Radar detection and radio detection unmanned aerial vehicle signal fusion method in complex environment

Abstract

The invention relates to the technical field of anti-unmanned aerial vehicle systems, and discloses a radar detection and radio detection unmanned aerial vehicle signal fusion method in a complex environment, which comprises the following steps: S1, signal acquisition: detecting data of an air flying target by adopting radar equipment, radio equipment and photoelectric equipment; s3, AI intelligent analysis and screening: analyzing and processing detected target data through an AI intelligent technology, and filtering invalid target data in detection equipment, S4, eliminating registered, legal and reported unmanned aerial vehicle targets through target identification of multi-platform data comparison, and S5, flexibly disposing and processing illegal flying unmanned aerial vehicles through multiple means; according to the method, simultaneous access of multiple data sources is supported, the advantages of multiple devices are played through fusion technical means and layer-by-layer filtering and screening, interference options are gradually removed, finally, the unmanned aerial vehicle is successfully identified, and defense and attack are carried out on enemy targets in an effective range.

Description

Technical Field

[0001] The present invention relates to the technical field of anti - drone systems, and particularly to a method for fusing radar detection and radio detection of drone signals in a complex environment. Background Art

[0002] An anti - drone system refers to a defense system used to counter drone systems, which uses a variety of technical means to detect, identify, interfere with, deceive, control, or even destroy drones.

[0003] In the context of the gradually expanding drone market, the probability of events such as unlicensed flight and random flight of drones shows an upward trend. Investigations have found that the occurrence of such events poses a serious threat to civil aviation airports, the safety of event organizations, etc. Coupled with the characteristics of drones such as being flexible, easy to operate, and having a low manufacturing cost, they are easily exploited by lawbreakers. In order to avoid accidents, it is necessary to use drone detection and counter - measure technologies for effective prevention, achieving comprehensive detection, accurate positioning, effective interference, timely capture, and destruction, creating favorable conditions for protecting national security and flight route safety.

[0004] Current mainstream anti - drone technologies include radar, optoelectronics, radio detection, and radio protocol cracking. Although radar technology is mature, there are problems such as clutter, false alarms; although optoelectronics has controllable recognition accuracy and traceable evidence collection, it is greatly affected by weather and has a short operating range; although radio detection can work automatically and unattended, it has low accuracy and cannot distinguish between enemies and friends; although radio protocol cracking can be attended, has a long operating range, and can identify targets, it has great technical difficulties.

[0005] Therefore, we need to propose a method for fusing radar detection and radio detection of drone signals in a complex environment, which supports simultaneous access to multiple data sources. Through fusion technology means, layer - by - layer filtering and screening are carried out, leveraging the advantages of multiple devices, gradually removing interference options, and finally successfully identifying drones to conduct defensive strikes on enemy targets within the effective range. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for fusing radar detection and radio detection of drone signals in a complex environment, which supports simultaneous access to multiple data sources. Through fusion technology means, layer - by - layer filtering and screening are carried out, leveraging the advantages of multiple devices, gradually removing interference options, and finally successfully identifying drones to conduct defensive strikes on enemy targets within the effective range, so as to solve the problems raised in the above - mentioned background art.

[0007] To achieve the above purpose, the present invention provides the following technical solution: A method for fusing radar detection and radio detection of drone signals in a complex environment, including the following steps:

[0008] S1. Signal acquisition: Use radar equipment, radio equipment, and optoelectronic equipment to detect data of airborne targets.

[0009] S2. Multi-source heterogeneous data fusion: Fuse the target data detected by radar, optoelectronic, and radio detection equipment.

[0010] S3. AI intelligent analysis and screening: Analyze and process the detected target data through AI intelligent technology, filter out invalid target data in the detection equipment, and provide accurate data for final target recognition.

[0011] S4. Target recognition through multi-platform data comparison: Exclude registered, legal, and reported drone targets by accessing third-party platform data and combining it with the data of its own platform.

[0012] S5. Flexible disposal by multiple means: According to the actual on-site situation, adopt multiple disposal means to deal with illegally flying drones.

