Radar echo drawing method based on vectorization distance detection

Abstract

The invention discloses a radar echo drawing method based on vectorization distance detection. The radar echo drawing method comprises the following steps: step 1, inputting radar video data; step 2, setting detection parameters; step 3, carrying out data normalization; step 4, one-dimensional distance detection; step 5, vectorization processing: according to the start offset and the end offset of each target, converting each target in each video azimuth line data MTP into a regular octagon, and calculating position coordinates of eight vertexes of the regular octagon; and step 6, generating a radar picture. According to the invention, a detection result is formed based on one-dimensional distance detection of radar video data. The radar video data are vectorized according to the one-dimensional distance detection result, and finally the vectorized data are pushed to a demander needing to draw radar echoes through a network roller. By adopting the method to draw the radar video, the radar video drawing efficiency can be further improved, and the radar video echo display effect is improved.

Description

Technical Field

[0001] The present invention relates to the fields of radar manufacturing, navigation, maritime affairs, and the like, and in particular to a radar echo drawing method based on vectorized distance detection. Background Art

[0002] Radar video echo usually refers to the image or data formed on the radar screen by the signal reflected by the target after the radar wave is transmitted in the radar system. The research on radar video echo is crucial to improving the performance of radar systems, especially in the fields of military defense, weather forecasting, aviation navigation, etc.

[0003] Radar video echo mapping technology continues to improve with the advancement of radar hardware and signal processing technology. For example, the application of digital signal processing technology allows radar to identify targets more accurately while reducing the impact of interference and noise. In addition, Doppler radar technology can measure the speed of the target, which is very important for weather forecasting and air traffic control.

[0004] On the algorithmic side, researchers are developing more efficient data processing methods to improve the quality of radar video echoes. This includes using machine learning and artificial intelligence techniques to automatically identify and classify different features in radar echoes, thereby providing more accurate target information.

[0005] In the field of meteorological applications, radar video echoes help scientists better understand and predict weather patterns. For example, by analyzing radar echoes, information such as storm paths and rainfall intensity can be predicted, which is crucial for disaster warning.

[0006] As technology develops, more and more military radar technology is being transferred to the civilian market. Civilian sectors such as aviation, maritime and traffic management are also using advanced radar video echo technology to improve safety and efficiency.

[0007] The research on radar video echo is an ongoing process. With the advancement of technology, the performance and functions of radar systems are constantly improving. The drawing of radar video echo is often completed based on the processing of raw video data, so it has high requirements for hardware parameters and software algorithm capabilities. Especially in the current prevalence of BS systems, it is difficult to directly draw the radar video echo data directly on the display page of the BS system using traditional rendering and drawing methods. Summary of the invention

[0008] The technical problem to be solved by the present invention is to provide a radar echo drawing method based on vectorized distance detection in view of the deficiencies of the above-mentioned prior art. The radar echo drawing method based on vectorized distance detection forms a detection result based on the one-dimensional distance detection of radar video data. The radar video data is then vectorized according to the one-dimensional distance detection result, and finally the vectorized data is pushed to the demander who needs to draw the radar echo through the network. The use of this method to draw radar video can further improve the efficiency of radar video drawing and improve the display effect of radar video echo.

[0009] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0010] A radar echo drawing method based on vectorized distance detection includes the following steps.

[0011] Step 1: Input radar video data: Input radar video data and read all video sampling point information from the radar video data; each video sampling point includes azimuth, distance and intensity.

[0012] Step 2: Set detection parameters: The detection parameters include the sampling point intensity threshold DetThr.

[0013] Step 3, data normalization: the intensity of each video sampling point obtained in step 1 is compared with the sampling point intensity threshold DetThr set in step 2, the video sampling point information below the sampling point intensity threshold DetThr is filtered out and normalized, and the video sampling point information not below the sampling point intensity threshold DetThr is retained and normalized, so as to obtain the normalized video sampling point information;

[0014] Step 4, one-dimensional distance detection: After the data is normalized, the amplitude units of each video azimuth line data MTP that are continuous or tend to be continuous in distance are merged into the same set of amplitude units and recorded as a target; each target includes a starting offset dStartRngX, an ending offset dEndRngX and a maximum pixel intensity ucAmpX; where X represents the target number.

