A method and device for monitoring drone intrusion

Abstract

The present invention provides a method and device for monitoring the intrusion of unmanned aerial vehicles, wherein the monitoring method comprises: S1, taking a ground station as the center, a plurality of flat panel antennas are arranged in a surrounding and tilted manner, the plurality of the flat panel antennas are combined into an omnidirectional detection area, the flat panel antennas are used to detect the commonly used communication frequency bands of unmanned aerial vehicles, the flat panel antennas adopt a phased array beam scanning algorithm, and the like; when a certain signal source has more than two link frequency bands at the same time, and the flat panel antenna is used to detect the commonly used communication frequency bands of unmanned aerial vehicles, the signal source is judged as a unmanned aerial vehicle with shooting capability, the beam direction and tilt angle are dynamically adjusted through the phased array beam scanning algorithm, and combined with step S2, the trajectory data of the real-time target unmanned aerial vehicle is obtained, and then the trajectory of the unmanned aerial vehicle is determined to be an intrusive unmanned aerial vehicle, and corresponding countermeasures are taken to avoid the unmanned aerial vehicle's sneak shooting behavior. The present invention has the advantages of sensitive detection and high detection accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle intrusion monitoring, and in particular to a method and a monitoring device for unmanned aerial vehicle intrusion monitoring. Background Art

[0002] The rapid development of drone technology has shown great potential in aerial photography, logistics, agriculture and other fields, but its privacy infringement issues are becoming increasingly prominent, especially unauthorized photography, which poses a serious threat to personal privacy and public safety.

[0003] This type of drone is small in size and flexible. Traditional radar and infrared methods are not ideal for identifying this type of small drone. Radio detection has the advantages of long distance, long warning time and long warning distance. However, most radio detection equipment on the market now matches fixed-model drones and cannot detect drones that have been modified in transmission spectrum, protocol message format, or new types. In addition, radio detection cannot accurately locate the distance of the drone. Traditional directional antenna detection is mostly with a directional antenna and a rotating gimbal, which has a complex mechanical structure and is prone to failure, and cannot achieve the effect of simultaneous all-round detection.

[0004] In order to meet this challenge, the present invention provides a drone intrusion monitoring method and monitoring device to ensure that small and maneuverable drones can be accurately identified and provide a basis for subsequent anti-intrusion measures. Summary of the invention

[0005] In view of the deficiencies in the prior art, the purpose of the present invention is to propose a drone intrusion monitoring method and a monitoring device to solve the problems mentioned in the above background technology section.

[0006] The present invention is achieved through the following technical solutions:

[0007] A method for monitoring intrusion of a drone, the monitoring method comprising:

[0008] S1. With the ground station as the center, several flat-panel antennas are arranged around and tilted. Several of the flat-panel antennas are combined into an omnidirectional detection area. The flat-panel antennas are used to detect the common communication frequency bands of drones. The flat-panel antennas use a phased array beam scanning algorithm to dynamically adjust the beam direction and tilt angle to achieve a detection area without blind spots, and collect signal sources in the detection area. When a signal source has more than two link frequency bands at the same time, it is judged as a target drone, and a communication signal feature data set containing modulation mode and pulse characteristics is generated;

[0009] S2. Obtain the definition of the initial state vector of the target UAV through the current beam direction of the flat-panel antenna, output the signal strength through the observation model, jointly obtain the observation Jacobian matrix for the signals in all link frequency bands, and cyclically adjust the beam direction in combination with the Kalman filter model so that the beam direction always follows the target UAV and generate real-time trajectory data;

[0010] S3. Set several sensitive areas in the detection area. Based on the real-time trajectory data set of the target UAV, determine whether it hovers for a long time or flies repeatedly in the sensitive area. If the target UAV hovers for a long time or flies repeatedly in the sensitive area, then determine this target UAV as an intruding UAV and take corresponding countermeasures.

