Unmanned aerial vehicle-based radar array shooting measurement method, system, medium and device

Abstract

This invention belongs to the field of radar array imaging, and particularly relates to a method, system, medium, and equipment for radar array imaging and measurement based on unmanned aerial vehicles (UAVs). This solution utilizes UAVs for imaging, making it suitable for photogrammetric work on high and large radar arrays. It eliminates the need for auxiliary equipment such as aerial work platforms, reducing the risk of accidents during measurement. By automatically generating the optimal movement route for the UAV, work efficiency is significantly improved, operational difficulty is reduced, and the number of personnel required is decreased. Furthermore, the automation effectively reduces the uncertainty of manual operation, improves the quality of photographs taken during the imaging and measurement task, and thus enhances measurement accuracy. This enables automated measurement and calibration of large / ultra-large radar arrays, eliminating blind spots in existing methods.

Description

Technical Field

[0001] This invention belongs to the field of radar array imaging, and particularly relates to radar array imaging measurement methods, systems, media and equipment based on unmanned aerial vehicles (UAVs). Background Technology

[0002] When calibrating the pointing accuracy of radar, photogrammetry equipment is often used to measure the flatness of the radar array, and the data is used to compensate for the radar pointing. Photogrammetry is an optical measurement method, and the equipment includes a camera, reflectors, a reference ruler, and a data analysis system. Its basic principle is to arrange reflectors (including coded points and measurement points) and a reference ruler on the surface of the object being measured. The density and distribution of the reflectors are adjusted according to the size, shape, and key points of the object being measured. A calibrated camera is used to take pictures of the reflectors and the reference ruler from multiple angles. Finally, the pictures of various parts of the object being measured are stitched together to obtain the precise spatial position data of each measurement point.

[0003] When using photogrammetry equipment to measure and evaluate the deformation and pointing accuracy of radar arrays, it is necessary to take pictures of the array from different positions and angles, and obtain the deformation state of the array through image analysis. When the object being measured is large or tall, it is often necessary to use an aerial work platform to assist in the operation, and the work position needs to be moved frequently, which is somewhat dangerous, has a low degree of automation, is time-consuming and labor-intensive, and often cannot be carried out due to lack of working conditions. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method, system, medium and equipment for radar array imaging and measurement based on unmanned aerial vehicles (UAVs).

[0005] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A radar array imagery and measurement method based on an unmanned aerial vehicle (UAV), comprising:

[0006] Based on the radar's location and array attitude information, a list of UAV hovering points is generated. Using the Prim algorithm, the optimal movement route of the UAV for performing the shooting and measurement task is determined based on the UAV hovering point list. The array attitude information includes the radar's array azimuth angle and array elevation angle.

[0007] When the UAV takes pictures of the radar array at any hovering point, it determines whether there is an obstruction. If there is an obstruction, it issues an alarm and performs the shooting and measurement task at that hovering point or reconfirms the optimal movement route according to the operator's instructions. If there is no obstruction, it performs the shooting and measurement task at that hovering point and determines the position of the next hovering point based on the optimal movement route until the shooting and measurement tasks of all hovering points in the optimal movement route are completed.

[0008] The beneficial effects of this invention are as follows: By using drones for photography, it is applicable to photogrammetric work on high and large arrays, eliminating the need for auxiliary equipment such as aerial work platforms, thus reducing the danger of the measurement work. By automatically generating the optimal movement route for the drone, work efficiency can be significantly improved, the difficulty of the operation reduced, and the number of personnel required decreased. Furthermore, the automation scheme effectively reduces the uncertainty of manual operation, improves the quality of photographs taken during the photogrammetric task, and thus improves the measurement accuracy, enabling automated measurement and calibration of large / ultra-large arrays and eliminating blind spots in existing methods.

[0009] Another technical solution of the present invention to solve the above-mentioned technical problems is as follows: A radar array imaging and measurement system based on an unmanned aerial vehicle (UAV), comprising:

[0010] The determination module is used to: generate a list of UAV hovering points based on the location of the radar and the array attitude information; and determine the optimal movement route of the UAV for performing the shooting and measurement task based on the list of UAV hovering points using the Prim algorithm. The array attitude information includes: the radar array azimuth angle and the array elevation angle.

[0011] The shooting module is used to: when the UAV is shooting at the radar array at any hovering point, determine whether there is an obstruction. If there is an obstruction, issue an alarm and perform the shooting and measurement task at that hovering point or reconfirm the optimal movement route according to the operator's instructions. If there is no obstruction, perform the shooting and measurement task at that hovering point and determine the position of the next hovering point based on the optimal movement route, until the shooting and measurement tasks of all hovering points in the optimal movement route are completed.

[0012] The beneficial effects of this invention are as follows: By using drones for photography, it is applicable to photogrammetric work on high and large arrays, eliminating the need for auxiliary equipment such as aerial work platforms, thus reducing the danger of the measurement work. By automatically generating the optimal movement route for the drone, work efficiency can be significantly improved, the difficulty of the operation reduced, and the number of personnel required decreased. Furthermore, the automation scheme effectively reduces the uncertainty of manual operation, improves the quality of photographs taken during the photogrammetric task, and thus improves the measurement accuracy, enabling automated measurement and calibration of large / ultra-large arrays and eliminating blind spots in existing methods.

[0013] Another technical solution of the present invention to solve the above-mentioned technical problems is as follows: a storage medium storing instructions, wherein when a computer reads the instructions, the computer executes the method described in any of the above-mentioned methods.

[0014] The beneficial effects of this invention are as follows: By using drones for photography, it is applicable to photogrammetric work on high and large arrays, eliminating the need for auxiliary equipment such as aerial work platforms, thus reducing the danger of the measurement work. By automatically generating the optimal movement route for the drone, work efficiency can be significantly improved, the difficulty of the operation reduced, and the number of personnel required decreased. Furthermore, the automation scheme effectively reduces the uncertainty of manual operation, improves the quality of photographs taken during the photogrammetric task, and thus improves the measurement accuracy, enabling automated measurement and calibration of large / ultra-large arrays and eliminating blind spots in existing methods.

