UAV pilot positioning method and system based on radar dynamic track overlap inversion

Abstract

The present invention relates to the field of drone detection and countermeasure technology, and in particular to a drone pilot positioning method and system based on radar dynamic track overlap inversion. The method provided by the present invention overcomes the technical difficulty that traditional electromagnetic detection technology cannot effectively detect and locate pilots when facing illegal drones such as radio silence and non-standard frequency band transmission. Through radar detection multi-track data analysis, the control source position of the drone is deduced, and the real-time position data of the pilot is provided to the law enforcement department. Secondly, based on the radar multi-track data, the spatial overlap characteristics of the drone flight trajectory can be analyzed in real time to deduce the pilot position, which not only fills the technical gap in the field of "illegal" pilot positioning, but also provides a new solution for low-altitude airspace safety supervision, which is of great significance to ensuring the safety of key areas of the country and public safety.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) detection and countermeasure technology, and in particular to a method and system for positioning an unmanned aerial vehicle (UAV) pilot based on radar dynamic track overlap inversion. Background Art

[0002] In recent years, with the rapid development of drone technology and the prosperity of the low-altitude economy, the application scenarios and number of drones have increased rapidly. However, the accompanying "black flying" phenomenon, that is, illegal flying without registration or permission, is posing a serious challenge to social security and public order.

[0003] What is more complicated is that many "illegal" drones use assembly, fiber optic control or radio silence technology, and do not emit or do not emit electromagnetic signals in the specified frequency band, making it difficult for conventional electromagnetic detection methods to effectively detect and track the control source of these drones, that is, the location of the pilot. This technical feature greatly increases the difficulty of investigating and punishing "illegal" pilots, and creates major obstacles for law enforcement agencies to collect evidence, track and crackdown. If "illegal" pilots cannot be accurately located and investigated in a timely manner, it will inevitably lead to the continued rampant "illegal" behavior and even cause serious public safety incidents.

[0004] Existing drone detection and countermeasures are mainly focused on the drone itself, usually using a "radar + optoelectronic + electronic reconnaissance" detection configuration, as well as electronic jamming, navigation deception, net capture, laser strikes and other countermeasures. Although these methods can deal with illegal drone activities to a certain extent, they have not effectively solved the problem of accurately locating and investigating "illegal" pilots. Due to the low cost of drones, pilots can easily replace equipment and continue illegal flights. It is difficult to fundamentally curb "illegal flying" by only attacking the drone itself. Summary of the invention

[0005] The purpose of the present invention is to provide a UAV pilot positioning method and system based on radar dynamic track overlapping inversion to solve the problems existing in the prior art.

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

[0007] In a first aspect, the present invention provides a method for positioning a UAV pilot based on radar dynamic track overlap inversion, comprising:

[0008] Acquire echo signals from multiple target drones in the detection airspace in real time, and obtain several track data in the detection airspace based on the echo information;

[0009] Based on several track data, the track motion characteristic data is fitted by fitting algorithms to obtain the theoretical curve corresponding to the track. By analyzing the track model, signal interference and data inconsistency are eliminated, and dynamic updates are performed using filtering algorithms.

[0010] Based on several track data, the intersection of multiple tracks is calculated by the intersection positioning algorithm. When the flight trajectory of the UAV aircraft is a straight line or a fitting curve, the position of the UAV pilot is located by calculating the track intersection;

[0011] The obtained pilot position is compared with the map database, the positioning information is output, and an early warning is issued through the alarm system.

[0012] Preferably, the fitting process of the track motion characteristic data by a fitting algorithm includes:

[0013] Mathematically model the UAV motion state vector and optimize the track data through Kalman filter;

[0014] Obtain optimized track data, perform polynomial fitting on several track data, and output the fitting results.

[0015] Preferably, the mathematical modeling of the UAV motion state vector includes:

[0016]

[0017] in, express Moment The motion state vector of the track, including the position coordinates in Cartesian coordinates and velocity components ;

[0018] Also includes the flight dynamics functions of the drone:

[0019]

[0020] in, is the state transfer matrix, is the control input, is the input matrix.

