Radar point cloud drawing method under variable-scale polar coordinate system

Abstract

The invention relates to radar point cloud drawing, in particular to a radar point cloud drawing method under a variable-scale polar coordinate system, and the method comprises the steps: calibrating a radar coordinate system, and calibrating a radar scanning region in a system interface; determining the position and size of a region of interest ROI in a system interface, and calculating the position information of the region of interest ROI according to the relative position in the interface; loading a corresponding second map on a radar point cloud display interface according to the position information of the ROI; connecting a radar, and starting data transmission; receiving and analyzing radar point cloud data; screening the target points according to the position information of the region of interest ROI; converting the position information of the target point into pixel points in a radar point cloud display interface; updating and displaying radar point cloud data frame by frame; according to the technical scheme provided by the invention, the defects of relatively low data processing efficiency and relatively poor display precision in the prior art can be effectively overcome.

Description

Technical Field

[0001] The present invention relates to radar point cloud rendering, and more particularly to a method for rendering radar point clouds in a variable-scale polar coordinate system. Background Art

[0002] With the rapid development of radar technology, its demand in the fields of meteorological observation, autonomous driving, intelligent transportation, UAV monitoring, and perimeter protection is increasing day by day. However, traditional radar point cloud rendering methods face the following technical bottlenecks when dealing with complex scenarios or large-scale data: 1) Traditional methods usually use a fixed global perspective. The full-scale point cloud data is huge, and a single frame of data can reach hundreds of thousands or millions of target points, resulting in low efficiency during the global rendering process and increasing the requirements for computer performance; 2) For the convenience of observation, the pixel points in the display interface represent a range in the actual geodetic coordinate system, resulting in overlapping of targets with similar distances. Summary of the Invention

[0003] In view of the above-mentioned drawbacks of the prior art, the present invention provides a method for rendering radar point clouds in a variable-scale polar coordinate system, which can effectively overcome the defects of low data processing efficiency and poor display accuracy existing in the prior art.

[0004] To achieve the above object, the present invention is realized through the following technical solutions: A method for rendering radar point clouds in a variable-scale polar coordinate system, comprising the following steps: S1. Calibrate the radar coordinate system and calibrate the radar scanning area in the system interface; S2. Determine the position and size of the region of interest (ROI) in the system interface, and calculate the position information of the ROI according to the relative position in the interface; S3. Load the corresponding second map in the radar point cloud display interface according to the position information of the ROI; S4. Connect the radar and start data transmission; S5. Receive and parse the radar point cloud data; S6. Screen the target points according to the position information of the ROI; S7. Convert the position information of the target points into pixel points in the radar point cloud display interface; S8. Update and display the radar point cloud data frame by frame.

[0005] Preferably, calibrating the radar coordinate system in S1 includes: According to the longitude and latitude where the radar is installed, load the first map at the corresponding position in the upper computer ROI selection window, and superimpose the radar coordinate system on the first map; Calculate the relationship between the calculation window size and the actual size of the first map to ensure that the scale ruler of the radar coordinate system corresponds to the geographical location: ; ; Among them, and are the length and width of the window respectively, and are the actual length and actual width of the first map respectively. a is the proportionality coefficient between the window length and the actual length of the first map, and b is the proportionality coefficient between the window width and the actual width of the first map.

[0006] Preferably, in S2, determine the position and size of the region of interest ROI in the system interface, and calculate the position information of the region of interest ROI according to the relative position in the interface, including: After determining the position and size of the region of interest ROI in the system interface, calculate the upper left corner point P LT , lower left corner point P LD , upper right corner point P RT , and lower right corner point P RD of the region of interest ROI in the radar coordinate system, and their position coordinates P LT (X LT , Y LT ), P LD (X LD , Y LD ), P RT (X RT , Y RT ), P RD (X RD , Y RD ), which are used to screen target points, and at the same time calculate the longitude and latitude of the center point P C of the region of interest ROI, which is used to load the corresponding second map in the radar point cloud display interface.

[0007] Preferably, in S3, according to the position information of the region of interest ROI, load the corresponding second map in the radar point cloud display interface, including: According to the calculated longitude and latitude of the center point P C of the region of interest ROI, determine the center position of the second map loaded in the radar point cloud display interface. At the same time, determine the magnification layer level of the second map and the size of the radar point cloud image loaded according to the window size, and calculate the relationship between the radar point cloud image size and the actual size of the second map: ; ; Among them, and are the length and width of the radar point cloud image respectively, and are the actual length and actual width of the second map respectively. l is the proportionality coefficient between the length of the radar point cloud image and the actual length of the second map, and h is the proportionality coefficient between the width of the radar point cloud image and the actual width of the second map.