[0015] Step 5, vectorization processing: according to the starting offset and ending offset of each target, each target in each video orientation line data MTP is converted into a regular octagon and the position coordinates of its 8 vertices are calculated; wherein, the 8 vertices in each target are vertex 1, vertex 2, vertex 3, vertex 4, vertex 5, vertex 6, vertex 7 and vertex 8 in counterclockwise order; wherein, vertex 1, vertex 4, vertex 5 and vertex 8 are the lower left corner point, lower right corner point, upper right corner point and upper left corner point respectively.

[0016] Step 6, generate radar image: according to the 8 vertex position coordinates calculated in step 5, draw each target on the display, and set the intensity of all pixels of the Xth target to ucAmpX;

[0017] Step 7, Update: When one of the MTPs is updated, repeat steps 1 to 6.

[0018] In step 3, let the intensity of the jth sampling point at the i-th position be amp ij , then the normalization method is:

[0019] A. When amp ij When <DetThr, after normalization, it is uniformly recorded as: amp ij =0.

[0020] B. When amp ij When ≥DetThr, after normalization, it is uniformly recorded as: amp ij =1.

[0021] In step 4, the one-dimensional distance detection method includes the following steps:

[0022] Step 4-1, detecting the starting offset of the target: using the M / N detector as a sliding window, passing the N amplitude units in each video azimuth line data MTP after data normalization through the window, if M in the window is 0, the window is moved backward in sequence until M>1 appears in the window; the amplitude unit where M>1 appears for the first time is recorded as the starting offset dStartRngX of the corresponding target;

[0023] Step 4-2, detect the target end offset: For the target whose starting offset has been found, the window continues to move backward in sequence. When M in the window is 0, the target is considered to be ended, and the amplitude unit where M=0 appears for the first time is recorded as the end offset dEndRngX of the corresponding target.

[0024] Step 4-3, calculate the maximum pixel intensity of the target: When the distance detection of a target is completed, calculate the maximum pixel intensity ucAmpX of the target at this time.

[0025] Step 4-4, point trace saving: When a starting offset and an ending offset are recorded, it is considered that a 1-dimensional point trace is generated, and the 1-dimensional point trace is placed in the 1-dimensional point trace storage queue.

[0026] In step 5, assume that the longitude and latitude of the radar location is (φ0, λ0), and the straight-line distances from vertex 1, vertex 2, vertex 3, vertex 4, vertex 5, vertex 6, vertex 7, and vertex 8 of the Xth target in the current input MTP to the radar are d1, d2, d3, d4, d5, d6, d7, and d8, respectively; then the calculation formulas for d1 to d8 are:

[0027] d1=d8=dStartRngX

[0028] d2=d7=dStartRngX+0.1*dDelRngX

[0029] d3=d6=dStartRng+0.9*dDelRngX

[0030] d4=d5=dEndRngX

[0031] in:

[0032] dDelRngX=dEndRngX-dStartRngX

[0033] Where dDelRngX is the length of the Xth target in the current input MTP.

[0034] In step 5, the azimuths of vertex 1, vertex 2, vertex 3, vertex 4, vertex 5, vertex 6, vertex 7 and vertex 8 of the Xth target in the current input MTP to the radar are θ1, θ2, θ3, θ4, θ5, θ6, θ7 and θ8 respectively; then the calculation formulas for θ1 to θ8 are:

[0035] θ1=θ4=dAzmX-dDel*0.75

[0036] θ2=θ3=dAzmX-dDel

[0037] θ5=θ8=dAzmX+dDel*0.75

[0038] θ6=θ7=dAzmX+dDel

[0039] Where dAzmX is the azimuth value of the Xth target in the current input MTP.

[0040] dDel is the azimuth step.

[0041] In step 2, the detection parameters also include the radar video azimuth quantization degree value MaxAziLines, and the calculation formula of the azimuth step dDel is:

[0042] dDel=360 / 1.0 / MaxAziLines.