[0021] Preferably, the optimizing the track data by using a Kalman filter comprises:

[0022]

[0023] in, represents the transpose operation, for The moment state prediction vector, for The moment state prediction covariance matrix, for The state update vector at each moment, express The state updates the covariance matrix at each moment. is the process noise covariance matrix;

[0024] When the observation is updated, the measured value is used To correct the status:

[0025]

[0026] in, represents the inverse operation, is the observation transfer matrix, is the measurement noise covariance matrix, is the Kalman gain, express The state vector update value at the moment, express The updated value of the state covariance at the moment;

[0027] The track set after track estimation is expressed as ,in , It is the track component, that is, point track information.

[0028] Preferably, performing polynomial fitting on a plurality of track data includes:

[0029]

[0030] in, Indicates approaching the minimum operation, Indicates the total number of tracks at the current moment. represents the polynomial fitting order, For the track time interval, Respectively Track fitting coefficients, , is the polynomial fitting result, For each track set received by the radar, for The track of the stage time interval, for The track of the stage time interval, for The track of the stage time interval, for The track of the stage time interval.

[0031] Preferably, the calculating the intersection points of the multiple tracks by using the intersection positioning algorithm comprises:

[0032] Determine the pilot's position by finding the intersection of multiple tracks;

[0033] Establish an intersection solution model, obtain the trajectory intersection time by solving the intersection solution model, and obtain the intersection coordinates through the trajectory equation based on the trajectory intersection time;

[0034] Check whether the starting point and the end point may overlap. If the starting point and the end point of a track have no intersection with the time starting point and the end point of other tracks, remove the track in the intersection solution model.

[0035] Preferably, the establishing of the intersection solution model includes:

[0036]

[0037] In the formula, is the intersection point, They are The three-axis coordinates of the intersection point, The independent variable is the time The trajectory expression of is the mth order coefficient, is the m-1th order coefficient, is the 0th order coefficient, For time.

[0038] Preferably, comparing the obtained pilot position with a map database comprises:

[0039] Associating the obtained pilot position with the map database and extracting the environmental information in the map database;

[0040] Based on the needs of drone control, unreasonable areas are eliminated in combination with geographic information;

[0041] The obtained pilot position is constrained by using terrain characteristics to narrow the range of the pilot position.

[0042] Preferably, the output positioning information includes:

[0043] The drone pilot positioning method is used to obtain a plurality of pilot positions, and a weighted average method is used to obtain a final position estimate for the plurality of pilot positions;

[0044]

[0045] In the formula, is the final position estimate, For the The calculation weight of each pilot position, For the pilot position.

[0046] In a second aspect, the present invention provides a UAV pilot positioning system based on radar dynamic track overlap inversion, comprising:

[0047] The track acquisition module is configured to acquire echo signals from multiple target drones in the detection airspace in real time, and acquire several track data in the detection airspace based on the echo information; based on several track data, the track motion characteristic data is fitted by a fitting algorithm to obtain a theoretical curve corresponding to the track, and the signal interference and data inconsistency are eliminated by analyzing the track model, and the filtering algorithm is used for dynamic update;

[0048] The track processing module is configured to calculate the intersection of multiple tracks based on the data of several tracks by using the intersection positioning algorithm. When the flight track of the UAV aircraft is a straight line or a fitting curve, the position of the pilot of the UAV is located by calculating the track intersection;

[0049] The positioning output module is configured to compare the obtained pilot position with the map database, output positioning information, and issue an early warning through the alarm system;

[0050] The main control module is connected to the track acquisition module, the track processing module and the positioning output module, and is used to execute the above-mentioned UAV pilot positioning method based on radar dynamic track overlapping inversion.