[0015] Another technical solution of the present invention to solve the above-mentioned technical problems is as follows: an electronic device, including the above-mentioned storage medium and a processor that executes the instructions in the above-mentioned storage medium.

[0016] The beneficial effects of this invention are as follows: By using drones for photography, it is applicable to photogrammetric work on high and large arrays, eliminating the need for auxiliary equipment such as aerial work platforms, thus reducing the danger of the measurement work. By automatically generating the optimal movement route for the drone, work efficiency can be significantly improved, the difficulty of the operation reduced, and the number of personnel required decreased. Furthermore, the automation scheme effectively reduces the uncertainty of manual operation, improves the quality of photographs taken during the photogrammetric task, and thus improves the measurement accuracy, enabling automated measurement and calibration of large / ultra-large arrays and eliminating blind spots in existing methods. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an embodiment of a radar array imaging and measurement method based on an unmanned aerial vehicle (UAV) according to the present invention.

[0018] Figure 2 This is a structural framework diagram of an embodiment of a radar array imaging and measurement system based on an unmanned aerial vehicle (UAV) according to the present invention.

[0019] Figure 3 This is a flowchart illustrating the single-UAV tracking mode workflow of an embodiment of a radar array imaging and measurement method based on a UAV according to the present invention.

[0020] Figure 4 This is a flowchart illustrating the multi-UAV collaborative mode workflow of an embodiment of a radar array imaging and measurement method based on UAVs according to the present invention.

[0021] Figure 5 This is a flowchart illustrating the generation of a UAV tracking mode motion path, provided in an embodiment of a radar array imaging and measurement method based on a UAV according to the present invention.

[0022] Figure 6This is a schematic diagram of the area on the radar array surface during oblique shooting, provided in an embodiment of a radar array imaging and measurement method based on an unmanned aerial vehicle (UAV) according to the present invention.

[0023] Figure 7 This is a geometric relationship diagram of the camera's oblique shooting image provided in an embodiment of a radar array imaging and measurement method based on an unmanned aerial vehicle (UAV) according to the present invention.

[0024] Figure 8 This is a schematic diagram illustrating that each point to be photographed needs to be photographed from four directions, as provided in an embodiment of a radar array imaging and measurement method based on an unmanned aerial vehicle (UAV) according to the present invention.

[0025] Figure 9 This is a schematic diagram of the movement path of a single UAV provided in an embodiment of a radar array imaging and measurement method based on a UAV according to the present invention;

[0026] Figure 10 This is a flowchart illustrating the multi-UAV cooperative mode operation route generation process provided in an embodiment of a radar array imaging and measurement method based on UAVs according to the present invention.

[0027] Figure 11 This is a schematic diagram showing the division of radar array images based on polar coordinates, provided in an embodiment of the radar array imagery and measurement method based on unmanned aerial vehicles (UAVs) of the present invention.

[0028] Figure 12 This is a schematic diagram showing the division of the radar array based on polar coordinates according to an embodiment of the radar array imaging and measurement method based on UAV of the present invention;

[0029] Figure 13 This is a schematic diagram of the multi-UAV system mode operation route provided in an embodiment of the radar array imaging and measurement method based on UAVs according to the present invention;

[0030] Figure 14 This is a schematic diagram of the angle adjustment logic of the UAV and servo gimbal provided in an embodiment of a radar array imaging and measurement method based on a UAV according to the present invention.

[0031] Figure 15 This is a schematic diagram of the photogrammetric task execution process provided in an embodiment of a radar array imaging and measurement method based on an unmanned aerial vehicle (UAV) according to the present invention.

[0032] Figure 16 This is a schematic diagram of the system working scenario provided by an embodiment of a radar array imaging and measurement method based on an unmanned aerial vehicle (UAV) according to the present invention.

[0033] Figure 17 This is a schematic diagram of a drone equipment provided for an embodiment of a radar array imaging and measurement method based on a drone according to the present invention.

[0034] The attached diagram lists the components represented by each number as follows:

[0035] 1. Subject point, 2. Hovering point, 3. Movement route, 4. Shooting direction, 5. Array surface, 6. Shooting area, 7. Target point, 8. Radar vehicle, 9. Data processing and control terminal, 10. RTK base station, 11. UAV, 12. Positioning satellite, 13. RTK positioning module, 14. Inertial navigation module, 15. Servo gimbal, 16. Camera. Detailed Implementation

[0036] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0037] like Figure 1 As shown, a radar array 5 imaging and measurement method based on a UAV 11 includes:

[0038] A list of hovering points 2 for UAV 11 is generated based on radar coordinates and the attitude information of array 5. The optimal motion route 3 is determined in the list of hovering points 2 of UAV 11 using the Prim algorithm. The attitude information of array 5 includes: the azimuth angle and the elevation angle of array 5 of the radar.

[0039] Determine whether there is an obstruction in the shooting area 6 at any hovering point 2. If there is an obstruction, issue an alarm and perform the shooting measurement task at hovering point 2 according to the operator's instructions or reconfirm the optimal movement route 3. If there is no obstruction, perform the shooting measurement task at hovering point 2 and determine the position of the next hovering point 2 based on the optimal movement route until the shooting measurement tasks of all hovering points 2 in the optimal movement route are completed. The set of shooting areas 6 corresponding to all shooting measurement tasks is the shooting area of ​​the radar array 5.

[0040] In some possible implementations, using a drone 11 for photography can be applied to photogrammetric work on high and large array surfaces 5, eliminating the need for auxiliary equipment such as aerial work platforms, thus reducing the danger of the measurement work. By automatically generating the optimal movement route 3 for the drone 11, work efficiency can be significantly improved, the difficulty of the operation can be reduced, and the number of personnel required can be decreased. In addition, automation can effectively reduce the uncertainty of manual operation, improve the quality of photographs taken in the photogrammetric task, and thus improve the measurement accuracy, realizing automated measurement and calibration of large / ultra-large array surfaces 5, and eliminating the blind spots of existing methods.