[0043] In step 5, the calculation formula of dAzmX is:

[0044] dAzmX=nIdx*360.0 / MaxAziLines

[0045] Where nIdx is the position code of the Xth target in the current input MTP.

[0046] In step 5, the longitude and latitude coordinates of vertex 1, vertex 2, vertex 3, vertex 4, vertex 5, vertex 6, vertex 7, and vertex 8 of the Xth target in the current input MTP are (φ1, λ1), (φ2, λ2), (φ3, λ3), (φ4, λ4), (φ5, λ5), (φ6, λ6), (φ7, λ7), and (φ8, λ8), respectively. The specific calculation formula is:

[0047] φ1=arcsin(sinφ0·cosδ1+cosφ0·sinδ1·cosθ1)

[0048] λ1=λ0+arctan2(sinθ1·sinδ1·cosφ0, cosδ1-sinφ0·sinφ1)

[0049] φ2=arcsin(sinφ0·cosδ2+cosφ0·sinδ2·cosθ2)

[0050] λ2=λ0+arctan2(sinθ2·sinδ2·cosφ0, cosδ2-sinφ0·sinφ2)

[0051] φ3=arcsin(sinφ0·cosδ3+cosφ0·sinδ3·cosθ3)

[0052] λ3=λ0+arctan2(sinθ3·sinδ3·cosφ0, cosδ3-sinφ0·sinφ3)

[0053] φ4=arcsin(sinφ0·cosδ4+cosφ0·sinδ4·cosθ4)

[0054] λ4=λ0+arctan2(sinθ4·sinδ4·cosφ0, cosδ4-sinφ0·sinφ4)

[0055] φ5=arcsin(sinφ0·cosδ5+cosφ0·sinδ5·cosθ5)

[0056] λ5=λ0+arctan2(sinθ5·sinδ5·cosφ0, cosδ5-sinφ0·sinφ5)

[0057] φ6=arcsin(sinφ0·cosδ6+cosφ0·sinδ6·cosθ6)

[0058] λ6=λ0+arctan2(sinθ6·sinδ6·cosφ0, cosδ6-sinφ0·sinφ6)

[0059] φ7=arcsin(sinφ0·cosδ7+cosφ0·sinδ7·cosθ7)

[0060] λ7=λ0+arctan2(sinθ7·sinδ7·cosφ0, cosδ7-sinφ0·sinφ7)

[0061] φ8=arcsin(sinφ0·cosδ8+cosφ0·sinδ8·cosθ8)

[0062] λ8=λ0+arctan2(sinθ8·sinδ8·cosφ0, cosδ8-sinφ0·sinφ8)

[0063] in:

[0064]

[0065] Where δ1, δ2, δ3, δ4, δ5, δ6, δ7 and δ8 are the spherical angles from vertex 1, vertex 2, vertex 3, vertex 4, vertex 5, vertex 6, vertex 7 and vertex 8 of the Xth target in the current input MTP to the radar, respectively.

[0066] R is the radius of the earth.

[0067] The present invention has the following beneficial effects: the present invention forms a detection result based on one-dimensional distance detection of radar video data. Then, the radar video data is vectorized according to the one-dimensional distance detection result, and finally the vectorized data is pushed to the demander who needs to draw radar echo through the network. By using this method to draw radar video, the efficiency of radar video drawing can be further improved, and the display effect of radar video echo can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A flow chart of a radar echo drawing method based on vectorized distance detection of the present invention is shown.

[0069] Figure 2 A simplified schematic diagram of data normalization in the present invention is shown.

[0070] Figure 3 A schematic diagram showing one-dimensional distance detection using an M / N detector in the present invention is shown.

[0071] Figure 4 A schematic diagram of one of the targets after one-dimensional distance detection in the present invention is shown.

[0072] Figure 5 The present invention shows that Figure 4 Schematic diagram of the target after vectorization. DETAILED DESCRIPTION

[0073] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific preferred embodiments.

[0074] In the description of the present invention, it should be understood that the terms "left side", "right side", "upper part", "lower part" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. "First", "second" and the like do not indicate the importance of the components, and therefore cannot be understood as limiting the present invention. The specific dimensions used in this embodiment are only for illustrating the technical solution, and do not limit the scope of protection of the present invention.