[0051] The technical solution of the present invention has at least the following advantages and beneficial effects:

[0052] The present invention proposes a technical route based on radar dynamic track overlap inversion, which uses radar to collaboratively collect multiple flight track information of drones over a period of time, combined with a dynamic track overlap analysis algorithm, to infer the ground position of the pilot. Compared with traditional technologies, it breaks through the limitations of traditional electromagnetic detection and effectively locates illegal drone pilots.

[0053] The present invention overcomes the technical difficulty that traditional electromagnetic detection technology cannot effectively detect and locate pilots when facing illegal UAVs such as radio silence and non-standard frequency band transmission. Through radar detection multi-track data analysis, the control source position of the UAV is deduced, and the real-time position data of the pilot is provided to the law enforcement department. Secondly, based on the radar multi-track data, the spatial overlapping characteristics of the UAV flight trajectory can be analyzed in real time to deduce the pilot's position, which not only fills the technical gap in the field of "illegal" pilot positioning, but also provides a new solution for low-altitude airspace safety supervision, which is of great significance to ensuring the safety of key areas of the country and public safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0055] Figure 1 It is the overall flow chart of the present invention;

[0056] Figure 2 The figure is a flowchart of pilot positioning of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, 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 in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0058] The modules or submodules described independently may be physically separated or not: they may be implemented by software or hardware, and some modules or submodules may be implemented by software, and the processor may call the software to implement the functions of these modules or submodules, and other modules or submodules may be implemented by hardware, such as by hardware circuits. In addition, some or all of the modules may be selected according to actual needs to achieve the purpose of the present application.

[0059] Please refer to Figure 1 The present invention provides a method for positioning a UAV pilot based on radar dynamic track overlap inversion, comprising:

[0060] S101: Acquire echo signals from multiple target drones in the detection airspace in real time, and acquire several track data in the detection airspace based on the echo information;

[0061] In the preset warning area, radar detection equipment is deployed to receive echo signals from multiple target drones in the detection airspace in real time, and obtain multiple track data in the detection airspace based on the echo information. Each track data includes the flight altitude, speed, azimuth, heading and other information of the drone.

[0062] Signal processing operations include but are not limited to digital down-conversion, pulse compression, digital beamforming, static clutter cancellation, target detection, and target angle measurement operations. The above signal processing operations are all common processing algorithms in radar signal processing and are not described in detail in this patent.

[0063] S102: Based on a plurality of track data, the track motion characteristic data are fitted by a fitting algorithm to obtain a theoretical curve corresponding to the track, and the track model is analyzed to eliminate signal interference and data inconsistency, and a filtering algorithm is used for dynamic update;

[0064] Radar data processing operations include but are not limited to outlier removal, track initiation, track association, and other operations.

[0065] S103: Based on the plurality of track data, the intersection of the plurality of tracks is calculated by an intersection positioning algorithm. When the flight track of the UAV aircraft is a straight line or a fitting curve, the position of the pilot of the UAV is located by calculating the track intersection;

[0066] S104: The obtained pilot position is compared with the map database, the positioning information is output, and an early warning is issued through the alarm system.

[0067] Based on the above process, in actual application, the following steps are included:

[0068] Radar, optoelectronics, and spectrum multi-means drone detection, obtain drone tracks, determine whether the drone is a whitelisted aircraft, or whether it is in free airspace that does not require "flight reporting". If so, it is considered a legal flight and the flight track is continuously monitored to determine whether it has crossed the boundary or exceeded the reporting limit;

[0069] If not, it is judged as illegal "black flying" and the threat ranking is carried out to determine whether the spectrum detection can detect the "black flying" drone and the pilot's position. If so, the drone and pilot's position are directly output. If not, the solution provided by this solution is used to calculate the pilot's position according to the drone trajectory and push it.

[0070] In an exemplary embodiment of the present invention, the fitting process of the track motion characteristic data by a fitting algorithm includes:

[0071] Mathematical modeling is performed on the UAV motion state vector, and the track data is optimized through the Kalman filter; the optimized track data is obtained, polynomial fitting is performed on several track data, and the fitting results are output.