[0041] It should be noted that the radar array azimuth angle refers to the angle between the projection of the array normal onto the ground and north (it doesn't necessarily have to be north, it can also be east). The elevation angle is the angle between the array plane and the ground (it also doesn't necessarily have to be the ground, it can also be the vertical plane of the ground). In short, for a radar with its elevation axis parallel to the ground, these two angles determine the array attitude. The process of generating a list of hovering points 2 for UAV 11 based on the radar coordinates and array attitude information, and determining the optimal movement route 3 from this list using the Prim algorithm, can be divided into two cases: one when there is only one UAV 11, and the other when there are multiple UAVs 11.

[0042] 1. When the number of drones is single.

[0043] like Figure 5 As shown, the shooting angle is determined according to the list of shooting points 1 on the array 5. The list of hovering points 2 of UAV 11 is determined according to the list of shooting points 1, shooting angle, radar coordinates, azimuth of array 5 and pitch angle of array 5. The list of hovering points 2 of UAV 11 is combined with the initial coordinates of UAV 11. The optimal movement route 3 of UAV 11 can be obtained by Prim algorithm.

[0044] In summary, based on the geometric dimensions and azimuth / elevation information of array 5, a list of photographed points 1 on array 5 can be initialized. Operators then adjust the distribution of photographed points 1 according to the array 5's skeletal structure and the surrounding space. Combining this with the required density of photographed points 1 for the photogrammetric algorithm, a final list of photographed points 1 on array 5, aligned with the lens of camera 16, is generated. For each photographed point 1, camera 16 needs to capture images from different directions. By integrating all photographed point 1 information, a list of hovering points 2 for UAV 11 is obtained. Finally, optimizing the motion path 3 for these hovering points 2 yields the motion path 3 for UAV 11.

[0045] 2. When there are multiple drones (11).

[0046] like Figure 10 As shown, the shooting angle is determined according to the list of shooting points 1 on the array 5. The list of hovering points 2 of UAV 11 is determined according to the list of shooting points 1, shooting angle, radar coordinates, azimuth of array 5 and elevation angle of array 5. The hovering points 2 in the list of hovering points 2 are grouped into regions, with each group corresponding to one UAV 11. The optimal motion route 3 of UAV 11 in each group is calculated using the Prim algorithm.

[0047] In summary, the shooting area 6 of camera 16 on array 5 is determined based on the field of view of camera 16 and the positional relationship between camera 16 and the shooting point 1. Combined with the geometric dimensions of array 5, a list of shooting points 1 on array 5 that are pointed at by the lens of camera 16 is finally generated. For each shooting point 1, camera 16 needs to shoot from different directions. By combining the information of all shooting points 1, a list of hovering points 2 of UAV 11 is obtained. Finally, these hovering points 2 are grouped according to spatial regions and assigned to UAV 11. For each hovering point 2 within a region, the motion path 3 is optimized to obtain the corresponding motion path 3 of UAV 11.

[0048] In the above embodiment, the list of shooting points 1 is determined by determining the number of shooting points 1. The number of shooting points 1 is determined by the density of shooting points 1. That is, firstly, the imaging field of view size of the camera 16 on each drone 11 is determined. Based on the imaging field of view size, the shooting area when the camera 16 shoots at an angle can be determined. Secondly, the number of shooting points 1 is determined by combining the shooting area and the pre-set constraints of the density of shooting points 1.

[0049] 1. Determine the imaging field of view of the camera 16 on each drone 11. Based on the imaging field of view, the process of determining the shooting area when the camera 16 is shooting at an angle is as follows:

[0050] Let the focal length of the lens of camera 16 be f, the size of the imaging sensor of camera 16 be h×v, where h is the horizontal length of the imaging sensor of camera 16, v is the vertical length of the imaging sensor of camera 16, and the shooting distance be L. Then the horizontal length H and vertical length V of the field of view captured by camera 16 are:

[0051]

[0052]

[0053] like Figure 6 as well as Figure 7 As shown, Figure 6 In this scenario, because the surface being photographed is not perpendicular to the shooting direction 4 of camera 16, the area captured in the frame changes. The circular area is the region that can be captured when photographed from different directions at the same angle. It is equivalent to the envelope of the images taken from different directions. The center of this circular area is defined as the photographed point 1. According to the rules, each photographed point 1 should be photographed from four directions, and the shooting position corresponding to each direction is the hovering point 2. That is, one photographed point 1 corresponds to four hovering points 2. The photographed areas 6 presented in the four directions are four congruent trapezoids. V1 represents the top side of the trapezoid, V2 represents the bottom side of the trapezoid, x represents the distance from the center to the top side, and y represents the distance from the center to the bottom side. Figure 7In this diagram, α is half the field of view. Note that this angle is determined by the camera's focal length and the size of the camera sensor. θ represents the angle of view of camera 16. The vertical line on the left represents camera 16 sensor. The line of the same thickness parallel to this vertical line represents the shooting area 6 when perpendicular. The line intersecting the shooting area 6 when perpendicular at an angle of θ represents the shooting area 6 when the included angle is θ. Figure 6 as well as Figure 7 The geometric relationships in the middle can be determined as follows:

[0054]

[0055]

[0056]

[0057]

[0058] Compare R and x, where R is the radius of the circular region, and S... 圆 S represents the area of ​​the circular region. 方 Let x be the area of ​​the trapezoidal region. If R > x, then:

[0059] S 圆 =πR 2

[0060]

[0061] If R≤x, then:

[0062] S 圆 =πx 2

[0063] S 方 =4x 2

[0064] Based on the shooting angle, the imaging field of view of the camera 16 on the drone 11 can be determined according to the above process.