[0075] like Figure 1 As shown, a radar echo drawing method based on vectorized distance detection includes the following steps.

[0076] Step 1: Input radar video data: Input radar video data and read all video sampling point information from the radar video data; each video sampling point includes azimuth, distance, intensity, etc.

[0077] Step 2: Set detection parameters: The detection parameters include the sampling point intensity threshold DetThr and the radar video azimuth quantization degree value MaxAziLines.

[0078] Step 3, data normalization: the intensity of each video sampling point obtained in step 1 is compared with the sampling point intensity threshold DetThr set in step 2, the video sampling point information below the sampling point intensity threshold DetThr is filtered out and normalized, and the video sampling point information not below the sampling point intensity threshold DetThr is retained and normalized, so as to obtain the normalized video sampling point information.

[0079] Let the intensity of the jth sampling point at the i-th position be amp ij , then the normalization method is preferably:

[0080] A. When amp ij When <DetThr, after normalization, it is uniformly recorded as: amp ij =0.

[0081] B. When amp ij When ≥DetThr, after normalization, it is uniformly recorded as: amp ij =1.

[0082] Step 4: One-dimensional distance detection

[0083] After the data is normalized, what remains is the data that needs to be plotted and displayed. These data need to be classified and simplified. Figure 2 As shown, the green ones are video data and the white ones are empty data. The green data needs to be drawn on the drawing panel at the corresponding position and distance.

[0084] The present invention merges the amplitude units that are continuous or tend to be continuous in distance of each video azimuth line data MTP after data normalization into the same set of amplitude units and records them as a target; each target includes a start offset dStartRngX, an end offset dEndRngX and a maximum pixel point intensity ucAmpX; wherein X represents the target number.

[0085] The above one-dimensional distance detection method preferably includes the following steps.

[0086] Step 4-1, detect the target starting offset: Figure 3 As shown, an M / N detector is used as a sliding window, and the N amplitude units in each video azimuth line data MTP after data normalization pass through the window. If M in the window is 0, the window moves backward in sequence until M>1 appears in the window; the amplitude unit where M>1 appears for the first time is recorded as the starting offset dStartRngX of the corresponding target.

[0087] Step 4-2, detect the target end offset: For the target whose starting offset has been found, the window continues to move backward in sequence. When M in the window is 0, the target is considered to be ended, and the amplitude unit where M=0 appears for the first time is recorded as the end offset dEndRngX of the corresponding target.

[0088] Step 4-3, calculate the maximum pixel intensity of the target: When the distance detection of a target is completed, calculate the maximum pixel intensity ucAmpX of the target at this time.

[0089] Step 4-4, point trace saving: When a starting offset and an ending offset are recorded, it is considered that a 1D point trace is generated, and the 1D point trace is put into the 1D point trace storage queue, as shown in Table 1 below.

[0090] Table 1

[0091]

[0092] Step 5: Vectorization: According to the starting offset and ending offset of each target, each video position line data MTP is vectorized. Figure 4Each target shown is converted into a regular octagon and the position coordinates of its 8 vertices are calculated.

[0093] like Figure 5 As shown, the eight vertices in each target are vertex 1, vertex 2, vertex 3, vertex 4, vertex 5, vertex 6, vertex 7 and vertex 8 in counterclockwise order; among them, vertex 1, vertex 4, vertex 5 and vertex 8 are the lower left corner point, lower right corner point, upper right corner point and upper left corner point respectively.

[0094] Assume that the longitude and latitude of the radar location is (φ0, λ0), and the straight-line distances from vertex 1, vertex 2, vertex 3, vertex 4, vertex 5, vertex 6, vertex 7, and vertex 8 of the Xth target in the current input MTP to the radar are d1, d2, d3, d4, d5, d6, d7, and d8 respectively; then the calculation formulas for d1 to d8 are:

[0095] d1=d8=dStartRngX

[0096] d2=d7=dStartRngX+0.1*dDelRngX

[0097] d3=d6=dStartRng+0.9*dDelRngX

[0098] d4=d5=dEndRngX

[0099] in:

[0100] dDelRngX=dEndRngX-dStartRngX

[0101] Where dDelRngX is the length of the Xth target in the current input MTP.