[0072] Specifically, the mathematical modeling of the UAV motion state vector includes:

[0073]

[0074] in, express Moment The motion state vector of the track, including the position coordinates in Cartesian coordinates and velocity components ;

[0075] Also includes the flight dynamics functions of the drone:

[0076]

[0077] in, is the state transfer matrix, is the control input, is the input matrix.

[0078] An exemplary embodiment of the present invention uses filtering to optimize the track data in order to enhance the accuracy of multi-track fitting. The filtering algorithm includes Kalman filtering, extended Kalman filtering, insensitive Kalman filtering, etc. The present invention selects but is not limited to the Kalman filtering algorithm. Optimizing the track data by using the Kalman filter includes:

[0079]

[0080] in, represents the transpose operation, for The moment state prediction vector, for The moment state prediction covariance matrix, for The state update vector at each moment, express The state updates the covariance matrix at each moment. is the process noise covariance matrix;

[0081] When the observation is updated, the measured value is used To correct the status:

[0082]

[0083] in, represents the inverse operation, is the observation transfer matrix, is the measurement noise covariance matrix, is the Kalman gain, express The state vector update value at the moment, express The moment state covariance update value can effectively reduce error accumulation and improve position estimation accuracy by updating the error covariance matrix.

[0084] The track set after track estimation is expressed as ,in , It is the track component, that is, point track information.

[0085] In an exemplary embodiment of the present invention, in order to fit the motion characteristics of multiple tracks, the present invention adopts but is not limited to the least square method to fit the track data. Each track set received by the radar is , at the time When the least square method is used to perform track fitting, the polynomial fitting of the plurality of track data includes:

[0086]

[0087] in, Indicates approaching the minimum operation, Indicates the total number of tracks at the current moment. represents the polynomial fitting order, For the track time interval, Respectively Track fitting coefficients, , is the polynomial fitting result, For each track set received by the radar, for The track of the stage time interval, for The track of the stage time interval, for The track of the stage time interval, for The track of the stage time interval.

[0088] In this example, in order to reduce the complexity of engineering implementation and prevent overfitting, the parameters are selected but not limited to setting , then each track can be expressed as a cubic function curve. Using the least squares optimization method, the fitting trajectory coefficient of each UAV track can be obtained. .

[0089] In an exemplary embodiment of the present invention, the step of calculating the intersection of multiple tracks by using an intersection positioning algorithm includes:

[0090] The pilot's position is determined by the intersection of multiple tracks to find the intersection, and an intersection solution model is established. The intersection time of the tracks is obtained by solving the intersection solution model. The intersection coordinates are obtained through the track equation based on the intersection time. Check whether the starting point and the end point may overlap. If the starting point and the end point of a track have no intersection with the time starting point and the end point of other tracks, remove the track from the intersection solution model.

[0091] The present invention uses but is not limited to Gaussian elimination method to eliminate high-order variables and obtain the time The univariate polynomial, specifically, the intersection solution model includes:

[0092]

[0093] In the formula, is the intersection point, They are The three-axis coordinates of the intersection point, The independent variable is the time The trajectory expression of is the mth order coefficient, is the m-1th order coefficient, is the 0th order coefficient, For time.

[0094] In an exemplary embodiment of the present invention, the comparison of the obtained pilot position with the map database includes:

[0095] First, the estimated preliminary range of the pilot's location is associated with a high-precision map database to extract environmental information such as topography, building distribution, and road network in the map database. Secondly, based on the needs of drone control (such as locations with open sight lines and less signal interference), unreasonable areas are excluded in combination with geographic information (such as the interior of closed buildings, high mountain cliffs that pilots cannot reach, etc.). Finally, the estimated results are constrained by using terrain characteristics. For example, possible locations are mostly concentrated in open areas, areas with convenient transportation, or near certain buildings, thereby further narrowing the range of the pilot's location.

[0096] By introducing geographic information and signal source comparison into the pilot's positioning process, not only can unreasonable positioning results be eliminated, but the accuracy and credibility of the calculation can also be further improved in combination with the real environment, providing key support for law enforcement agencies to quickly identify offenders.