[0008] Preferably, in S6, according to the position information of the region of interest ROI, the target points are screened, including: Since a radar point cloud image is composed of 4096 radar data packets divided from 360°, when screening the target points, first determine whether the azimuth angle of the current radar data packet is covered by the region of interest ROI, and then determine whether the target points in the radar data packet are within the region of interest ROI.

[0009] Preferably, when screening the target points, first determine whether the azimuth angle of the current radar data packet is covered by the region of interest ROI, and then determine whether the target points in the radar data packet are within the region of interest ROI, including: When the region of interest ROI does not cover the origin of the radar coordinate system, first determine whether the azimuth angle in the header description data of the radar data packet is within the minimum azimuth angle and the maximum azimuth angle of the region of interest ROI: Then determine whether the nearest point distance R 包min and the farthest point distance R 包max in the header description data of the radar data packet overlap with the minimum distance R min and the maximum distance R max between the region of interest ROI and the origin: R min ≤R 包max ; R 包min ≤R max ; Finally, determine one by one whether the target points in the radar data packet are within the region of interest ROI; When the region of interest ROI covers the origin of the radar coordinate system, the region of interest ROI covers all azimuth angles of the current radar data packet. At this time, first determine whether the nearest point distance R 包min in the header description data of the radar data packet is not greater than the maximum distance R max between the region of interest ROI and the origin: R 包min ≤R max ; Then, it is determined one by one whether the target points in the radar data packet are within the region of interest ROI.

[0010] Preferably, the azimuth angle of the region of interest ROI is calculated using the following formula: ; where is the angle between the region of interest ROI and the origin, , (X, Y) is the position coordinate of the point on the region of interest ROI in the radar coordinate system. Substitute the position coordinates of the upper left corner point P LT , the lower left corner point P LD , the upper right corner point P RT , and the lower right corner point P RD of the region of interest ROI in the radar coordinate system respectively, and the minimum azimuth angle and the maximum azimuth angle of the region of interest ROI can be calculated; The distance R between the region of interest ROI and the origin is calculated using the following formula: ; where, substitute the position coordinates of the upper left corner point P LT , the lower left corner point P LD , the upper right corner point P RT , and the lower right corner point P RD of the region of interest ROI in the radar coordinate system respectively, and the minimum distance R min and the maximum distance R max between the region of interest ROI and the origin can be calculated.

[0011] Preferably, one radar point cloud image is composed of 4096 radar data packets divided from 360°. The radar data packet is composed of a packet header description data and multiple target point data. The packet header description data includes azimuth angle, total number of target points in this packet, nearest point distance in this packet, farthest point distance in this packet, total number of azimuth angle data packets in this packet, azimuth angle number in this packet, and check data. The target point data includes target number ID, target distance, target distance X component, target distance Y component, target angle, target speed, and signal strength; where, the value range of the radar azimuth angle is 0 to 4095, that is, 360° is divided into 4096 parts, each part corresponds to a radar data packet, and the radar rotates one week every 2 s.

[0012] Preferably, in S7, converting the position information of the target point into a pixel point in the radar point cloud display interface includes: Converting the position information of the target point into a pixel point in the radar point cloud display interface is represented by the following formula: ; ; Wherein, (X0, Y0) is the position coordinate of the target point T0 in the radar coordinate system, and (P x , P y ) is the pixel point coordinate in the radar point cloud display interface.