[0011] Further, in step S2, the coordinate system of the target UAV is generated through the following steps:

[0012] S21. Establish a coordinate system with the center point of the ground station as the origin, and obtain the definition of the initial state vector of the UAV through the current beam direction of the flat-panel antenna:

[0013] ;

[0014] where is the initial position coordinate of the target UAV, is the initial velocity component of the target UAV, represents the signal strengths of the two link frequency bands of the target UAV;

[0015] S22. Output the signal strength through the observation model formula :

[0016] ;

[0017] where represents the beam azimuth angle of the flat-panel antenna; represents the elevation angle of the flat-panel antenna; represents the true azimuth angle of the target UAV; represents the true elevation angle of the target UAV; represents the distance from the target UAV to the flat-panel antenna, which can be inversely deduced from the signal strength; represents the th signal parameter of the link frequency band; represents the th signal wavelength of the link frequency band; represents the th observation noise of the link frequency band;

[0018] S23. When a signal source has two link frequency bands at the same time, jointly obtain the observation Jacobian matrix for the signals in the two link frequency bands :

[0019] ;

[0020] Then, the Kalman filter formula is used to provide a basis for the next round of beam direction adjustment:

[0021] ;

[0022] ;

[0023] In the formula, represents the Kalman gain, which is used to balance the weights of the predicted value and the observed value; represents the prior estimation error covariance matrix, which is used to describe the uncertainty of the UAV state estimation before the th observation; represents the transpose operation on the observation Jacobian matrix ; represents the observation noise covariance matrix; , represent the output signal strengths of two different link frequency bands of the target UAV at the th observation; represents the estimated value of the signal strength of the first link frequency band of the target UAV at the th observation; represents the estimated value of the signal strength of the second link frequency band of the target UAV at the th observation;

[0024] S24. Extract the coordinate system from the updated state vector , that is, when the signal strength is the maximum, record ;

[0025] Calculate through the maximum signal strength value , and substitute it into the coordinate transformation formula to obtain the coordinates of the target UAV, where:

[0026] ;

[0027] ;

[0028] In the formula, represents the signal wavelength, corresponding to the communication frequency band of the target UAV; represents the environment-related coefficient, reflecting the signal propagation loss; represents the directional gain function of the flat antenna.

[0029] Further, step S3 specifically includes:

[0030] S31. Centering on the ground station, establish a detection area coordinate system, and divide several sensitive areas in the detection area;

[0031] S32. Determine whether the UAV is located within a sensitive area. If the current coordinates of the target UAV fall within the boundary of any sensitive area, record the start time and location information of the target UAV staying in this sensitive area;

[0032] S33. Calculate the staying duration of the UAV within the sensitive area. If the staying time exceeds the preset time threshold, mark it as "long-term hovering in the sensitive area";

[0033] S34. Analyze the historical trajectory of the UAV, count the number of visits to the sensitive area. If the number of repeated entries into the sensitive area within a short time exceeds the threshold, mark it as "repeated flight in the sensitive area";

[0034] S35. According to the determination results of the two types of behavior characteristics of "long-term hovering in the sensitive area" and "repeated flight in the sensitive area", if the target UAV meets any condition, mark the target UAV as an intrusive UAV.

[0035] Further, the monitoring method further includes step S4:

[0036] S4. Input the generated communication signal feature dataset containing modulation mode and pulse characteristics to compare with the features in the pre-constructed UAV fingerprint database. When the similarity exceeds the set threshold, the model, brand, and identity information of the intrusive UAV can be determined and identified, and corresponding countermeasure interference means are taken; when the similarity is lower than the set threshold, the ground station detection personnel use a telescope or a zoom camera to observe the intrusive UAV, and fill in the model, brand, and identity information of the intrusive UAV into the UAV fingerprint database.

[0037] Further, the monitoring method further includes step S5:

[0038] S5. When an intrusive UAV is identified, and lamps are arranged around the flat antenna, and the lamps flash at a preset frequency to interfere with the white balance of the camera of the intrusive UAV.

[0039] Further, the monitoring method further includes step S6:

[0040] S6. When an intrusive UAV is identified, and a loudspeaker is arranged around the flat antenna, and the loudspeaker emits a warning sound to alert the intrusive UAV.

[0041] Furthermore, the detection bands of the flat antenna are: 433MHz, 900MHz, 2400MHz-2476MHz, 5725MHz-5829MHz, 840.5MHz-845MHz, 1430MHz-1444MHz.