[0013] Preferably, in step S1, the radio equipment includes ADS-B equipment, signal detection equipment, and spectrum equipment. Since the protocols between different equipment are different, the signal data detected by all equipment needs to be integrated into a unified standard protocol, and the data is summarized and integrated based on the standard protocol.

[0014] Preferably, in step S1, the spectrum detection range of radio detection is delimited into an interval segment every 100 MHz. With a time interval of 0.2 seconds, the number of times the spectrum higher than 2 times the current average clutter amplitude appears in each spectrum interval segment is cumulatively counted. When the characteristic frequency band is detected continuously for 3 seconds in a certain spectrum interval segment, the targets detected by the radar equipment are traversed for further confirmation.

[0015] Preferably, in step S2, the targets detected by the radar equipment are traversed to determine whether the targets are within the azimuth sector threshold range detected by radio detection. If not within the azimuth sector threshold range, the targets detected by the radar are considered false alarms and the targets are filtered out; otherwise, they are retained.

[0016] Preferably, in step S3, the invalid target data includes false alarm targets of radar equipment, civil aviation aircraft targets of ADS-B, bird and kite targets of optoelectronics, and model toy targets of signal detection equipment.

[0017] Preferably, in step S5, for illegally flying drones, the disposal means include shooting down, capturing, or radio countermeasures. Among them, radio countermeasures include cutting off the navigation signal, cutting off the video transmission signal, returning, forced landing, deception, and driving away.

[0018] Preferably, the effective target detected by the detection device is set as the target recognition module. After obtaining the three-dimensional coordinate information of the target recognition module, unified timestamp configuration processing is performed. The processing method is as follows: Taking the A moment when the radar data is received as a reference, taking the latest returned data B of the target recognition module and the previous set of data C, and calculating the data D corresponding to the target recognition module at the A moment according to the data B and data C, and comparing the radar data at the A moment with the data D; among them, the types of data include: timestamp, longitude, latitude, and altitude.

[0019] Preferably, the specific process of calculating the data D corresponding to the target recognition module at the A moment is as follows: Taking the equipment installation point of the target recognition module as a reference, in the geocentric polar coordinate system, the longitude, latitude, and altitude of the data B and data C are respectively converted into the distance, azimuth, and pitch information of the data B and data C;

[0020] When the interval Time_AB between the A moment and the time corresponding to the data B is less than 20 milliseconds, direct comparison is performed without further calculation; when the time interval Time_AB is greater than 20 milliseconds, calculation is performed: Let

[0021] Time_BA = TB - TA

[0022] Time_AB = TA - TB;

[0023] Among them, Time_BA is the time interval between the time corresponding to the data B and the A moment, Time_AB is the time interval between the A moment and the time corresponding to the data B, TA is the time at the A moment, and TB is the time corresponding to the data B.

[0024] Preferably, calculate the distance vectors in the Y, Z, and X directions according to the distance, azimuth, and pitch information of the data B and data C relative to the ground station, where X represents east, Y represents north, and Z represents sky; LB_Y = B_distance × cos(B_pith ÷ 180 × π) × cos(B_direction ÷ 180 × π) LB_X = B_distance × cos(B_pith ÷ 180 × π) × sin(B_direction ÷ 180 × π)

[0025] LB_Z = B_distance × sin(B_pith ÷ 180 × π);

[0026] LC_Y = C_distance × cos(C_pith ÷ 180 × π) × cos(C_direction ÷ 180 × π) LC_X = C_distance × cos(C_pith ÷ 180 × π) × sin(C_direction ÷ 180 × π)

[0027] LC_Z = C_distance × sin(C_pith ÷ 180 × π);

[0028] Where B_distance represents the distance of data B, B_pith represents the pitch of data B, B_direction represents the azimuth of data B, where C_distance represents the distance of data C, C_pith represents the pitch of data C, and C_direction represents the azimuth of data C;

[0029] LB_Y, LB_Z, LB_X represent the distance vectors of data B in the north, sky, and east directions, and LC_Y, LC_Z, LC_X represent the distance vectors of data C in the north, sky, and east directions.