[0065] 2. The process of determining the number of shooting points 1, based on the area to be photographed and the pre-set density constraints of shooting point 1, is as follows:

[0066] The photographed area refers to the area comprised of all photographed points 1;

[0067] The constraints are: 1 ≤ p < y / x, where P is the density set of the photographed point 1;

[0068] Suppose that the points to be photographed are arranged in a rectangular array on the surface to be photographed, the number of points to be photographed is n, and the area of ​​the surface to be photographed is S. 面 Then take p = S 面 / (nS 方) represents the density of shooting locations, where S 方 The calculation is determined according to the above formula based on the preset shooting angle. It is required that the value of p falls within the range of 1 ≤ p < y / x, thus ensuring that every point on array 5 is captured.

[0069] Once the number of shooting points 1 is determined, a list of shooting points 1 can be generated by manually arranging them. The list of shooting points 1 provides the location information of each shooting point 1 and the shooting angle of each shooting point 1.

[0070] The process of determining the list of hovering points 2 for UAV 11 based on the list of photographed points 1, the shooting angle, the radar coordinates, the azimuth of array 5, and the elevation angle of array 5 is as follows:

[0071] like Figure 8 As shown, P1, P2, P3, and P4 represent hovering points 2 corresponding to any one of the photographed points 1. θ1, θ2, θ3, and θ4 represent the shooting angles corresponding to the four hovering points 2, P1, P2, P3, and P4, respectively.

[0072] For each photographed point 1, it is generally photographed from four directions, thus each photographed point 1 corresponds to four drone hovering points 2. Let the coordinates of photographed point 1 (in the array plane coordinate system) be (x, yz), and the shooting distance be L, then the coordinates of each hovering point 2 are:

[0073] P1(x,y-Lcosθ1,z+Lsinθ1)

[0074] P2(x+Lcosθ2,y,z+Lsinθ2)

[0075] P3(x,y+Lcosθ3,z+Lsinθ3)

[0076] P4(x-Lcosθ4,y,z+Lsinθ4)

[0077] The coordinates of hovering point 2 obtained above are in the coordinate system of array face 5. They need to be converted to the northeast-sky coordinate system. Let the azimuth of array face 5 be A. fw (The angle between the projection of the normal of array 5 onto the ground and the east-northeast direction), the elevation angle of array 5 is E. fy (The angle between the normal of array face 5 and the direction of the plumb line), then the transformation matrix from the array face 5 coordinate system to the northeast-sky coordinate system is:

[0078]

[0079]

[0080] In the above formula, [x0,y0,z0] are the coordinates of the photographed point 1 on array 5 in the northeast celestial coordinate system, [x阵 ,y 阵 ,z 阵 [] is the coordinate of the origin of the array 5 coordinate system in the northeast celestial coordinate system.

[0081] The calculation of the coordinates of the origin of the array 5 coordinate system is related to the radar structure size and form, and is affected by the azimuth axis position, elevation axis position, and array 5 attitude. It needs to be calculated according to the specific model of the radar structure.

[0082] After obtaining all hovering points 2 of UAV 11, the coordinates of each point are analyzed and compared. Points with similar positions can be merged, and their average coordinates are calculated as follows:

[0083]

[0084] It should be noted that the points with similar locations mentioned here can be compared using a preset value. That is, if the distance between two points is less than the preset value, then these two points can be merged. After filtering and merging all hovering points 2, a list of hovering points 2 for drone 11 is obtained.

[0085] The process of combining the list of hovering points 2 of UAV 11 with the initial coordinates of UAV 11 and calculating the optimal motion path 3 of UAV 11 using the Prim algorithm is as follows:

[0086] like Figure 9 As shown, D is the starting point of the optimal running path, and d is the ending point of the optimal running path. Given n hovering points 2 of UAV 11 in space, find a shortest path to traverse these points. This is the minimum spanning tree problem solved using Prim's algorithm. Note that Prim's algorithm requires the initial coordinates of UAV 11.

[0087] The hovering points 2 in the list of hovering points 2 are grouped into regions, with each group corresponding to a UAV 11. The process of calculating the optimal motion route 3 of UAV 11 in each group using the Prim algorithm is as follows:

[0088] The area grouping of hover point 2 is as follows Figure 11 as well as Figure 12 As shown, after generating the list of hovering points 2 for UAV 11, they are grouped according to their spatial positional relationships. The grouping is based on the coordinate system of array surface 5, and there are several grouping methods:

[0089] 1. Divide by angle range. Project the hovering point 2 of UAV 11 onto the array surface 5, and group them according to the area where the projection point is located. For example... Figure 11 As shown, the hovering point 2 of UAV 11 is grouped according to the polar angle; each region is captured and measured by one UAV 11, as shown in the figure. Figure 13As shown, points A, B, and C are the starting points of the three UAVs 11 responsible for the shooting and measurement tasks in the three areas, while a, b, and c are the ending points of the three UAVs 11 responsible for the shooting and measurement tasks in the three areas.

[0090] 2. Divide into inner and outer rings. Similarly, project the hovering point 2 of UAV 11 onto array surface 5, and group them according to the distance of the projected point from the center of array surface 5. For example... Figure 12 As shown, hovering point 2 of UAV 11 is grouped according to the polar radius region;

[0091] 3. Manual editing of groups. These groups can be processed according to actual needs; however, due to the variability in grouping scenarios, details will not be elaborated here.

[0092] The process of determining whether there are obstructions in the shooting area 6 at any hovering point 2, and issuing an alarm if obstructions are present, and then performing the shooting and measurement task at hovering point 2 or reconfirming the optimal motion route 3 according to the operator's instructions, is as follows:

[0093] 1. When a single drone (11) is performing shooting and measurement:

[0094] like Figure 3 As shown, after determining the optimal movement route 3 for the drone 11, the drone 11 performs the shooting and measurement task from the starting point according to the hovering points 2 in the optimal movement route 3. After the shooting and measurement task at the current hovering point 2 is completed, when the drone 11 moves to the next hovering point 2, it checks whether there are large obstacles on the path. If a large obstacle is found, an alarm is issued and the position information of the next hovering point 2 is adjusted. The adjusted position information and alarm information are sent to the operator for manual confirmation. If the operator confirms that the adjusted position information is used for shooting and measurement, the adjusted position information replaces the original position information of the next hovering point 2. It should be noted that only the position information is adjusted; other task content such as shooting angles remains unchanged. If the operator does not agree to use the adjusted position information for shooting and measurement, the original position information of the next hovering point 2 is used for subsequent shooting and measurement processing.