[0042] On the other hand, the present invention provides a drone intrusion monitoring device, which is used to implement a drone intrusion monitoring method as described in any of the above items, and the drone intrusion monitoring device includes: a ground station as the center, and several flat-panel antennas are arranged around and tilted, and several of the flat-panel antennas are combined to form an omnidirectional detection area; it also includes an RF analysis device connected to the flat-panel antenna, and the RF analysis device is used to input a communication signal feature data set containing modulation mode and pulse characteristics into a pre-built drone fingerprint library for feature comparison, so as to identify the model, brand and identity information of the intrusive drone.

[0043] Furthermore, one of the flat panel antennas is individually connected to one of the RF analysis devices, or two of the flat panel antennas are correspondingly connected to one of the RF analysis devices, or all of the flat panel antennas are connected to an antenna selector, and the antenna selector is connected to the RF analysis device.

[0044] The beneficial effects of the present invention are as follows: when a certain signal source has more than two link frequency bands at the same time, and the flat antenna is used to detect the commonly used communication frequency bands of the drone, the signal source is judged to be a drone with shooting capabilities, and the beam direction and tilt angle are dynamically adjusted through a phased array beam scanning algorithm, and combined with step S2, the trajectory data of the real-time target drone is obtained, and then the drone's trajectory is determined to be an invasive drone, and corresponding countermeasures are taken to avoid the drone's sneak shooting behavior. The present invention has the advantages of sensitive detection and high detection accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 The present invention is a flowchart of a method for monitoring the intrusion of unmanned aerial vehicles.

[0046] Figure 2 The present invention is a schematic diagram of the stereoscopic structure of a drone intrusion monitoring device.

[0047] Figure 3 The present invention is a schematic diagram of the structure of a UAV intrusion monitoring device from a top view.

[0048] Figure 4 It is a connection diagram of the first embodiment of the flat antenna and the RF analysis device in the monitoring device of the present invention.

[0049] Figure 5 It is a connection diagram of the second embodiment of the flat antenna and the RF analysis device in the monitoring device of the present invention.

[0050] Figure 6 This is a schematic diagram of the connection between the flat antenna and the third embodiment of the RF analysis device in the monitoring device of the present invention. Detailed implementation manners

[0051] To make the purposes, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It should be noted here that the descriptions of these embodiments are used to help understand the present invention, but do not constitute a limitation on the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0052] Referring to Figures 1 to 3 As shown, a method for monitoring unmanned aerial vehicle (UAV) intrusion, the monitoring method includes:

[0053] S1. With the ground station as the center, a number of flat antennas are arranged in a circumferential and inclined manner, and the number of the flat antennas form an omnidirectional detection area. The flat antennas are used to detect the common communication frequency bands of UAVs. The flat antennas adopt a phased array beam scanning algorithm to dynamically adjust the beam direction and inclination angle to achieve a dead-angle-free detection area, and collect signal sources in the detection area. When a signal source has two or more link frequency bands at the same time, it is determined as the target UAV, and a communication signal feature data set including modulation mode and pulse characteristics is generated;

[0054] In step S1, when the signal source has two or more link frequency bands and the link frequency bands are all within the common communication frequency bands of UAVs detected by the flat antenna, it indicates that the signal source is initially determined as a UAV with the ability to take pictures, and thus it is calibrated as the target UAV.

[0055] Specifically, the detection frequency bands of the flat antennas are: 433 MHz, 900 MHz, 2.4 GHz (2400 MHz - 2476 MHz), 5.8 GHz (5725 MHz - 5829 MHz), 840.5 MHz - 845 MHz, 1430 MHz - 1444 MHz. Usually, a target UAV with the ability to take pictures has an uplink and a downlink. The main function of the uplink is to send instructions from the ground control station or remote control handle to the UAV for controlling the flight, propulsion, etc. of the UAV; the downlink is used to transmit the flight state of the UAV and the data collected during the execution of tasks by the UAV, such as images, videos, etc., back to the ground control station or remote control handle, etc.

[0056] Therefore, by setting the flat antenna to have the ability to detect the common communication frequency bands of drones and the cooperation of detecting more than two link frequency bands, drones with shooting capabilities can be quickly identified. Thus, other non-drone frequency band signals are excluded, and there is interference from signal sources with two-way communication on the uplink and downlink, such as mobile phones.