[0030] Preferably, the distance of data B, the azimuth of data B, and the pitch of data B are respectively the distance, azimuth, and pitch information of data B relative to the ground station, and the distance of data C, the azimuth of data C, and the pitch of data C are respectively the distance, azimuth, and pitch information of data C relative to the ground station; Calculate the distance vectors of data D in the north, sky, and east directions according to the polar of the distance vectors of data B and data C:

[0031] LD_Y = LB_Y + (LB_Y - LC_Y) ÷ Time_BA × Time_AB

[0032] LD_Z = LB_Z + (LB_Z - LC_Z) ÷ Time_BA × Time_AB

[0033] LD_X = LB_X + (LB_X - LC_X) ÷ Time_BA × Time_AB;

[0034] Where LD_Y, LD_Z, and LD_X are respectively the distance vectors of data D in the north, sky, and east directions. Convert LD_Y, LD_Z, and LD_X into distance vectors in the geocentric coordinate system, and then convert the distance vectors in the geocentric coordinate system into longitude, latitude, and altitude information to obtain the three-dimensional space coordinates of data D, and then comparison, fusion can be carried out to filter out illegal targets.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] The present invention supports simultaneous access to multiple data sources. Through fusion technical means, layer-by-layer filtering and screening are carried out to give play to the advantages of multiple devices, gradually remove interference options, and finally successfully identify the unmanned aerial vehicle to carry out defensive strikes on enemy targets within the effective range. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a flowchart of the present invention;

[0038] Figure 2 is a flowchart of the present invention;

[0039] Figure 3 is a schematic diagram of the three-dimensional coordinate system for identifying the target of the present invention. Detailed implementation manners

[0040] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0041] Please refer to Figures 1 - 3 , the present invention provides a technical solution: a method for fusing radar detection and radio detection unmanned aerial vehicle signals in a complex environment, including the following steps:

[0042] S1. Signal acquisition: Use radar equipment, radio equipment, and optoelectronic equipment to detect data of aerial flying targets;

[0043] In step S1, the radio equipment includes ADS-B equipment, signal detection equipment, and spectrum equipment. Since the protocols between different equipment are different, the signal data detected by all equipment needs to be integrated into a unified standard protocol, and the data is summarized and integrated based on the standard protocol.

[0044] Radar systems usually use dedicated communication protocols to transmit detected target information, including the distance, speed, azimuth, etc. of the target; optoelectronic detection equipment, such as infrared or visible light cameras, usually uses image transmission protocols to send captured image or video data. ADS-B equipment: uses the ADS-B protocol to broadcast the position, speed, and other relevant information of the aircraft; signal detection equipment: uses a specific radio communication protocol to transmit detected signals or data, spectrum equipment: uses spectrum sensing and measurement protocols to collect and analyze signals in the radio spectrum;

[0045] The standard protocol can cover all data types and formats required for transmission by all equipment, while ensuring the accuracy and integrity of the data.

[0046] During data integration, data from different equipment is collected, cleaned, transformed, and stored. For radar, optoelectronic, and radio detection equipment, data interfaces need to be established separately to receive the data they transmit. During the data integration process, the data needs to be verified and validated to ensure the accuracy and integrity of the data;

[0047] Then, the integrated data is processed and analyzed to extract useful information and generate comprehensive situation awareness.

[0048] In step S1, the spectrum detection range of radio detection is delimited into an interval segment every 100 MHz, which helps to more accurately monitor and analyze signals in different frequency bands, thereby more effectively identifying the spectrum characteristics of UAV communication and control signals. With a time interval of 0.2 seconds, the continuity and real-time nature of the data are ensured, which helps to timely detect and track the spectrum characteristics of UAVs. The number of occurrences of spectra higher than twice the current average clutter amplitude in each spectrum interval segment is cumulatively counted to screen out potential UAV signals, because UAV communication and control signals usually have a higher amplitude than background clutter. When a characteristic frequency band is detected continuously for 3 seconds in a certain spectrum interval segment, this is regarded as a potential UAV signal. The characteristic frequency band usually refers to the frequency band occupied by UAV communication and control signals, and these frequency bands have important identification value in UAV detection. The targets detected by the radar equipment are traversed for further confirmation. The radar equipment can provide key information such as the position and speed of the UAV, which helps to more accurately identify the UAV.