[0095] The location information in the above process is obtained through the RTK positioning module 13. The information obtained by the RTK positioning module 13 is generally longitude, latitude and elevation data in the WGS84 coordinate system, which needs to be converted to the ENU (Northeast Sky) coordinate system with the radar station site as the coordinate origin.

[0096] First, convert latitude, longitude, and altitude to the Earth-centered, Earth-fixed coordinate system:

[0097]

[0098]

[0099] In the above formula: B is longitude; L is latitude; H is elevation; a is the radius of the Earth's semi-major axis; b is the radius of the Earth's semi-minor axis; e is the first eccentricity of the ellipsoid; N is the radius of the zonal circle; the coordinates of point P (UAV 11) are [X,Y,Z].

[0100] Then transform from the Earth-centered Earth-fixed coordinate system to the ENU (North-East-South) coordinate system with the station site as the origin:

[0101]

[0102]

[0103]

[0104] In the formula: [X,Y,Z] are the coordinates of UAV 11 in the geocentric coordinate system, [x0,y0,z0] are the coordinates of the radar site in the geocentric coordinate system, and [e,n,u] are the coordinates of UAV 11 in the ENU coordinate system.

[0105] RTK positioning data at any point can be converted into coordinates in the ENU coordinate system with the radar station site as the origin, as described above.

[0106] During the process, after the drone 11 detects an obstacle during its movement, it determines whether each hovering point 2 is affected based on the obstacle's distance and size information, combined with the coordinates of the hovering points 2. If the obstacle may interfere with the drone 11, or if the obstacle obstructs the view of the drone 11's camera 16, the operator is notified for confirmation, and the obstacle is removed or the coordinate information of the affected hovering points 2 is modified.

[0107] 2. When multiple drones (11) are conducting shooting and measurement:

[0108] After determining the optimal movement route 3 for each UAV 11, the optimal movement route 3 for each UAV 11 needs to be sent to the control center. The control center will then determine whether there is any interference between UAVs 11 within the same time period. This interference refers to interference with the flight path and interference with the execution of the shooting and measurement tasks, including shooting interference or signal interference. If interference exists, the control center needs to rearrange the matching order of UAVs 11 or modify the optimal movement route 3 of the UAVs 11. If there is no interference, the UAV 11 will perform shooting and measurement tasks starting from the starting point according to the hovering point 2 sequence in the optimal movement route 3. After the shooting and measurement task at the current hovering point 2 is completed, when the UAV 11 moves to the next hovering point 2, it will check whether there are any large obstacles on the path. If a large obstacle is found, an alarm will be issued and the position information of the next hovering point 2 will be adjusted. The adjusted position information and alarm information will be sent to the operator for manual confirmation. If the operator confirms that the adjusted position information is used for shooting and measurement, the adjusted position information will replace the original position information of the next hovering point 2. It should be noted that only the position information is adjusted; other task content such as shooting angles remains unchanged. If the operator does not agree to use the adjusted location information for shooting and measurement, the original location information of the next hovering point 2 will continue to be used for subsequent shooting and measurement processing.

[0109] Whether performing a single or multiple drones 11 for photography and measurement tasks, the photography must be processed according to the following requirements: Each measurement point needs 2 visible angles (preferably 4); the intersection angle of the various shooting angles at each measurement point should be between 60-120°, with a maximum limit of 30-150°; each image should contain at least 4 coded points (not collinear); each image should contain at least 12 measurement points. These four points are just examples and do not represent the only possible requirements; the requirements can be adjusted according to actual needs.

[0110] like Figure 15 as well as Figure 14 As shown, the photogrammetry task execution flow is as follows: After the movement route 3 of the UAV 11 is planned, photogrammetry work can be carried out. To ensure the camera 16 lens is aligned with the shooting point, the attitude of the UAV 11 and the servo gimbal 15 need to be adjusted respectively. Specifically, the UAV 11 moves to the predetermined hovering point 2, according to... Figure 14In the upper-middle scheme, the heading of the drone 11 is adjusted to align with the ground projection of the shooting direction 4. This adjustment process is performed as the drone 11 moves from the previous hovering point 2 to the next hovering point 2. During this process, the pitch and roll angles of the drone 11 must be kept stable near zero. When the drone 11 reaches the predetermined hovering point 2, the alignment process is complete. The purpose of the alignment process is to adjust the drone 11 to the preset shooting angle. After alignment, the servo gimbal 15 needs to be adjusted so that the camera 16 on the drone 11 is aligned with the shooting point 1. This adjustment process involves adjusting the azimuth, pitch, and roll angles of the servo gimbal 15. This process can be performed simultaneously with the attitude adjustment of the drone 11, i.e., the alignment process mentioned above, or the camera 16 adjustment can begin after the alignment process is completed. The purpose of adjusting the camera 16 is to align the camera lens with the shooting point 1 or switch between horizontal and vertical shooting. After the camera 16 is adjusted, it can begin shooting according to the shooting and measurement task of the hovering point 2. After completing the shooting, check if it is necessary to shoot other points 1 at hover point 2. If not, end the shooting at hover point 2 and move to the next hover point 2. If so, you need to perform alignment processing again.

[0111] If there are no obstructions, the shooting and measurement task for hovering point 2 is performed, and the position of the next hovering point 2 is determined based on the optimal running route, until the shooting and measurement task for all hovering points 2 in the optimal running route is completed.