[0057] Through the phased array beam scanning algorithm of the flat antenna and in combination with step S2, the trajectory data of the real-time target drone is obtained, and then whether the trajectory of the drone is an invasive drone is determined, and corresponding countermeasures are taken to avoid the sneak shooting behavior of the drone. Specifically:

[0058] S2. Obtain the definition of the initial state vector of the target drone through the current beam direction of the flat antenna, and output the signal strength through the observation model. By combining the signals of all link frequency bands, an observation Jacobian matrix is obtained, and in combination with the Kalman filter model, the beam direction is cyclically adjusted so that the beam direction always follows the target drone and generates real-time trajectory data.

[0059] Through the observation Jacobian matrix of multiple link frequency bands, the dependence on a single frequency band can be reduced, and the joint processing of frequency band signals can suppress single-frequency interference and multipath effects. In addition, through parameter unification and formula linkage, the system realizes full-closed-loop high-precision tracking from beam control to coordinate generation, which is applicable to the dynamic monitoring of drones in complex environments. High-precision and strong-robustness dynamic tracking of drones can be achieved through a single phased array flat antenna.

[0060] For example, when the target drone only has two link frequency bands, in step S2, the coordinate system of the target drone is generated through the following steps:

[0061] S21. Establish a coordinate system with the center point of the ground station, and obtain the definition of the initial state vector of the drone through the current beam direction of the flat antenna:

[0062] ;

[0063] In the formula, is the initial position coordinate of the target drone, is the initial velocity component of the target drone, represents the signal strength of the two link frequency bands of the target drone;

[0064] S22. Output the signal strength through the observation model formula :

[0065] ;

[0066] In the formula, represents the beam azimuth angle of the flat antenna; Represents the elevation angle of the flat-panel antenna; Represents the true azimuth angle of the target UAV; Represents the true elevation angle of the target UAV; Represents the distance from the target UAV to the flat-panel antenna, which can be inferred by the signal strength; Represents the signal parameters of the th link frequency band; Represents the signal wavelength of the th link frequency band; Represents the observation noise of the

[0067] S23. When a signal source has two link frequency bands at the same time, the signals of the two link frequency bands are combined to obtain the observation Jacobian matrix :

[0068] ;

[0069] Then, the Kalman filter is used to provide a basis for the next round of beam direction adjustment:

[0070] ;

[0071] ;

[0072] In the formula, Represents the Kalman gain, which is used to balance the weights of the predicted value and the observed value; Represents the prior estimation error covariance matrix, which is used to describe the uncertainty of the UAV state estimation before the th observation; Represents the transpose operation on the observation Jacobian matrix ; Represents the observation noise covariance matrix; , Represents the output signal strengths of two different link frequency bands of the target UAV at the th observation; Represents the estimated value of the signal strength of the first link frequency band of the target UAV at the th observation; Represents the estimated value of the signal strength of the second link frequency band of the target UAV at the th observation;

[0073] S24. Extract the coordinate system from the updated state vector , that is, when the signal strength is the maximum, record ;

[0074] Through the maximum signal strength value Calculate , and substitute it into the coordinate transformation formula to obtain the coordinates of the target UAV , where:

[0075] ;

[0076] ;

[0077] In the formula, represents the signal wavelength, corresponding to the communication frequency band of the target UAV; represents the environment-related coefficient, reflecting the signal propagation loss; represents the directivity gain function of the planar antenna, characterizing the attenuation effect of the beam pointing deviation on the signal strength.

[0078] Specifically, the essence of is , by setting to reach the maximum signal strength value , the angular deviation between the beam pointing of the planar antenna and the true direction of the target UAV is eliminated. At this time , , that is, when the angular deviation is 0, the antenna gain is the largest; finally, substitute into to make it reach the maximum antenna gain .