[0102] In step 5, the azimuths of vertex 1, vertex 2, vertex 3, vertex 4, vertex 5, vertex 6, vertex 7 and vertex 8 of the Xth target in the current input MTP to the radar are θ1, θ2, θ3, θ4, θ5, θ6, θ7 and θ8 respectively; then the calculation formulas for θ1 to θ8 are:

[0103] θ1=θ4=dAzmX-dDel*0.75

[0104] θ2=θ3=dAzmX-dDel

[0105] θ5=θ8=dAzmX+dDel*0.75

[0106] θ6=θ7=dAzmX+dDel

[0107] Where dAzmX is the azimuth value of the Xth target in the current input MTP.

[0108] dDel is the azimuth step.

[0109] In step 2, the detection parameters also include, then the calculation formula of the azimuth step dDel is:

[0110] dDel=360 / 1.0 / MaxAziLines.

[0111] In step 5, the calculation formula of dAzmX is:

[0112] dAzmX=nIdx*360.0 / MaxAziLines

[0113] Where nIdx is the position code of the Xth target in the current input MTP.

[0114] In step 5, the longitude and latitude coordinates of vertex 1, vertex 2, vertex 3, vertex 4, vertex 5, vertex 6, vertex 7, and vertex 8 of the Xth target in the current input MTP are (φ1, λ1), (φ2, λ2), (φ3, λ3), (φ4, λ4), (φ5, λ5), (φ6, λ6), (φ7, λ7), and (φ8, λ8), respectively. The specific calculation formula is:

[0115] φ1=arcsin(sinφ0·cosδ1+cosφ0·sinδ1·cosθ1)

[0116] λ1=λ0+arctan2(sinθ1·sinδ1·cosφ0, cosδ1-sinφ0·sinφ1)

[0117] φ2=arcsin(sinφ0·cosδ2+cosφ0·sinδ2·cosθ2)

[0118] λ2=λ0+arctan2(sinθ2·sinδ2·cosφ0, cosδ2-sinφ0·sinφ2)

[0119] φ3=arcsin(sinφ0·cosδ3+cosφ0·sinδ3·cosθ3)

[0120] λ3=λ0+arctan2(sinθ3·sinδ3·cosφ0, cosδ3-sinφ0·sinφ3)

[0121] φ4=arcsin(sinφ0·cosδ4+cosφ0·sinδ4·cosθ4)

[0122] λ4=λ0+arctan2(sinθ4·sinδ4·cosφ0, cosδ4-sinφ0·sinφ4)

[0123] φ5=arcsin(sinφ0·cosδ5+cosφ0·sinδ5·cosθ5)

[0124] λ5=λ0+arctan2(sinθ5·sinδ5·cosφ0, cosδ5-sinφ0·sinφ5)

[0125] φ6=arcsin(sinφ0·cosδ6+cosφ0·sinδ6·cosθ6)

[0126] λ6=λ0+arctan2(sinθ6·sinδ6·cosφ0, cosδ6-sinφ0·sinφ6)

[0127] φ7=arcsin(sinφ0·cosδ7+cosφ0·sinδ7·cosθ7)

[0128] λ7=λ0+arctan2(sinθ7·sinδ7·cosφ0, cosδ7-sinφ0·sinφ7)

[0129] φ8=arcsin(sinφ0·cosδ8+cosφ0·sinδ8·cosθ8)

[0130] λ8=λ0+arctan2(sinθ8·sinδ8·cosφ0, cosδ8-sinφ0·sinφ8)

[0131] in:

[0132]

[0133] Where δ1, δ2, δ3, δ4, δ5, δ6, δ7 and δ8 are the spherical angles from vertex 1, vertex 2, vertex 3, vertex 4, vertex 5, vertex 6, vertex 7 and vertex 8 of the Xth target in the current input MTP to the radar, respectively.

[0134] R is the radius of the earth.