[0112] like Figure 3 as well as Figure 4 As shown, if there are no large obstacles, the aircraft flies to the next hovering point 2 to perform the shooting and measurement task. After execution, it determines whether there is another hovering point 2 based on the optimal motion route 3. If there is another hovering point 2, it moves to the next hovering point 2 and continues to perform the shooting and measurement task corresponding to the next hovering point 2 until all the shooting and measurement tasks corresponding to hovering points 2 in the optimal route are completed. After all the shooting and measurement tasks corresponding to hovering points 2 are completed, it checks whether all the captured images meet the requirements. If they do not meet the requirements, the optimal route needs to be re-determined through manual processing. If they meet the requirements, it determines whether it is necessary to measure other attitudes of the radar. If so, it is necessary to obtain new attitude information of the array 5 and determine the optimal running path based on the new attitude information of the array 5. The determination of the optimal running path is the same as above and will not be repeated here. If it is not necessary to measure other attitudes of the radar, the shooting and measurement task ends.

[0113] Preferably, in any of the above embodiments, before generating the list of hovering points 2 of the UAV 11 based on radar coordinates and array 5 attitude information, the method further includes determining the number of points to be photographed. The process of determining the number of points to be photographed is as follows:

[0114] Calculate the imaging field of view of the camera 16 carried on the UAV 11, and determine the shooting area when the camera 16 is shooting at an angle based on the imaging field of view. Determine the number of shooting points based on the constraints of the shooting area, the shooting area, and the density of shooting points.

[0115] Preferably, in any of the above embodiments, when there is only one UAV 11 performing the shooting and measurement task, the process of generating the list of hovering points 2 of UAV 11 based on radar coordinates and the attitude information of array 5 is as follows:

[0116] The list of photographed points 1 on array 5 and the shooting angle are combined with the radar coordinates, the azimuth of array 5 and the elevation angle of array 5 to generate a list of hovering points 2 for UAV 11. The list of photographed points 1 is determined based on the number of photographed points 1.

[0117] Preferably, in any of the above embodiments, when there are multiple UAVs 11 performing the shooting and measurement task, the process of generating the list of hovering points 2 of the UAVs 11 based on radar coordinates and the attitude information of the array 5 is as follows:

[0118] The list of points 1 to be photographed on array 5 and the shooting angle are combined with the radar coordinates, the azimuth of array 5 and the elevation angle of array 5 to generate a list of hovering points 2 for UAV 11. The list of hovering points 2 for UAV 11 is grouped to generate a list of hovering points 2 for each UAV 11. The list of points 1 to be photographed is determined based on the number of points 1 to be photographed. The grouping of the list of hovering points 2 for UAV 11 includes one of the following: grouping by angle range, grouping by inner and outer circles, and manual editing of grouping.

[0119] Preferably, in any of the above embodiments, when there are multiple drones 11 performing the shooting and measurement task, the method further includes, before determining whether there is an obstruction in the shooting area 6 at any hovering point 2:

[0120] Based on the optimal motion route 3 corresponding to the hovering point 2 list for each UAV 11, determine whether there is interference between multiple optimal motion routes 3. If there is interference, notify the control center to adjust the flight order of multiple UAVs 11 or adjust the optimal motion route 3 of multiple UAVs 11.

[0121] Preferably, in any of the above embodiments, the process of performing the shooting and measurement task at hovering point 2 if there is no obstruction is as follows:

[0122] If there are no obstructions, the RTK positioning module 13 on the drone 11 determines that the drone 11 has reached the predetermined hovering point 2. The drone 11's heading is adjusted to align with the projection of the shooting direction 4 in the shooting and measurement task of the predetermined hovering point 2 on the ground. The servo gimbal 15 on the drone 11 is used to point the camera 16 on the drone 11 towards the shooting point 1, completing the shooting and measurement task of that heading. It is then determined whether there are any shooting points 1 in the shooting and measurement task of the predetermined hovering point 2 that have not completed the shooting and measurement task. If there are other shooting points 1, the drone 11's heading is readjusted until there are no shooting points 1 in the shooting and measurement task of the predetermined hovering point 2 that have not completed the shooting and measurement task, thus completing the shooting and measurement task of the hovering point 2.

[0123] like Figure 16 As shown in Figure 17, the hardware of this scheme mainly consists of an unmanned aerial platform, photogrammetry equipment, data processing and control terminal 9, RTK base station 10, and also includes array 5, shooting area 6, radar vehicle 8, positioning satellite 12 and unmanned aerial vehicle 11.

[0124] The unmanned aerial platform includes a drone 11, an RTK positioning module 13, an inertial navigation module 14, a servo gimbal 15, and a communication module. The drone 11 serves as the platform carrying the shooting equipment and various functional modules, enabling flexible and rapid movement of the aerial equipment. The RTK positioning module 13 provides the precise coordinates of the drone 11; the inertial navigation module 14 provides the real-time attitude of the drone 11; the servo gimbal 15 is used to adjust the angle of the shooting equipment; and the communication module receives commands from the control terminal and transmits the status information and image data of the unmanned aerial platform.

[0125] The photogrammetry equipment includes a camera 16, a reference ruler, and target points 7. The camera 16 is mounted on an unmanned aerial platform and takes pictures under the command of a control terminal. The reference ruler and target points 7 are placed on the object being photographed. The reference ruler serves as the reference for calculating the data of target points 7, and target points 7 are the measurement points.

[0126] The main functions of the data processing and control terminal are to send motion commands from the UAV 11 and shooting commands from the camera 16, receive image data, and analyze and process the image data.

[0127] Table 1. Hardware composition of the automatic photogrammetry system based on the radar array 5 of UAV 11.

[0128]

[0129]

[0130] The drone 11 is positioned with high precision by equipping the drone with an RTK positioning module 13; the drone 11 is positioned with an inertial navigation module 14 on the aircraft to obtain the flight attitude of the drone 11, and the shooting direction 4 of the camera 16 is adjusted by the attitude conversion relationship between the inertial navigation module 14, the servo gimbal 15 and the camera 16.

[0131] The UAV 11 has its own safety protection functions to ensure the safety of mission execution. Relying on the UAV 11's automatic obstacle avoidance function, when an obstacle is detected in the direction of movement of the UAV 11, a warning signal can be issued in advance, and the coordinates of the affected hovering point 2 can be automatically adjusted and reported to the operator for confirmation.