[0049] S2. Multi-source heterogeneous data fusion: Fuse the target data detected by radar, optoelectronic and radio detection equipment;

[0050] In step S2, the targets detected by the radar equipment are traversed to determine whether the targets are within the azimuth sector threshold range detected by radio detection. If not within the azimuth sector threshold range, it is considered that the targets detected by the radar are all false alarms and the targets are filtered out, otherwise they are retained.

[0051] The radar equipment detects targets by transmitting and receiving electromagnetic waves and provides information such as the distance, speed and azimuth of the targets. The radio detection equipment identifies UAV communication and control signals by monitoring spectrum signals and provides information such as the frequency, intensity and direction of the signals;

[0052] The radio detection equipment can detect the direction of the signal, which is usually expressed as an azimuth angle (such as 0° to 360°). To combine with radar data, one or more azimuth sector thresholds are set, and these thresholds define the azimuth range from which the detected signals by radio detection may come.

[0053] It should be noted that the data of the radar and radio detection equipment need to be synchronized to ensure that the data at the same time point is used when judging the target azimuth.

[0054] S3. AI intelligent analysis and screening: Analyze and process the detected target data through AI intelligent technology, filter out invalid target data in the detection equipment, and provide accurate data for the final target identification;

[0055] In step S3, the invalid target data includes false alarm targets of radar equipment, civil aviation aircraft targets of ADS-B, bird and kite targets of optoelectronics, and model aircraft toy targets of signal detection equipment.

[0056] S4. Target recognition through multi-platform data comparison: By accessing third-party platform data and combining it with the data of its own platform, registered, legal, and reported UAV targets are excluded;

[0057] Integrate the UAV registration and reporting information provided by the third-party data platform with the data of its own platform, clean the integrated data to remove duplicate, incorrect, or invalid information, establish comparison rules based on the unique identifier of the UAV (such as serial number, model, etc.), set a comparison threshold, and if the similarity reaches a certain degree (such as 85%), it can be considered the same UAV; Compare the real-time detected UAV targets with the integrated UAV registration and reporting information, and according to the comparison rules, identify the registered, legal, and reported UAV targets, exclude the identified registered, legal, and reported UAV targets from the real-time detection results, and retain other unregistered or unreported UAV targets as potential risk points for further processing.

[0058] S5. Flexible disposal by multiple means: According to the actual on-site situation, take multiple disposal means to deal with illegally flying UAVs.

[0059] In step S5, for illegally flying UAVs, the disposal means include shooting down, capturing, or radio countermeasures, where radio countermeasures include cutting off the navigation signal, cutting off the video transmission signal, returning, forced landing, deception, and driving away.

[0060] The fusion of radar detection and radio signal detection includes the following methods:

[0061] Set the effective targets detected by the detection equipment as the target recognition module. After obtaining the three-dimensional coordinate information of the target recognition module, perform unified timestamp configuration processing. The processing method is: taking the A moment when the radar data is received as a reference, taking the latest returned data B of the target recognition module and the previous set of data C, and calculating the data D corresponding to the target recognition module at the A moment based on data B and data C, and comparing the radar data at the A moment with data D; Among them, the types of data include: timestamp, longitude, latitude, and altitude.

[0062] The specific process of calculating the data D corresponding to the target recognition module at the A moment is: taking the equipment installation point of the target recognition module as a reference, in the geocentric polar coordinate system, respectively convert the longitude, latitude, and altitude of data B and data C into the distance, azimuth, and pitch information of data B and data C;

[0063] When the interval Time_AB between moment A and the time corresponding to data B is less than 20 milliseconds, direct comparison is made and no further calculation is performed; when the time interval Time_AB is greater than 20 milliseconds, the following calculation is carried out: Let

[0064] Time_BA = TB - TA

[0065] Time_AB = TA - TB;

[0066] where Time_BA is the time interval between the time corresponding to data B and moment A, Time_AB is the time interval between moment A and the time corresponding to data B, TA is the time of moment A, and TB is the time corresponding to data B.