[0079] Through the above formula steps, the coordinates output by the Kalman filter are used to calculate the true direction , and then guide the next round of beam adjustment to achieve the following transfer closed-loop of parameters:

[0080]

[0081] Through the observation Jacobian matrix of multiple link frequency bands, that is, through independent estimation, the dependence on a single frequency band is reduced. The joint processing of frequency band signals can suppress single-frequency interference and multipath effects. In addition, through parameter unification and formula linkage, the system realizes full-closed-loop high-precision tracking from beam control to coordinate generation, which is applicable to the dynamic monitoring of UAVs in complex environments. High-precision and strong-robustness UAV dynamic tracking can be achieved through the planar antenna of a single phased array.

[0082] S3. Set several sensitive areas in the detection area. By using the real-time trajectory dataset of the target UAV, determine whether it hovers or flies repeatedly in the sensitive area for a long time. If so, determine the target UAV as an intrusive UAV and take corresponding countermeasures.

[0083] Furthermore, the step S3 specifically includes:

[0084] S31, establishing a detection area coordinate system with the ground station as the center, and dividing a number of sensitive areas in the detection area;

[0085] S32, determining whether the drone is located in a sensitive area, if the current coordinates of the target drone fall within the boundary of any sensitive area, recording the start time and location information of the target drone's stay in the sensitive area;

[0086] S33, calculating the length of time the UAV stays in the sensitive area, and if the length of time exceeds a preset time threshold, marking it as “long-term hovering in a sensitive area”;

[0087] S34, analyzing the drone's historical trajectory and counting the number of visits to sensitive areas. If the number of repeated entries into sensitive areas in a short period of time exceeds a threshold, it is marked as "repeated flight in sensitive areas";

[0088] S35. Based on the judgment results of the two types of behavior characteristics, namely, “long-term hovering in a sensitive area” and “repeated flight in a sensitive area”, if the target UAV meets any one of the conditions, the target UAV is marked as an intrusive UAV.

[0089] The monitoring method further comprises step S4:

[0090] S4. Input the generated communication signal feature data set containing modulation mode and pulse characteristics into the pre-built UAV fingerprint library for feature comparison. When the similarity exceeds the set threshold, the model, brand and identity information of the intruding UAV can be determined and identified, and corresponding counter-interference measures can be taken. When the similarity is lower than the set threshold, the ground station detection personnel use a telescope or a zoom camera to observe the intruding UAV, and fill the model, brand and identity information of the intruding UAV into the UAV fingerprint library.

[0091] The drone intrusion monitoring method of the present invention is mainly applicable to the civilian field and various civilian scenes, including hot springs, hotels, concerts, urban communities, etc., and can effectively protect personal privacy and public safety. Therefore, when a possible candid shooting behavior is identified, the present invention collects drone information through the above step S4, specifically including the model, brand, identity, flight trajectory, intrusion time and location of the drone as evidence.

[0092] The drone intrusion monitoring method of the present invention is mainly applicable to the civilian field. Therefore, when countering drones, it is necessary to avoid damaging the target drone to prevent economic disputes. Based on the above reasons, the monitoring method further includes step S5:

[0093] S5. When an intruding drone is recognized and lamps are arranged around the flat antenna, the lamps flash at a preset frequency to interfere with the white balance of the camera of the intruding drone.

[0094] Through the above steps, the camera function of the drone can be effectively interfered, preventing it from taking secret photos, and at the same time avoiding damaging the drone, thus avoiding economic disputes. This technology affects the white balance of the drone's camera through the flashing of lights at a specific frequency, making the captured images lose their normal colors, so as to achieve the purpose of interference.

[0095] Further, the monitoring method further includes step S6:

[0096] S6. When an intruding drone is recognized and a loudspeaker is arranged around the flat antenna, the loudspeaker emits a prompt sound to warn the intruding drone.

[0097] By emitting a prompt sound through the loudspeaker, the drone operator can be reminded that their behavior may be illegal, prompting them to stop the intrusion behavior, and at the same time reminding the ground station staff on our side that there may be a drone intrusion behavior.

[0098] Refer to Figures 2 to 6 As shown, in Figure 2 and Figure 3 , R1 to R4 respectively represent the four detection surfaces of the four flat antennas. Among them, the number of flat antennas includes but is not limited to four, and T1 represents the target drone.