[0013] Compared with the prior art, a method for drawing radar point cloud in a variable-scale polar coordinate system provided by the present invention has the following beneficial effects: 1) By operating the pixel points of the radar point cloud image to draw the point cloud, it avoids creating a large number of objects in the program, effectively reduces the occupation of system memory by the program, and at the same time can shorten the processing time of each frame of radar point cloud data, reducing the requirements of the program for computer performance; 2) By selecting the region of interest ROI, two-stage point cloud filtering is performed on the radar point cloud data, significantly reducing the number of single-frame rendering target points. In addition, by directly converting the filtered data into pixel points, the data processing efficiency is significantly improved, meeting the usage requirements of real-time rendering and updating; 3) By artificially selecting the region of interest ROI for variable-scale rendering of the local area, it avoids the overlap phenomenon of nearby targets, determines the position and size of the region of interest ROI, avoids viewing all the point clouds within the radar monitoring range from a fixed perspective, and adaptively changes the ratio of the target point to the pixel point according to the size of the region of interest ROI, effectively improving the display accuracy of the target point and facilitating the staff to clearly observe and accurately analyze the radar point cloud data. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0015] Figure 1 is the flow schematic diagram of the present invention; Figure 2 is the schematic diagram of the principle of the present invention; Figure 3 is the schematic diagram of screening target points in the case where the region of interest ROI does not cover the origin of the radar coordinate system in the present invention; Figure 4 is the schematic diagram of screening target points in the case where the region of interest ROI covers the origin of the radar coordinate system in the present invention; Figure 5It is a schematic diagram of the relationship between the radar coordinate system and the radar azimuth angle in the present invention; Figure 6 It is the actual effect diagram of the upper computer ROI selection window in the present invention; Figure 7 It is the actual effect diagram of the radar point cloud display interface in the present invention. Specific implementation manners

[0016] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] A method for drawing radar point cloud in a variable-scale polar coordinate system, as Figure 1 shown, S1, calibrate the radar coordinate system and calibrate the radar scanning area in the system interface.

[0018] Specifically, calibrate the radar coordinate system, as Figure 2 and Figure 6 shown, including: According to the longitude and latitude where the radar is installed, load the first map at the corresponding position in the upper computer ROI selection window, and superimpose the radar coordinate system on the first map; Calculate the relationship between the window size and the actual size of the first map to ensure that the scale of the radar coordinate system corresponds to the geographical location: ; ; Wherein, and are the length and width of the window respectively, and are the actual length and actual width of the first map respectively, a is the proportionality coefficient between the window length and the actual length of the first map, and b is the proportionality coefficient between the window width and the actual width of the first map.

[0019] In the technical solution of this application, V Length and V Height are 200m, M Length and M Height are 20000m, a and b are 100, and the actual effect of the upper computer ROI selection window is as Figure 6 shown.

[0020] S2. Determine the position and size of the region of interest (ROI) in the system interface, and calculate the position information of the ROI based on the relative positions in the interface, specifically including: After determining the position and size of the ROI in the system interface, calculate the coordinates of the upper-left corner point P LT 、lower-left corner point P LD 、upper-right corner point P RT 、lower-right corner point P RD of the ROI in the radar coordinate system, specifically P LT (X LT , Y LT ), P LD (X LD , Y LD ), P RT (X RT , Y RT ), P RD (X RD , Y RD ). These are used to filter target points, and at the same time, calculate the longitude and latitude of the center point P C of the ROI, which is used to load the corresponding second map in the radar point cloud display interface.

[0021] In the technical solution of this application, the size of the ROI can be configured through parameters, such as in formats like 100%, 50%, 20%, 10%, 5%, etc. By manually dragging the ROI, the area to be viewed can be independently selected.

[0022] S3. According to the position information of the ROI, load the corresponding second map in the radar point cloud display interface, specifically including: Based on the calculated longitude and latitude of the center point P C of the ROI, determine the center position for loading the second map in the radar point cloud display interface. At the same time, determine the magnification layer level of the second map and the size of the radar point cloud image to be loaded according to the window size, and calculate the relationship between the size of the radar point cloud image and the actual size of the second map: ; ; Among them, 、 are the length and width of the radar point cloud image respectively, 、 are the actual length and actual width of the second map respectively, l is the proportionality coefficient between the length of the radar point cloud image and the actual length of the second map, and h is the proportionality coefficient between the width of the radar point cloud image and the actual width of the second map.

[0023] In the technical solution of this application, m Length and m Height are related to the window size. The size of the radar point cloud image is 900*600 (which can be adjusted according to the actual situation). A pixel represents an area with a length of l and a width of h in reality. The larger the values of l and h, the easier it is to observe the pixel in the radar point cloud display interface, but at the same time, it will also cause overlapping of targets with similar distances. When the window size is smaller, that is, when the values of m Length and m Height are smaller, the point cloud drawing is more accurate and the overlapping phenomenon is less. The actual effect of the radar point cloud display interface is as shown in Figure 7 .

[0024] S4. Connect the radar and start data transmission.

[0025] S5. Receive and analyze the radar point cloud data.

