of data fusion. The specific data content and its corresponding field position in various messages shall be subject to the actual situation.

[0080] (6) A period T s The received message [A1, A2, ..., A i ] Perform arithmetic averaging to obtain a unique message Right now in At the same time, the standard deviation of each type of data is calculated.

[0081] (7) Calculate the unique message in the same way And the standard deviation of various types of data related to it.

[0082] (8) Kalman filtering is used to fuse similar data from different messages. Taking class a data as an example, it appears in the first field of class A message, the third field of class B message, and the first field of class C message. After fusion, it is recorded as a′ s , the specific method is:

[0083] a′ s =+K1( B -)+K2( C -),

[0084] a′ s =(1-K1-K2)+K 1B +K 2C ,

[0085] Where K1 and K2 are Kalman gains.

[0086] (9) The communication quality of different communication modes (ADS-B, 5G, ad hoc network, Remote ID) is different, so it is necessary to s The data of type a comes from three different types of messages, so the correction process involves the parameter ω A ,ω B ,ω C , the corrected fusion data is recorded as a s , the specific correction method is:

[0087]

[0088] a s =(1-K1-K2)*R1+K 1B *R2+K 2C *R3,

[0089] Among them, R1, R2, R3 are correction parameters, in a specific period T s In the figure, μ is a constant. μ is the number of correction parameters, which is related to the number of communication modes corresponding to the data types to be fused. Here, μ=3.

[0090] (10) For the fused data a s To find the variance, the specific method is:

[0091] Var(a s )=Var(R1(1-K1-K2))+Var(R2K 1B )+Var(R3K 2C ),

[0092] Var(a s )=R1 2 (1-K1-K2) 2 Var(a A )+R2 2 K1 2 Var( B )+R3 2 K2 2 Var( C ),

[0093] Var(a s )=R1 2 (1-K1-K2) 2 σ A,a +R2 2 K12σ B,a +R3 2 K2 2 σ C,a ,

[0094] Var(a s )=(R12 σ A,a +R2 2 σ B,a )K1 2 +(R1 2 σ A,a +R3 2 σ C,a )K2 2 +R1 2 σ A,a (2K1K2-2K1-2K2+1).

[0095] (11) Find the value that makes Var(a s ) is the smallest Kalman gain, which is specifically determined by setting Var(a s ) with respect to K1 is equal to Var(a s ) with respect to K2 is equal to 0, that is:

[0096]

[0097] (12) In the same way, find b s , c s , d s , e s , α s , β s , δ s The data is merged and put into S messages in sequence. It should be pointed out that:

[0098] If a certain type of data exists in 4 types of messages, such as class e data, which exists in the 5th field of class A messages, the 7th field of class B messages, the 3rd field of class C messages, and the 4th field of class D messages, then e s There are 3 Kalman coefficients (K1, K2, K3) and 4 correction coefficients (R1, R2, R3, R4). Here, μ=4, specifically:

[0099] e s =R1e A +K1(R2e B -R1e A )+K2(R3e c -R1e A )+K3(R4e D -R1e A ).

[0100] If a certain type of data only exists in type 2 messages, such as type β data, which exists in the second field of type B messages and the fourth field of type C messages, then β s There is only one Kalman coefficient (K1) and two correction coefficients (R1, R2), where μ = 2, specifically:

[0101] β s =R1β B +K1(R1β B -R2β c ).

[0102] If a certain type of data only exists in a Class 1 message, such as class δ data, which exists in the first field of a Class D message, then δ s There is no Kalman coefficient or correction coefficient, specifically:

[0103] δ s =δ D .

[0104] In step 10, the MQTT protocol is used to push the S-type message to the track display terminal to display the flight status information of the drone, thereby realizing real-time monitoring of the drone.