[0099] On the other hand, the present invention provides a drone intrusion monitoring device, which is used to implement a drone intrusion monitoring method as described in any one of the above. The drone intrusion monitoring device includes: several flat antennas are arranged around the ground station in a circumferential and inclined manner, and the several flat antennas are combined into an all-round detection area; it also includes an RF analysis device connected to the flat antenna, and the RF analysis device is used to compare the communication signal feature data set containing modulation methods and pulse characteristics with the features in a pre-constructed drone fingerprint library, so as to identify the model, brand and identity information of the intruding drone.

[0100] As a first embodiment, refer to Figure 4 As shown, one of the flat antennas is separately connected to one of the RF analysis devices. The advantages of this connection method are: the RF analysis devices of each flat antenna are independent of each other, with low coupling degree. If one fails, the other several can still be used and are easy to replace; the search speed is fast. The disadvantages are: the hardware cost is relatively high and the structure is relatively complex.

[0101] As a second embodiment, refer to Figure 5As shown, two of the flat antennas are respectively connected to an RF analysis device. The advantages of this connection method are as follows: two flat antennas share one RF analysis device, with a relatively low coupling degree and moderate cost; the search speed is moderate. The disadvantage is that if a single RF analysis device fails, the two flat antennas connected thereto will both malfunction.

[0102] As a third embodiment, refer to Figure 6 As shown, all of the flat antennas are connected to an antenna selector, and the antenna selector is connected to an RF analysis device. The advantages of this connection method are as follows: all flat antennas share one RF analysis device, with low cost. The disadvantages are as follows: the RF analysis device polls and switches antennas, with a relatively slow search speed; the coupling degree is high, and if the RF analysis device fails, all flat antennas will be unavailable.

[0103] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention.

[0104] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features.

[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for monitoring drone intrusion, characterized in that, The monitoring method includes: S1. With the ground station as the center, several flat antennas are arranged in a circumferential and inclined manner. The several flat antennas form an omnidirectional detection area. The flat antennas are used to detect the common communication frequency bands of unmanned aerial vehicles (UAVs). The flat antennas adopt a phased array beam scanning algorithm to dynamically adjust the beam direction and inclination angle, realize a dead - angle - free detection area, and collect signal sources within the detection area. When a signal source has two or more link frequency bands at the same time, it is judged as a target UAV, and a communication signal feature data set including modulation mode and pulse characteristics is generated. S2. Obtain the definition of the initial state vector of the target UAV through the current beam direction of the flat antenna, and output the signal intensity through the observation model. Combine the signals of all link frequency bands to obtain the observation Jacobian matrix, and combine with the Kalman filter model to cyclically adjust the beam direction so that the beam direction always follows the target UAV, and generate real - time trajectory data. S3. Set several sensitive areas in the detection area. Through the real - time trajectory data set of the target UAV, judge whether it hovers for a long time or flies repeatedly in the sensitive area. If the target UAV hovers for a long time or flies repeatedly in the sensitive area, then determine this target UAV as an intrusive UAV and take corresponding counter - measures.

2. The method for monitoring drone intrusion according to claim 1, characterized in that In step S2, the coordinate system of the target UAV is generated through the following steps: S21. Establish a coordinate system with the center point of the ground station, and obtain the definition of the initial state vector of the UAV through the current beam direction of the flat antenna: ； In the formula, is the initial position coordinate of the target UAV, is the initial velocity component of the target UAV, represents the signal strength of the two-link frequency bands of the target UAV; S22. Output the signal strength through the observation model formula : ； Wherein, represents the beam azimuth angle of the planar antenna; represents the elevation angle of the planar antenna; represents the true azimuth angle of the target UAV; represents the true elevation angle of the target UAV; represents the distance from the target UAV to the planar antenna, which can be inversely deduced from the signal strength; represents the signal parameter of the th link frequency band; represents the signal wavelength of the th link frequency band; represents the observation noise of the S23. When a certain signal source has two link frequency bands at the same time, the signals of the two link frequency bands are combined to obtain an observed Jacobian matrix :[[-1]] ； Then provide a basis for the next - round beam direction adjustment through the Kalman filter formula: ； ； In the formula, represents the Kalman gain, which is used to balance the weights of the predicted value and the observed value; represents the prior estimation error covariance matrix, which is used to describe the uncertainty of the UAV state estimation before the th observation; represents the transpose operation on the observation Jacobian matrix ; represents the observation noise covariance matrix; , represent the output signal strengths of two different link frequency bands of the target UAV at the th observation; represents the estimated value of the signal strength of the first link frequency band of the target UAV at the th observation; represents the estimated value of the signal strength of the second link frequency band of the target UAV at the th observation; S24. Extract the coordinate system from the updated state vector That is, when the signal strength is maximum, record ; ​ By the maximum signal strength value Calculate , and substitute it into the coordinate conversion formula to obtain the coordinates of the target UAV , where: ； ； In the formula, represents the signal wavelength, corresponding to the communication frequency band of the target UAV; represents the environment-related coefficient, reflecting the signal propagation loss; represents the directivity gain function of the planar antenna.