[0026] S6. According to the position information of the region of interest ROI, screen the target points, specifically including: Since a radar point cloud image is composed of 4096 radar data packets divided from 360°, when screening the target points, first judge whether the azimuth angle of the current radar data packet is covered by the region of interest ROI, and then judge whether the target points in the radar data packet are within the region of interest ROI.

[0027] Specifically, when screening the target points, first judge whether the azimuth angle of the current radar data packet is covered by the region of interest ROI, and then judge whether the target points in the radar data packet are within the region of interest ROI, including: As shown in Figure 3 , when the region of interest ROI does not cover the origin of the radar coordinate system, first judge whether the azimuth angle in the packet header description data of the radar data packet is within the minimum azimuth angle and the maximum azimuth angle of the region of interest ROI: ; Then judge whether the nearest point distance R 包min and the farthest point distance R 包max in the packet header description data of the radar data packet overlap with the minimum distance R min and the maximum distance R max between the region of interest ROI and the origin: R min ≤R 包max ; R 包min ≤R max ; Finally, it is determined one by one whether the target points in the radar data packet are within the region of interest ROI; As Figure 4 shown, when the region of interest ROI covers the origin of the radar coordinate system, the region of interest ROI covers all azimuth angles of the current radar data packet. At this time, first determine whether the nearest point distance R 包min in the packet header description data of the radar data packet is not greater than the maximum distance R max between the region of interest ROI and the origin: R 包min ≤R max ; Then, it is determined one by one whether the target points in the radar data packet are within the region of interest ROI.

[0028] Specifically, the azimuth angle of the region of interest ROI is calculated using the following formula: ; where is the angle between the region of interest ROI and the origin, , (X,Y) is the position coordinate of the point on the region of interest ROI in the radar coordinate system. Substituting the position coordinates of the upper left corner point P LT , lower left corner point P LD , upper right corner point P RT , and lower right corner point P RD of the region of interest ROI in the radar coordinate system respectively can calculate the minimum azimuth angle and the maximum azimuth angle ; The distance R between the region of interest ROI and the origin is calculated using the following formula: ; where, substituting the position coordinates of the upper left corner point P LT , lower left corner point P LD , upper right corner point P RT , and lower right corner point P RD of the region of interest ROI in the radar coordinate system respectively can calculate the minimum distance R min and the maximum distance R max .

[0029] In the technical solution of this application, a radar point cloud image is composed of 4,096 radar data packets divided from 360°. A radar data packet consists of a packet header description data and multiple target point data. The packet header description data includes azimuth angle, total number of target points in this packet, distance of the nearest point in this packet, distance of the farthest point in this packet, total number of azimuth angle data packets in this packet, azimuth angle number of this packet, and check data. The target point data includes target number ID, target distance, X component of target distance, Y component of target distance, target angle, target speed, and signal strength; Among them, the value range of the radar azimuth angle is 0 to 4,095, that is, 360° is divided into 4,096 parts (as Figure 5 shown), each part corresponds to a radar data packet, and the radar rotates one week every 2 s.

[0030] S7. Convert the position information of the target point into pixel points in the radar point cloud display interface, specifically including: Converting the position information of the target point into pixel points in the radar point cloud display interface is represented by the following formula: ; ; Among them, (X0, Y0) is the position coordinate of the target point T0 in the radar coordinate system, and (P x , P y ) is the pixel point coordinate in the radar point cloud display interface.

[0031] In the technical solution of this application, by operating the pixel points of the radar point cloud image to draw the point cloud, at least 1,024,000 target points can be drawn into the radar point cloud image within 100 ms, effectively improving the drawing efficiency of the radar point cloud image.

[0032] S8. Update and display the radar point cloud data frame by frame.

[0033] In the technical solution of this application, according to the position and size of the region of interest ROI, theoretically the calculation amount will be reduced by 0% to 99%. Assuming that a radar point cloud image consists of M radar data packets, and on average each packet has N target points, when drawing the radar point cloud image in full volume, the total time complexity is O(M×N), and the total space complexity is O(M×N). When the position and size of the region of interest ROI change, the worst time complexity is O(M×N), and the best is O(M). Since the technical solution of this application needs to clear historical points when drawing the radar point cloud image, the space complexity is O(M×N).

[0034] To better verify the technical effect of the technical solution of this application, a specific example will be described in detail below.