[0132] like Figure 2 As shown, a radar array 5 imaging and measurement system based on a UAV 11 includes:

[0133] The determination module 100 is used to: generate a list of hovering points 2 of UAV 11 based on radar coordinates and attitude information of array 5, and determine the optimal motion route 3 in the list of hovering points 2 of UAV 11 using the Prim algorithm. The attitude information of array 5 includes: azimuth of array 5 and pitch angle of array 5.

[0134] The shooting module 200 is used to: determine whether there is an obstruction in the shooting area 6 at any hovering point 2; if there is an obstruction, issue an alarm prompt and perform the shooting measurement task at the hovering point 2 according to the operator's instructions or reconfirm the optimal movement route 3; if there is no obstruction, perform the shooting measurement task at the hovering point 2, and determine the position of the next hovering point 2 based on the optimal movement route, until the shooting measurement tasks of all hovering points 2 in the optimal movement route are completed, wherein the set of shooting areas 6 corresponding to all shooting measurement tasks is the shooting area of ​​the radar array 5.

[0135] In some possible implementations, using a drone 11 for photography can be applied to photogrammetric work on high and large array surfaces 5, eliminating the need for auxiliary equipment such as aerial work platforms, thus reducing the danger of the measurement work. By automatically generating the optimal movement route 3 for the drone 11, work efficiency can be significantly improved, the difficulty of the operation can be reduced, and the number of personnel required can be decreased. In addition, automation can effectively reduce the uncertainty of manual operation, improve the quality of photographs taken in the photogrammetric task, and thus improve the measurement accuracy, realizing automated measurement and calibration of large / ultra-large array surfaces 5, and eliminating the blind spots of existing methods.

[0136] Preferably, in any of the above embodiments, before generating the list of hovering points 2 of the UAV 11 based on radar coordinates and array 5 attitude information, the method further includes determining the number of points to be photographed. The process of determining the number of points to be photographed is as follows:

[0137] Calculate the imaging field of view of the camera 16 carried on the UAV 11, and determine the shooting area when the camera 16 is shooting at an angle based on the imaging field of view. Determine the number of shooting points based on the constraints of the shooting area, the shooting area, and the density of shooting points.

[0138] Preferably, in any of the above embodiments, when there is only one UAV 11 performing the shooting and measurement task, the process of generating the list of hovering points 2 of UAV 11 based on radar coordinates and the attitude information of array 5 is as follows:

[0139] The list of photographed points 1 on array 5 and the shooting angle are combined with the radar coordinates, the azimuth of array 5 and the elevation angle of array 5 to generate a list of hovering points 2 for UAV 11. The list of photographed points 1 is determined based on the number of photographed points 1.

[0140] Preferably, in any of the above embodiments, when there are multiple UAVs 11 performing the shooting and measurement task, the process of generating the list of hovering points 2 of the UAVs 11 based on radar coordinates and the attitude information of the array 5 is as follows:

[0141] The list of points 1 to be photographed on array 5 and the shooting angle are combined with the radar coordinates, the azimuth of array 5 and the elevation angle of array 5 to generate a list of hovering points 2 for UAV 11. The list of hovering points 2 for UAV 11 is grouped to generate a list of hovering points 2 for each UAV 11. The list of points 1 to be photographed is determined based on the number of points 1 to be photographed. The grouping of the list of hovering points 2 for UAV 11 includes one of the following: grouping by angle range, grouping by inner and outer circles, and manual editing of grouping.

[0142] Preferably, in any of the above embodiments, when there are multiple drones 11 performing the shooting and measurement task, the method further includes, before determining whether there is an obstruction in the shooting area 6 at any hovering point 2:

[0143] Based on the optimal motion route 3 corresponding to the hovering point 2 list for each UAV 11, determine whether there is interference between multiple optimal motion routes 3. If there is interference, notify the control center to adjust the flight order of multiple UAVs 11 or adjust the optimal motion route 3 of multiple UAVs 11.

[0144] Preferably, in any of the above embodiments, the process of performing the shooting and measurement task at hovering point 2 if there is no obstruction is as follows:

[0145] If there are no obstructions, the RTK positioning module 13 on the drone 11 determines that the drone 11 has reached the predetermined hovering point 2. The drone 11's heading is adjusted to align with the projection of the shooting direction 4 in the shooting and measurement task of the predetermined hovering point 2 on the ground. The servo gimbal 15 on the drone 11 is used to point the camera 16 on the drone 11 towards the shooting point 1, completing the shooting and measurement task of that heading. It is then determined whether there are any shooting points 1 in the shooting and measurement task of the predetermined hovering point 2 that have not completed the shooting and measurement task. If there are other shooting points 1, the drone 11's heading is readjusted until there are no shooting points 1 in the shooting and measurement task of the predetermined hovering point 2 that have not completed the shooting and measurement task, thus completing the shooting and measurement task of the hovering point 2.

[0146] Another technical solution of the present invention to solve the above-mentioned technical problems is as follows: a storage medium storing instructions, wherein when a computer reads the instructions, the computer executes a moving target tracking method as described in any of the above claims.

[0147] In some possible implementations, using a drone 11 for photography can be applied to photogrammetric work on high and large array surfaces 5, eliminating the need for auxiliary equipment such as aerial work platforms, thus reducing the danger of the measurement work. By automatically generating the optimal movement route 3 for the drone 11, work efficiency can be significantly improved, the difficulty of the operation can be reduced, and the number of personnel required can be decreased. In addition, automation can effectively reduce the uncertainty of manual operation, improve the quality of photographs taken in the photogrammetric task, and thus improve the measurement accuracy, realizing automated measurement and calibration of large / ultra-large array surfaces 5, and eliminating the blind spots of existing methods.

[0148] Another technical solution of the present invention to solve the above-mentioned technical problems is as follows: an electronic device, including the above-mentioned storage medium and a processor that executes the instructions in the above-mentioned storage medium.