[0135] Step 6, generate radar image: according to the 8 vertex position coordinates calculated in step 5, draw each target on the display, and set the intensity of all pixels of the Xth target to ucAmpX.

[0136] Step 7, Update: When one of the MTPs is updated, repeat steps 1 to 6.

[0137] The preferred embodiments of the present invention are described in detail above; however, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all belong to the protection scope of the present invention.

Claims

1. A radar echo rendering method based on vectorized distance detection, characterized in that: The steps include: Step 1, input radar video data: input radar video data, and read all video sampling point information from the radar video data; each video sampling point includes azimuth, distance and intensity; Step 2, set detection parameters: the detection parameters include sampling point intensity threshold DetThr; Step 3, data normalization: the intensity of each video sampling point obtained in step 1 is compared with the sampling point intensity threshold DetThr set in step 2, the video sampling point information below the sampling point intensity threshold DetThr is filtered out and normalized, and the video sampling point information not below the sampling point intensity threshold DetThr is retained and normalized, so as to obtain the normalized video sampling point information; Step 4, one-dimensional distance detection: After the data is normalized, the amplitude units of each video azimuth line data MTP that are continuous or tend to be continuous in distance are merged into the same set of amplitude units and recorded as a target; each target includes a start offset dStartRngX, an end offset dEndRngX and a maximum pixel intensity ucAmpX; where X represents the target number; Step 5, vectorization processing: according to the starting offset and the ending offset of each target, each target in each video azimuth line data MTP is converted into a regular octagon and the position coordinates of its 8 vertices are calculated; wherein, the 8 vertices in each target are vertex 1, vertex 2, vertex 3, vertex 4, vertex 5, vertex 6, vertex 7 and vertex 8 in counterclockwise order; wherein, vertex 1, vertex 4, vertex 5 and vertex 8 are the lower left corner point, the lower right corner point, the upper right corner point and the upper left corner point respectively; Step 6, generate radar image: according to the 8 vertex position coordinates calculated in step 5, draw each target on the display, and set the intensity of all pixels of the Xth target to ucAmpX; Step 7, Update: When one of the MTPs is updated, repeat steps 1 to 6.

2. The radar echo rendering method based on vectorized distance detection according to claim 1, characterized in that: In step 3, let the intensity of the jth sampling point at the i-th position be amp ij , then the normalization method is: A. When amp ij When <DetThr, after normalization, it is uniformly recorded as: amp ij =0; B. When amp ij When ≥DetThr, after normalization, it is uniformly recorded as: amp ij =1.

3. The radar echo rendering method based on vectorized distance detection according to claim 1, characterized in that: In step 4, the one-dimensional distance detection method includes the following steps: Step 4-1, detecting the starting offset of the target: using the M / N detector as a sliding window, passing the N amplitude units in each video azimuth line data MTP after data normalization through the window, if M in the window is 0, the window is moved backward in sequence until M>1 appears in the window; the amplitude unit where M>1 appears for the first time is recorded as the starting offset dStartRngX of the corresponding target; Step 4-2, detecting the end offset of the target: for the target whose start offset has been found, the window continues to move backward in sequence. When M in the window is all 0, the target is considered to be ended, and the amplitude unit where M=0 appears for the first time is recorded as the end offset dEndRngX of the corresponding target; Step 4-3, calculate the maximum pixel intensity of the target: when the distance detection of a target is completed, calculate the maximum pixel intensity ucAmpX of the target at this time; Step 4-4, point trace saving: When a starting offset and an ending offset are recorded, it is considered that a 1-dimensional point trace is generated, and the 1-dimensional point trace is placed in the 1-dimensional point trace storage queue.

4. The radar echo rendering method based on vectorized distance detection according to claim 1, characterized in that: In step 5, assume that the longitude and latitude of the radar location is (φ0, λ0), and the straight-line distances from vertex 1, vertex 2, vertex 3, vertex 4, vertex 5, vertex 6, vertex 7, and vertex 8 of the Xth target in the current input MTP to the radar are d1, d2, d3, d4, d5, d6, d7, and d8, respectively; then the calculation formulas for d1 to d8 are: d1=d8=dStartRngX d2=d7=dStartRngX+0.1*dDelRngX d3=d6=dStartRng+0.9*dDelRngX d4=d5=dEndRngX in: dDelRngX=dEndRngX-dStartRngX Where dDelRngX is the length of the Xth target in the current input MTP.