[0097] In an exemplary embodiment of the present invention, the position is estimated by the coincidence of the intersection points of multiple tracks, which can further improve the accuracy of the pilot's positioning. The output positioning information includes:

[0098] The drone pilot positioning method is used to obtain a plurality of pilot positions, and a weighted average method is used to obtain a final position estimate for the plurality of pilot positions;

[0099]

[0100] In the formula, is the final position estimate, For the The calculation weight of each pilot position, For the pilot position.

[0101] The obtained location information is converted into latitude, longitude and altitude information based on the radar base station information, and an early warning is issued through the alarm system.

[0102] The second method is a UAV pilot positioning system based on radar dynamic track overlap inversion, including:

[0103] The track acquisition module is configured to acquire echo signals from multiple target drones in the detection airspace in real time, and acquire several track data in the detection airspace based on the echo information; based on several track data, the track motion characteristic data is fitted by a fitting algorithm to obtain a theoretical curve corresponding to the track, and the signal interference and data inconsistency are eliminated by analyzing the track model, and the filtering algorithm is used for dynamic update;

[0104] The track processing module is configured to calculate the intersection of multiple tracks based on the data of several tracks by using the intersection positioning algorithm. When the flight track of the UAV aircraft is a straight line or a fitting curve, the position of the pilot of the UAV is located by calculating the track intersection;

[0105] The positioning output module is configured to compare the obtained pilot position with the map database, output positioning information, and issue an early warning through the alarm system;

[0106] The main control module is connected to the track acquisition module, the track processing module and the positioning output module, and is used to execute the above-mentioned UAV pilot positioning method based on radar dynamic track overlapping inversion.

[0107] The present invention solves the problem of accurate positioning of "illegal" pilots.

[0108] The present invention is based on the multi-target tracks detected by radar and realizes efficient inference of the pilot's position through a dynamic track overlapping inversion algorithm, thus overcoming the technical bottleneck that traditional electromagnetic detection methods are unable to locate radio silent "illegal flying" UAV pilots.

[0109] Improve the robustness and reliability of positioning algorithms.

[0110] By fitting all radar track data into a cubic polynomial curve and jointly solving the intersection point, a unified multi-target dynamic analysis model is formed. This method can effectively cope with complex flight scenarios and improve the robustness and adaptability of pilot positioning.

[0111] Combined with map database comparison, positioning accuracy can be improved.

[0112] The present invention effectively eliminates errors and corrects positioning results through dynamic comparison with a map database, thereby further improving the accuracy and practicality of pilot position estimation.

[0113] Adapt to a wide range of practical application scenarios.

[0114] The present invention can be used to investigate and punish pilots of various types of "illegal flying" of drones in the context of low-altitude economic development, and has important application value in many fields such as national security, security of major events, and protection of military facilities.

[0115] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0116] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0117] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A UAV pilot positioning method based on radar dynamic track overlap inversion, characterized in that: include: Acquire echo signals from multiple target drones in the detection airspace in real time, and obtain several track data in the detection airspace based on the echo information; Based on several track data, the track motion characteristic data is fitted by fitting algorithms to obtain the theoretical curve corresponding to the track. By analyzing the track model, signal interference and data inconsistency are eliminated, and dynamic updates are performed using filtering algorithms. Based on several track data, the intersection of multiple tracks is calculated by the intersection positioning algorithm. When the flight trajectory of the UAV aircraft is a straight line or a fitting curve, the position of the UAV pilot is located by calculating the track intersection; By comparing the obtained pilot position with the map database, the positioning information is output and an early warning is issued through the alarm system; The method of calculating the intersection points of multiple tracks by using an intersection positioning algorithm includes: Determine the pilot's position by finding the intersection of multiple tracks; Establish an intersection solution model, obtain the trajectory intersection time by solving the intersection solution model, and obtain the intersection coordinates through the trajectory equation based on the trajectory intersection time; Check whether the starting point and the end point may overlap. If the starting point and the end point of a track have no intersection with the time starting point and the end point of other tracks, remove the track from the intersection solution model. The establishment of the intersection solution model comprises: In the formula, is the intersection point, They are The three-axis coordinates of the intersection point, The independent variable is the time The trajectory expression of is the mth order coefficient, is the m-1th order coefficient, is the 0th order coefficient, For time.