[0035] The length of the region of interest (ROI) is 3000 m and the width is 2000 m. In the radar rectangular coordinate system, the upper left corner point P of the ROI LT (3000, 2000), the lower left corner point P LD (3000, 0), the upper right corner point P RT (6000, 2000), the lower right corner point P RD (6000, 0). Substituting into the formula for calculation, the included angle between the ROI and the origin is obtained as in the range of (326.31, 360), and the azimuth angle of the ROI is in the range of (3712, 4095). The minimum distance R between the ROI and the origin is min 3000 m, and the maximum distance R max is 6324.56 m.

[0036] When receiving the radar data packet, the first step: judge whether the azimuth angle in the packet header description data is in the range of (3712, 4095). From this, it can be deduced that among the 4096 azimuth angles, only 9.375% of the radar data packets need to be further parsed; The second step: judge whether the nearest point distance R 包min and the farthest point distance R 包max in the packet header description data overlap with (3000, 6324.56), and perform a secondary screening on the radar data packets; The third step: for the radar data packets that meet the conditions, continue to parse the target point data, and judge whether the X component of the target distance is in the range of (3000, 6000) and whether the Y component of the target distance is in the range of (0, 2000). If the target point is within the ROI, enter the fourth step.

[0037] The fourth step: The specification of the radar point cloud image used for drawing in this example is 900 * 600. Therefore, both the scale factor l between the length of the radar point cloud image and the actual length of the second map and the scale factor h between the width of the radar point cloud image and the actual width of the second map are 10 / 3. Assuming that the X component of the target distance of a target point is 4173 and the Y component of the target distance is 1300, substituting into the formula to calculate the pixel coordinates in the corresponding radar point cloud display interface is (351.9, 210). Since the pixel positions in the radar point cloud display interface are integers, the display position of this target point in the radar point cloud image is the 210th row and the 351st column.

[0038] In the above example, the number of target points for each azimuth angle is 5000. If the method of selecting the position and size of the selected region of interest (ROI) is not used, it takes 1820 ms to draw a radar point cloud image; while by using the method of selecting the position and size of the selected region of interest (ROI), it only takes 174 ms to complete the drawing of the radar point cloud image within this region.

[0039] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements will not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for radar point cloud rendering in a variable-scale polar coordinate system, characterized in that: It includes the following steps: S1. Calibrate the radar coordinate system and calibrate the radar scanning area in the system interface; S2. Determine the position and size of the region of interest (ROI) in the system interface, and calculate the position information of the ROI according to the relative position in the interface; S3. Load the corresponding second map in the radar point cloud display interface according to the position information of the ROI; S4. Connect the radar and start data transmission; S5. Receive and parse the radar point cloud data; S6. Screen the target points according to the position information of the ROI; S7. Convert the position information of the target points into pixel points in the radar point cloud display interface; S8. Update and display the radar point cloud data frame by frame.

2. The method for drawing radar point cloud in a variable-scale polar coordinate system according to claim 1, wherein: Calibrating the radar coordinate system in S1 includes: According to the longitude and latitude where the radar is installed, load the first map at the corresponding position in the upper computer ROI selection window, and superimpose the radar coordinate system on the first map; Calculate the relationship between the window size and the actual size of the first map to ensure that the scale of the radar coordinate system corresponds to the geographical location: ； ； Among them, , are the length and width of the window respectively, , are the actual length and actual width of the first map respectively, a is the proportionality coefficient between the window length and the actual length of the first map, and b is the proportionality coefficient between the window width and the actual width of the first map.

3. The method for drawing radar point cloud in a variable-scale polar coordinate system according to claim 2, wherein: Determining the position and size of the region of interest (ROI) in the system interface in S2 and calculating the position information of the ROI according to the relative position in the interface includes: After determining the position and size of the region of interest (ROI) in the system interface, calculate the coordinates of the upper-left corner point P of the ROI LT , the lower-left corner point P LD , the upper-right corner point P RT , and the lower-right corner point P RD in the radar coordinate system, denoted as P LT (X LT , Y LT ), P LD (X LD , Y LD ), P RT (X RT , Y RT ), and P RD (X RD , Y RD ). These are used to filter target points. At the same time, calculate the longitude and latitude of the center point P C of the ROI, which is used to load the corresponding second map in the radar point cloud display interface.