[0149] In some possible implementations, using a drone 11 for photography can be applied to photogrammetric work on high and large array surfaces 5, eliminating the need for auxiliary equipment such as aerial work platforms, thus reducing the danger of the measurement work. By automatically generating the optimal movement route 3 for the drone 11, work efficiency can be significantly improved, the difficulty of the operation can be reduced, and the number of personnel required can be decreased. In addition, automation can effectively reduce the uncertainty of manual operation, improve the quality of photographs taken in the photogrammetric task, and thus improve the measurement accuracy, realizing automated measurement and calibration of large / ultra-large array surfaces 5, and eliminating the blind spots of existing methods.

Claims

1. A radar array imagery measurement method based on unmanned aerial vehicles (UAVs), characterized in that, include: Based on the radar's location and array attitude information, a list of UAV hovering points is generated. Using the Prim algorithm, the movement route of the UAV used to perform the shooting and measurement task is determined based on the UAV hovering point list. The array attitude information includes the radar's array azimuth and array elevation angles. When the UAV takes pictures of the radar array at any hovering point, it determines whether there is an obstruction. If there is an obstruction, it issues an alarm and performs the shooting and measurement task at that hovering point or reconfirms the movement route according to the operator's instructions. If there is no obstruction, it performs the shooting and measurement task at that hovering point and determines the position of the next hovering point based on the movement route until the shooting and measurement tasks of all hovering points in the movement route are completed. When a single drone is used for shooting and measurement: After determining the drone's movement route, the drone is controlled to perform shooting and measurement tasks starting from the starting point, following the order of hovering points along the route. After the shooting and measurement task at the current hovering point is completed, when the drone moves to the next hovering point, it is checked whether there are large obstacles on the path. If a large obstacle is found, an alarm is issued and the position information of the next hovering point is adjusted. The adjusted position information and alarm information are sent to the operator for manual confirmation. If the operator confirms that the adjusted position information is used for shooting and measurement, the adjusted position information replaces the original position information of the next hovering point. If the operator does not confirm that the adjusted position information is used for shooting and measurement, the original position information of the next hovering point is used for subsequent shooting and measurement processing. When there are multiple drones; The shooting angle is determined based on the list of shooting points on the array. The hovering point list of the UAV is determined based on the list of shooting points, shooting angle, radar location, array azimuth and array elevation angle. The hovering points in the hovering point list are grouped by region, with each group corresponding to one UAV. The Prim algorithm is used to calculate the movement path of the UAV in each group. The list of points to be photographed is determined by determining the number of points to be photographed. The number of points to be photographed is determined by the density of points to be photographed. That is, the imaging field of view of the camera on each drone is determined, the shooting area when the camera is shooting at an angle is determined based on the imaging field of view, and the number of points to be photographed is determined by combining the shooting area and the pre-set constraints of the density of points to be photographed. The constraint is: 1≤p<y / x, where P is the density set of the points being photographed; The process of determining the imaging field of view (FLAD) of each camera on the drone, and then determining the shooting area when the camera is shooting at an angle, is as follows: Let the focal length of the camera lens be... The camera imaging sensor size is h is the horizontal length of the camera's imaging sensor, v is the vertical length of the camera's imaging sensor, and the shooting distance is... Then the horizontal length H and vertical length V of the field of view captured by the camera are: Each shooting point is photographed from four directions, and the shooting position corresponding to each direction is the hovering point. That is, one shooting point corresponds to four hovering points. The shooting area presented in the four directions is four congruent trapezoids. V1 represents the top side of the trapezoid, V2 represents the bottom side of the trapezoid, x represents the distance from the center of the circle to the top side, and y represents the distance from the center of the circle to the bottom side. It is half the field of view. This indicates the camera's angle of view. The vertical line on the left represents the camera sensor, and the lines of the same thickness parallel to this vertical line represent the shooting area when the camera is perpendicular. The lines intersecting the shooting area when the camera is perpendicular form... The lines are at an angle of The shooting area at the time; Compare R with x, where R is the radius of the circular region. The area of ​​the circular region. Let the area of ​​the trapezoidal region be... ,but: like ,but: 。 2. The method for radar array imaging and measurement based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, Grouping the list of drone hovering points includes one of the following: grouping by angle range, grouping by inner and outer circles, and manually editing the grouping.

3. The method for radar array imaging and measurement based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The process of taking pictures and measuring the hovering point if there are no obstructions is as follows: If there are no obstructions, the RTK positioning module on the drone determines that the drone has reached the predetermined hovering point. The drone's heading is adjusted to align with the projection of the shooting direction on the ground in the shooting and measurement task of the predetermined hovering point. The servo gimbal on the drone is used to point the camera on the target point to complete the shooting and measurement task for that heading. It is then determined whether there are any target points in the shooting and measurement task of the predetermined hovering point that have not completed their shooting and measurement tasks. If there are other target points, the drone's heading is readjusted until there are no target points in the shooting and measurement task of the predetermined hovering point that have not completed their shooting and measurement tasks, thus completing the shooting and measurement task for that hovering point.

4. A radar array imaging and measurement system based on unmanned aerial vehicles (UAVs), employing the radar array imaging and measurement method based on UAVs as described in claim 1, characterized in that... The system includes: The determination module is used to: generate a list of UAV hovering points based on the location of the radar and the array attitude information; and determine the movement route of the UAV for performing the shooting and measurement task based on the list of UAV hovering points using the Prim algorithm. The array attitude information includes: the radar array azimuth angle and the array elevation angle. The shooting module is used to: when the UAV takes pictures of the radar array at any hovering point, determine whether there is an obstruction. If there is an obstruction, issue an alarm and perform the shooting and measurement task at that hovering point or reconfirm the movement route according to the operator's instructions. If there is no obstruction, perform the shooting and measurement task at that hovering point and determine the position of the next hovering point based on the movement route, until the shooting and measurement tasks of all hovering points in the movement route are completed.

5. A storage medium, characterized in that, The medium stores instructions that, when read by a computer, cause the computer to execute the method as described in any one of claims 1 to 3.

6. An electronic device, characterized in that, Includes the storage medium of claim 5 and a processor that executes instructions within the storage medium.

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