2. The method for positioning UAV pilots based on radar dynamic track overlap inversion according to claim 1 is characterized in that: The fitting process of the track motion characteristic data by the fitting algorithm comprises: Mathematically model the UAV motion state vector and optimize the track data through Kalman filter; Obtain optimized track data, perform polynomial fitting on several track data, and output the fitting results.

3. The method for positioning UAV pilots based on radar dynamic track overlap inversion according to claim 2 is characterized in that: The mathematical modeling of the UAV motion state vector includes: in, express Moment The motion state vector of the track, including the position coordinates in Cartesian coordinates and velocity components ; Also includes the drone's flight dynamics functions: in, is the state transfer matrix, is the control input, is the input matrix.

4. The method for positioning UAV pilots based on radar dynamic track overlap inversion according to claim 3 is characterized in that: The optimization of the track data by using the Kalman filter includes: in, represents the transpose operation, for The moment state prediction vector, for The moment state prediction covariance matrix, for The state update vector at each moment, express The state updates the covariance matrix at each moment. is the process noise covariance matrix; When the observation is updated, the measured value is used To correct the status: in, represents the inverse operation, is the observation transfer matrix, is the measurement noise covariance matrix, is the Kalman gain, express The state vector update value at the moment, express The updated value of the state covariance at the moment; The track set after track estimation is expressed as ,in , It is the track component, that is, point track information.

5. The method for positioning UAV pilots based on radar dynamic track overlap inversion according to claim 4 is characterized in that: The polynomial fitting of the plurality of track data comprises: in, Indicates approaching the minimum operation, Indicates the total number of tracks at the current moment. represents the polynomial fitting order, For the track time interval, Respectively Track fitting coefficients, , is the polynomial fitting result, For each track set received by the radar, for The track of the stage time interval, for The track of the stage time interval, for The track of the stage time interval, for The track of the stage time interval.

6. The method for positioning UAV pilots based on radar dynamic track overlap inversion according to claim 1, characterized in that: The comparison of the obtained pilot position with the map database includes: Associating the obtained pilot position with the map database and extracting the environmental information in the map database; Based on the needs of drone control, unreasonable areas are eliminated in combination with geographic information; The obtained pilot position is constrained by using terrain characteristics to narrow the range of the pilot position.

7. The method for positioning UAV pilots based on radar dynamic track overlap inversion according to claim 6, characterized in that: The output positioning information includes: The drone pilot positioning method is used to obtain a plurality of pilot positions, and a weighted average method is used to obtain a final position estimate for the plurality of pilot positions; In the formula, is the final position estimate, For the The calculation weight of each pilot position, For the pilot position.

8. The UAV pilot positioning system based on radar dynamic track overlap inversion is characterized by: include: The track acquisition module is configured to acquire echo signals from multiple target drones in the detection airspace in real time, and acquire several track data in the detection airspace based on the echo information; based on several track data, the track motion characteristic data is fitted by a fitting algorithm to obtain a theoretical curve corresponding to the track, and the signal interference and data inconsistency are eliminated by analyzing the track model, and the filtering algorithm is used for dynamic update; The track processing module is configured to calculate the intersection of multiple tracks based on the data of several tracks by using the intersection positioning algorithm. When the flight track of the UAV aircraft is a straight line or a fitting curve, the position of the pilot of the UAV is located by calculating the track intersection; The positioning output module is configured to compare the obtained pilot position with the map database, output positioning information, and issue an early warning through the alarm system; The main control module is connected to the track acquisition module, the track processing module and the positioning output module, and is used to execute the UAV pilot positioning method based on radar dynamic track overlapping inversion as described in any one of claims 1-7.

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