[0105] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0106] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0107] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0108] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0109] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0110] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A collaborative data fusion method for ADS-B, 5G, ad hoc network and Remote ID based on Kalman filtering, characterized in that: The collaborative data fusion method comprises the following steps: Connect the positioning receiving antenna to the positioning module. The positioning module transmits the UAV positioning data sent by the positioning receiving antenna to ADS-B Out as the first data. After ADS-B Out encapsulates and decodes the first data, it is broadcasted to the receiving antenna by the ADS-B antenna. The receiving antenna is connected to the RTL-SDR, and the ADS-B message corresponding to the first data is transmitted to the PC for decoding and saved locally. The first Raspberry Pi is used to read the flight control information of the drone, and the information is transmitted to the 5G module as the second data. The 5G module encapsulates and decodes the information, and then transmits the information to the receiving antenna through the 5G network. The receiving antenna is connected to the 5G receiving module, and the 5G message corresponding to the second data is transmitted to the PC for decoding and saved locally. The second Raspberry Pi is used to read the UAV flight control information, which is used as the third data. After processing, it is transmitted to the self-organizing network module through the network port, and then transmitted to the self-organizing network receiving end of the ground station through the self-organizing network. The self-organizing network receiving end of the ground station transmits the self-organizing network message corresponding to the third data to the PC for data processing; Build the drone Remote ID transmitter: Mount the DroneBeacon on the drone, which has a built-in positioning data source. The acquired drone positioning information is used as the fourth data and transmitted to the DroneScout module of the Remote ID receiving end of the ground station via Bluetooth or WiFi. The DroneScout module receives the Remote ID message corresponding to the fourth data and transmits it to the PC through the switch for local storage. The received ADS-B messages, 5G messages, ad hoc network messages and Remote ID messages are merged into a new message structure using Kalman filtering, which is defined as a Class S message. After all S-type messages are corrected, they are pushed to the track display terminal in the local area network in real time through the MQTT protocol to monitor the situation of the drone; The process of fusing received ADS-B messages, 5G messages, ad hoc network messages, and Remote ID messages into a new message structure using Kalman filtering includes the following steps: Set the frequency of ADS-B update messages to f1, the frequency of 5G update messages to f2, the frequency of Remote ID update messages to f3, and the frequency of ad hoc network update messages to f4; for S-type messages, set their update frequency to f s ; ω is used to measure the communication quality of various communication methods, which is defined as the ratio of the actual number of received messages to the theoretical number of received messages; In a cycle T s =1 / f s During the time: The received i ADS-B messages are placed in the local buffer, denoted as A = [A1, A2, ..., A i ], The j received 5G packets are placed in the local buffer, denoted as B = [B1, B2, ..., B j ], The received m Remote ID messages are placed in the local buffer, denoted as C = [C1, C2, ..., C m ], The n received ad hoc network messages are placed in the local buffer, denoted as D = [D1, D2, ..., D n ], A cycle T s The received messages A, B, C, and D are averaged to get a unique message And the standard deviation of various types of data related to it; The Kalman filter is used to fuse the same type of parameters from different messages, and the fusion data of all types of parameters are obtained and put into the S message in sequence; For any type of parameter Ω, the corresponding field data in message A, message B, message C and message D are screened out, and the screened field data are fused to obtain the fused data Ω′ s ;Ω′ s =(1-K1-K2-K3)Ω+K1Ω B +K2Ω C +K3Ω D , where K1, K2, K3 are Kalman gains; According to the communication quality of different communication modes, the fused data Ω′ s After correction, the fused data is recorded as Ω s , the specific correction method is: After simplification: Oh s =(1-K1-K2-K3)Ω*R1+K1Ω B *R2+K2Ω C *R3+K3Ω D *R4, Among them, R1, R2, R3 and R4 are correction parameters. s Inside, is a constant; μ is the number of correction parameters; For the fused data Ω s Find the variance and find the value that makes Var(Ω s ) is the minimum Kalman gain.

2. The collaborative data fusion method of ADS-B, 5G, ad hoc network and Remote ID based on Kalman filtering according to claim 1 is characterized in that: The first data includes mission number, mode, reply, flight number, altitude, speed, heading, latitude, longitude, signal strength, number of messages and time.

3. The collaborative data fusion method of ADS-B, 5G, ad hoc network and Remote ID based on Kalman filtering according to claim 1 is characterized in that: The second data includes sensor ID, signal strength, timestamp, channel, communication type, status, horizontal speed, vertical speed, latitude, longitude, altitude, height, horizontal accuracy, vertical accuracy, air pressure accuracy, speed accuracy and timestamp accuracy.

4. The collaborative data fusion method of ADS-B, 5G, ad hoc network and Remote ID based on Kalman filtering according to claim 1 is characterized in that: The third data includes aircraft type, flight mode, voltage, current, remaining power, number of satellite searches, roll angle, pitch angle, yaw angle, roll angular velocity, pitch angular velocity, yaw angular velocity, latitude, longitude, altitude, relative altitude, X-direction speed, Y-direction speed, Z-direction speed, GPS reliability, next waypoint distance, airspeed, ground speed and climb rate.

5. The collaborative data fusion method of ADS-B, 5G, ad hoc network and Remote ID based on Kalman filtering according to claim 1 is characterized in that: The fourth data includes connection status, flight mode, GPS status, next waypoint distance, voltage, remaining power, flight speed, roll angle, pitch angle, yaw angle, latitude, longitude and altitude.

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