[0067] Calculate the distance vectors of data B and data C in the Y, Z, and X directions based on their distances, azimuths, and pitches relative to the ground station, where X represents east, Y represents north, and Z represents sky;

[0068] LB_Y = B_distance × cos(B_pith ÷ 180 × π) × cos(B_direction ÷ 180 × π) LB_X = B_distance × cos(B_pith ÷ 180 × π) × sin(B_direction ÷ 180 × π)

[0069] LB_Z = B_distance × sin(B_pith ÷ 180 × π);

[0070] LC_Y = C_distance × cos(C_pith ÷ 180 × π) × cos(C_direction ÷ 180 × π) LC_X = C_distance × cos(C_pith ÷ 180 × π) × sin(C_direction ÷ 180 × π)

[0071] LC_Z = C_distance × sin(C_pith ÷ 180 × π);

[0072] where B_distance represents the distance of data B, B_pith represents the pitch of data B, B_direction represents the azimuth of data B, where C_distance represents the distance of data C, C_pith represents the pitch of data C, and C_direction represents the azimuth of data C;

[0073] LB_Y, LB_Z, and LB_X represent the distance vectors of data B in the north, sky, and east directions, and LC_Y, LC_Z, and LC_X represent the distance vectors of data C in the north, sky, and east directions.

[0074] The distance, azimuth, and elevation of Data B are the distance, azimuth, and elevation information of Data B relative to the ground station, respectively. The distance, azimuth, and elevation of Data C are the distance, azimuth, and elevation information of Data C relative to the ground station, respectively. Calculate the distance vectors of Data D in the north, sky, and east directions based on the distance vectors of Data B and Data C:

[0075] LD_Y = LB_Y + (LB_Y - LC_Y) ÷ Time_BA × Time_AB

[0076] LD_Z = LB_Z + (LB_Z - LC_Z) ÷ Time_BA × Time_AB

[0077] LD_X = LB_X + (LB_X - LC_X) ÷ Time_BA × Time_AB;

[0078] Among them, LD_Y, LD_Z, and LD_X are the distance vectors of Data D in the north, sky, and east directions, respectively. Convert LD_Y, LD_Z, and LD_X into the distance vectors in the geocentric coordinate system, and then convert the distance vectors in the geocentric coordinate system into longitude, latitude, and altitude information to obtain the three-dimensional space coordinates of Data D, and then comparison, fusion can be carried out to filter out illegal targets.

[0079] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for fusion of radar detection and radio detection drone signals in complex environments, characterized by: The following steps are involved: S1. Signal acquisition: using radar equipment, radio equipment, and optoelectronic equipment to detect data of flying targets in the air; S2. Multi-source heterogeneous data fusion: Fusion of target data detected by radar, optoelectronic and radio detection equipment; S3, AI intelligent analysis and screening: Analyze and process the detected target data through AI intelligent technology, filter out invalid target data in the detection equipment, and provide accurate data for the final target identification; S4. Target identification through multi-platform data comparison: By accessing third-party station data and combining it with the data of its own platform, registered, legal, and reported drone targets are excluded; S5. Flexible disposal through multiple means: Based on the actual on-site situation, adopt a variety of disposal methods to deal with illegally flying drones.

2. According to the method for fusion of radar detection and radio detection of drone signals in complex environments in claim 1, it is characterized by: In step S1, the radio equipment includes ADS-B equipment, signal detection equipment and spectrum equipment. The protocols between different devices are different. It is necessary to integrate the signal data detected by all devices into a unified standard protocol, and summarize and integrate the data based on the standard protocol.

3. The method for fusion of radar detection and radio detection drone signals in complex environments according to claim 1 is characterized by: In step S1, the spectrum detection range of radio detection is divided into an interval every 100 MHz, and the number of frequency spectrum occurrences in each spectrum interval that is more than twice the current average clutter amplitude is accumulated and counted at a time interval of 0.2 seconds. When a characteristic frequency band is detected in a spectrum interval for 3 consecutive seconds, the target detected by the traversal radar equipment is further confirmed.

4. The method for fusion of radar detection and radio detection of drone signals in complex environments according to claim 1 is characterized by: In step S2, the targets detected by the radar equipment are traversed to determine whether the targets are within the azimuth sector threshold range detected by the radio detection. If not, the targets detected by the radar are considered to be false alarms and are filtered out, otherwise they are retained.