5. The radar echo rendering method based on vectorized distance detection according to claim 4 is characterized in that: In step 5, the azimuths of vertex 1, vertex 2, vertex 3, vertex 4, vertex 5, vertex 6, vertex 7 and vertex 8 of the Xth target in the current input MTP to the radar are θ1, θ2, θ3, θ4, θ5, θ6, θ7 and θ8 respectively; then the calculation formulas for θ1 to θ8 are: θ1=θ4=dAzmX-dDel*0.75 θ2=θ3=dAzmX-dDel θ5=θ8=dAzmX+dDel*0.75 θ6=θ7=dAzmX+dDel Where dAzmX is the azimuth value of the Xth target in the current input MTP; dDel is the azimuth step.

6. The radar echo rendering method based on vectorized distance detection according to claim 5 is characterized in that: In step 2, the detection parameters also include the radar video azimuth quantization degree value MaxAziLines, and the calculation formula of the azimuth step dDel is: dDel=360 / 1.0 / MaxAziLines.

7. The radar echo rendering method based on vectorized distance detection according to claim 6 is characterized in that: In step 5, the calculation formula of dAzmX is: dAzmX=nIdx*360.0 / MaxAziLines Where nIdx is the position code of the Xth target in the current input MTP.

8. The radar echo rendering method based on vectorized distance detection according to claim 5, characterized in that: In step 5, the longitude and latitude coordinates of vertex 1, vertex 2, vertex 3, vertex 4, vertex 5, vertex 6, vertex 7, and vertex 8 of the Xth target in the current input MTP are (φ1, λ1), (φ2, λ2), (φ3, λ3), (φ4, λ4), (φ5, λ5), (φ6, λ6), (φ7, λ7), and (φ8, λ8), respectively. The specific calculation formula is: φ1=arcsin(sinφ0·cosδ1+cosφ0·sinδ1·cosθ1) λ1=λ0+arctan2(sinθ1·sinδ1·cosφ0, cosδ1-sinφ0·sinφ1) φ2=arcsin(sinφ0·cosδ2+cosφ0·sinδ2·cosθ2) λ2=λ0+arctan2(sinθ2·sinδ2·cosφ0, cosδ2-sinφ0·sinφ2) φ3=arcsin(sinφ0·cosδ3+cosφ0·sinδ3·cosθ3) λ3=λ0+arctan2(sinθ3·sinδ3·cosφ0, cosδ3-sinφ0·sinφ3) φ4=arcsin(sinφ0·cosδ4+cosφ0·sinδ4·cosθ4) λ4=λ0+arctan2(sinθ4·sinδ4·cosφ0, cosδ4-sinφ0·sinφ4) φ5=arcsin(sinφ0·cosδ5+cosφ0·sinδ5·cosθ5) λ5=λ0+arctan2(sinθ5·sinδ5·cosφ0, cosδ5-sinφ0·sinφ5) φ6=arcsin(sinφ0·cosδ6+cosφ0·sinδ6·cosθ6) λ6=λ0+arctan2(sinθ6·sinδ6·cosφ0, cosδ6-sinφ0·sinφ6) φ7=arcsin(sinφ0·cosδ7+cosφ0·sinδ7·cosθ7) λ7=λ0+arctan2(sinθ7·sinδ7·cosφ0, cosδ7-sinφ0·sinφ7) φ8=arcsin(sinφ0·cosδ8+cosφ0·sinδ8·cosθ8) λ8=λ0+arctan2(sinθ8·sinδ8·cosφ0, cosδ8-sinφ0·sinφ8) in: Wherein, δ1, δ2, δ3, δ4, δ5, δ6, δ7 and δ8 are the spherical angles from vertex 1, vertex 2, vertex 3, vertex 4, vertex 5, vertex 6, vertex 7 and vertex 8 of the Xth target in the current input MTP to the radar respectively; R is the radius of the earth.

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