3. A method for monitoring drone intrusion according to claim 1, characterized in that, Step S3 specifically includes: S31. With the ground station as the center, establish a detection area coordinate system, and divide several sensitive areas in the detection area; S32. Judge whether the UAV is located within the sensitive area. If the current coordinates of the target UAV fall within the boundary of any sensitive area, record the start time and position information of the target UAV staying in this sensitive area; S33. Calculate the staying duration of the UAV in the sensitive area. If the staying time exceeds the preset time threshold, mark it as "long - time hovering in the sensitive area"; S34. Analyze the historical trajectory of the UAV, count the number of visits to the sensitive area. If the number of times of repeatedly entering the sensitive area within a short time exceeds the threshold, mark it as "repeated flight in the sensitive area"; S35. According to the determination results of the two types of behavioral characteristics of "long - time hovering in the sensitive area" and "repeated flight in the sensitive area", if the target UAV meets any condition, then mark this target UAV as an intrusive UAV.

4. A method for monitoring drone intrusion according to claim 1, characterized in that, The monitoring method further includes step S4: S4. Input the communication signal feature dataset containing modulation mode and pulse characteristics into the features of the pre-constructed UAV fingerprint database for comparison. When the similarity exceeds the set threshold, the model, brand, and identity information of the invasive UAV can be determined and identified, and corresponding countermeasure interference means can be taken; when the similarity is lower than the set threshold, the ground station detection personnel use a telescope or a zoom camera to observe the invasive UAV, and fill in the model, brand, and identity information of the invasive UAV into the UAV fingerprint database.

5. A method for monitoring drone intrusion according to claim 1, characterized in that, The monitoring method further includes step S5: S5. When an invasive UAV is identified, and lamps are arranged around the flat antenna, and the lamps flash at a preset frequency to interfere with the white balance of the camera of the invasive UAV.

6. The method for monitoring drone intrusion according to claim 1, characterized in that, The monitoring method further includes step S6: S6. When an invasive UAV is identified, and a loudspeaker is arranged around the flat antenna, and the loudspeaker emits a warning sound to warn the invasive UAV.

7. A method for monitoring drone intrusion according to claim 1, characterized in that The detection bands of the flat antenna are: 433 MHz, 900 MHz, 2400 MHz - 2476 MHz, 5725 MHz - 5829 MHz, 840.5 MHz - 845 MHz, 1430 MHz - 1444 MHz.

8. An unmanned aerial vehicle intrusion monitoring device, which is used to implement an unmanned aerial vehicle intrusion monitoring method as described in any one of claims 1 to 7, and is characterized in that, The UAV intrusion monitoring device includes: with the ground station as the center, several flat antennas are arranged in a surrounding and inclined manner, and the several flat antennas form an omnidirectional detection area; it also includes an RF analysis device connected to the flat antenna, and the RF analysis device is used to input the communication signal feature dataset containing modulation mode and pulse characteristics into the features of the pre-constructed UAV fingerprint database for comparison, so as to identify the model, brand, and identity information of the invasive UAV.

9. The drone intrusion monitoring device according to claim 8, wherein, One of the flat antennas is individually connected to one of the RF analysis devices, or, two of the flat antennas are connected to one RF analysis device, or, all of the flat antennas are connected to an antenna selector, and the antenna selector is connected to the RF analysis device.

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