5. The method for fusion of radar detection and radio detection drone signals in complex environments according to claim 1 is characterized by: In step S3, invalid target data includes false alarm targets of radar equipment, civil aircraft targets of ADS-B, flying birds and kites targets of optoelectronics, and model aircraft toy targets of signal detection equipment.

6. The method for fusion of radar detection and radio detection drone signals in complex environments according to claim 1 is characterized by: In step S5, for illegally flying drones, the processing methods include shooting down, capturing or radio countermeasures, among which radio countermeasures include cutting off navigation signals, cutting off image transmission signals, returning, forced landing, decoy, and driving away.

7. The method for fusion of radar detection and radio detection drone signals in complex environments according to claim 1 is characterized by: The valid target detected by the detection equipment is set as the target recognition module. After obtaining the three-dimensional coordinate information of the target recognition module, a unified timestamp configuration process is performed. The processing method is: taking the time A when the radar data is received as a reference, take the latest data B returned by the target recognition module and the previous set of data C, calculate the data D corresponding to the target recognition module at time A based on data B and data C, and compare the radar data at time A with data D; the types of data include: timestamp, longitude, latitude, and altitude.

8. The method for fusion of radar detection and radio detection of drone signals in complex environments according to claim 7 is characterized by: The specific process of calculating the data D corresponding to the target recognition module at time A is as follows: based on the equipment installation point of the target recognition module, the longitude, latitude and altitude of data B and data C are converted into the distance, azimuth and elevation information of data B and data C respectively in the geocentric polar coordinate system; When the interval Time_AB between time A and the time corresponding to data B is less than 20 milliseconds, direct comparison is no longer calculated; when the time interval Time_AB is greater than 20 milliseconds, calculation is performed: Time_BA=TB-TA Time_AB = TA - TB; Among them, Time_BA is the time interval between the time corresponding to data B and time A, Time_AB is the time interval between time A and the time corresponding to data B, TA is the time of time A, and TB is the time corresponding to data B.

9. The method for fusion of radar detection and radio detection drone signals in complex environments according to claim 8, characterized in that: According to the distance, azimuth and elevation information of data B and data C relative to the ground station, the distance vectors in the three directions of Y, Z and X are calculated, where X represents east, Y represents north and Z represents sky; LB_Y=B_distance×cos(B_pith÷180×π)×cos(B_direction÷180×π)LB_X=B_distance×cos(B_pith÷180×π)×sin(B_direction÷180×π) LB_Z=B_distance×sin(B_pith÷180×π); LC_Y=C_distance×cos(C_pith÷180×π)×cos(C_direction÷180×π)LC_X=C_distance×cos(C_pith÷180×π)×sin(C_direction÷180×π) LC_Z=C_distance×sin(C_pith÷180×π); Where B_distance represents the distance of data B, B_pith represents the pitch of data B, and B_direction represents the direction of data B. Where C_distance represents the distance of data C, C_pith represents the pitch of data C, and C_direction represents the direction of data C. LB_Y, LB_Z, and LB_X represent the distance vector of data B in the north, sky, and east directions, and LC_Y, LC_Z, and LC_X represent the distance vector of data C in the north, sky, and east directions.

10. The method for fusion of radar detection and radio detection of drone signals in complex environments according to claim 9, characterized in that: The distance of data B, the azimuth of data B, and the elevation of data B are the distance, azimuth, and elevation information of data B relative to the ground station, respectively. The distance of data C, the azimuth of data C, and the elevation of data C are the distance, azimuth, and elevation information of data C relative to the ground station, respectively. The distance vector of data D in the north, sky, and east directions is calculated based on the distance vectors of data B and data C: LD_Y=LB_Y+(LB_Y-LC_Y)÷Time_BA×Time_AB LD_Z=LB_Z+(LB_Z-LC_Z)÷Time_BA×Time_AB LD_X=LB_X+(LB_X-LC_X)÷Time_BA×Time_AB; Among them, LD_Y, LD_Z, and LD_X are the distance vectors of data D in the north, sky, and east directions respectively. LD_Y, LD_Z, and LD_X are converted into distance vectors in the geocentric coordinate system, and then the distance vectors in the geocentric coordinate system are converted into longitude, latitude, and altitude information to obtain the three-dimensional spatial coordinates of data D, which can be compared and fused to filter out illegal targets.

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