4. The method for drawing radar point cloud in a variable-scale polar coordinate system according to claim 3, wherein: Loading the corresponding second map in the radar point cloud display interface according to the position information of the ROI in S3 includes: According to the center point P of the region of interest (ROI) obtained by calculation C of the longitude and latitude, determine the center position of the second map loaded on the radar point cloud display interface. At the same time, determine the magnification layer level of the second map and the size of the radar point cloud image loaded according to the window size, and calculate the relationship between the size of the radar point cloud image and the actual size of the second map: ； ； Among them, , are the length and width of the radar point cloud image respectively, , are the actual length and actual width of the second map respectively. l is the proportionality coefficient between the length of the radar point cloud image and the actual length of the second map, and h is the proportionality coefficient between the width of the radar point cloud image and the actual width of the second map.

5. The method for drawing radar point clouds in a variable-scale polar coordinate system according to claim 4, wherein: Screening the target points according to the position information of the ROI in S6 includes: Since a radar point cloud image is composed of 4096 radar data packets divided from 360°, when screening the target points, first judge whether the azimuth angle of the current radar data packet is covered by the ROI, and then judge whether the target points in the radar data packet are within the ROI.

6. The method for drawing radar point cloud in a variable-scale polar coordinate system according to claim 5, wherein: When screening the target points, first judge whether the azimuth angle of the current radar data packet is covered by the ROI, and then judge whether the target points in the radar data packet are within the ROI, including: When the region of interest (ROI) does not cover the origin of the radar coordinate system, first determine the azimuth angle in the header description data of the radar data packet whether it is within the minimum azimuth angle and the maximum azimuth angle of the region of interest (ROI): ； Then, determine whether the nearest point distance R and the farthest point distance R in the header description data of the radar data packet overlap with the minimum distance R and the maximum distance R between the region of interest ROI and the origin: 包min and the farthest point distance R of this packet 包max overlap with the minimum distance R between the region of interest ROI and the origin min and the maximum distance R max : R min ≤R 包max ; R 包min ≤R max ; Finally, judge one by one whether the target points in the radar data packet are within the ROI; When the region of interest (ROI) covers the origin of the radar coordinate system, the ROI covers all azimuth angles of the current radar data packet. At this time, first determine whether the nearest point distance R in the packet header description data of the radar data packet 包min is not greater than the maximum distance R between the ROI and the origin max : R 包min ≤R max ; Then judge one by one whether the target points in the radar data packet are within the ROI.

7. The method for drawing radar point cloud in a variable-scale polar coordinate system according to claim 6, characterized in that: The azimuth angle of the region of interest (ROI) is calculated using the following formula: ； Wherein, is the angle between the region of interest ROI and the origin, , (X, Y) is the position coordinate of the point on the region of interest ROI in the radar coordinate system. Substituting the position coordinates of the upper left corner point P LT , the lower left corner point P LD , the upper right corner point P RT , and the lower right corner point P RD of the region of interest ROI in the radar coordinate system respectively can calculate the minimum azimuth angle and the maximum azimuth angle ; The distance R between the ROI and the origin is calculated by the following formula: ； Among them, substituting the position coordinates of the upper left corner point P LT , the lower left corner point P LD , the upper right corner point P RT , and the lower right corner point P RD of the region of interest ROI in the radar coordinate system respectively can calculate the minimum distance R min and the maximum distance R max between the region of interest ROI and the origin.

8. The method for drawing radar point cloud in a variable-scale polar coordinate system according to claim 6, characterized in that: A radar point cloud image is composed of 4096 radar data packets divided from 360°. The radar data packet consists of a packet header description data and multiple target point data. The packet header description data includes azimuth angle, total number of target points in this packet, nearest point distance in this packet, farthest point distance in this packet, total number of azimuth angle data packets in this packet, azimuth angle number in this packet, and check data. The target point data includes target number ID, target distance, X component of target distance, Y component of target distance, target angle, target speed, and signal strength; Among them, the value range of the radar azimuth angle is 0 to 4095, that is, 360° is divided into 4096 parts, each corresponding to a radar data packet, and the radar rotates one week every 2 s.

9. The method for drawing radar point cloud in a variable-scale polar coordinate system according to claim 5, wherein: Converting the position information of the target points into pixel points in the radar point cloud display interface in S7 includes: Converting the position information of the target points into pixel points in the radar point cloud display interface is expressed by the following formula: ； ； Among them, (X0, Y0) is the position coordinate of the target point T0 in the radar coordinate system, and (P x , P y ) is the pixel coordinate in the radar point cloud display interface.

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