A method for judging the number of visible satellites in a global navigation satellite system at a measurement control point

Abstract

The present invention discloses a method for judging the number of visible satellites of a global navigation satellite system at a measurement control point, including: taking surround photos at a proposed measurement control point and splicing them into a 360° surround image, and transforming the projection into a spherical projection map; identifying the spherical projection map, distinguishing between obstructions and sky areas, and generating a mask layer; using an ephemeris file to draw a multi-constellation satellite sky map of the global navigation satellite system at the control point; superimposing the mask layer on the satellite trajectory sky map, analyzing and calculating the number of visible satellites of each constellation in the sky range; and looping the above steps to batch generate a table of changes in the number of visible satellites within a specified time period of a certain day for multiple global navigation satellite system control points. The present invention overcomes the time-consuming and labor-intensive disadvantages of traditional field testing methods, and uses spherical projection of surround images to generate a mask layer, which is superimposed on a multi-constellation satellite trajectory sky map to analyze the number of visible satellites of the global navigation satellite system at the measurement control point.

Description

Technical Field

[0001] The present invention relates to the field of global navigation satellite system measurement, and more specifically, to a method for judging the number of visible satellites of a global navigation satellite system at a measurement control point. Background Art

[0002] When using the global navigation satellite system for control measurement, it is usually necessary to track observation conditions of no less than 4 satellites at an altitude angle of more than 15°. In order to resist the error of adverse factors such as multipath effect, the more satellites that can be tracked and received, the better, so as to ensure accuracy and reliability. However, when selecting control points in urban built-up areas or high mountain valleys, obstructions such as buildings, tall trees and mountains will affect the reception of satellite signals. The visible altitude angle in some directions may be much greater than 15°, so that these directions cannot receive global navigation satellite system satellite signals or the time for receiving global navigation satellite system satellite signals is insufficient. In some time periods, it is difficult to meet the global navigation satellite system satellite observation requirements at the same time. The traditional method for selecting global navigation satellite system control points that meet the conditions is to analyze the observation data after conducting global navigation satellite system tests on the spot to obtain the visible satellite situation, but this method is very time-consuming and labor-intensive. At the same time, this method has little guiding significance when the single-point field test is different from the official observation time of the control network. Summary of the invention

[0003] The purpose of the present invention is to provide a method for judging the number of visible satellites of the global navigation satellite system at a measurement control point, so as to overcome the time-consuming and labor-intensive disadvantages of the traditional field testing method, generate a mask layer by spherical projection of a panoramic image, superimpose it on a sky map of the multi-constellation satellite orbits, and analyze the number of visible satellites of the global navigation satellite system at the measurement control point.

[0004] The technical solution adopted by the present invention to solve this technical problem is: a method for judging the number of visible satellites of a global navigation satellite system at a measurement control point, comprising the following steps:

[0005] S1. At the proposed measurement control point, take panoramic photos with a standing camera, stitch them into a 360° panoramic image, intercept the image of the range above the horizontal plane of the antenna center after the global navigation satellite system receiver is set up during the estimated control measurement, and perform spherical equiangular projection to form a spherical projection map;

[0006] S2, identifying the spherical projection image, distinguishing the occlusion and the sky area, classifying them into complete occlusion, partial occlusion and sky area, and generating a mask layer;

[0007] S3, generating a GNSS multi-constellation satellite trajectory sky map at a specified time point or time period on a certain day at the location of the measurement control point using the predicted ephemeris, placing it under the above-mentioned mask layer, and calculating the number of visible satellites of each constellation in the sky range;

[0008] S4. According to the above steps, a table of changes in the number of visible satellites of the global navigation satellite system for multiple measurement control points within a certain day and a certain period of time is generated in batches in a loop.

[0009] Preferably, the 360° surround view image in S1 is synthesized by taking photos or generated according to an existing three-dimensional model.

[0010] Preferably, the image captured from the horizontal line to a certain range above in step S1 is specifically an image from the horizontal line of the shooting position to the zenith or an image from the horizontal line to the highest obstruction.

[0011] Preferably, in step S1, the altitude angle of the highest obstruction is recorded after intercepting the image from the horizontal line to a certain upper range, wherein the altitude angle of the highest obstruction is obtained by recording the altitude angle by aiming at the top of the highest obstruction at the measurement control point or by calculating the plane distance and height difference measured in the three-dimensional model, and the range from the highest obstacle to the zenith is filled with representative colors of the sky area.

[0012] Preferably, in step S2, a threshold segmentation algorithm / deep learning method is used to distinguish between the obstruction and the sky area.

[0013] Preferably, the step S3 specifically includes:

[0014] Use the predicted ephemeris data to calculate the altitude and azimuth of each satellite at the control point within a specified time period;

[0015] According to the occlusion in the mask layer, each satellite is divided into three situations: full occlusion, partial occlusion or full visibility;

[0016] According to the condition that the altitude angle is greater than 15°, the number of visible satellites is counted and the results are recorded in the data table;

[0017] Batch generate a table of the number of visible satellites for multiple control points.

[0018] The present invention has at least the following beneficial effects:

[0019] 1. Assist surveyors to judge the conditions for tracking and observing global navigation satellite system satellites at the measurement control points, and check whether they meet the requirements of specifications and technical documents. It can more accurately evaluate the visibility of control points, avoid the problem of field measurement required by traditional methods, save a lot of time and human resources, and has a high degree of automation;

[0020] 2. Computer vision algorithms are used to identify obstructions around control points, generate mask layers, and overlay them on the visible satellite ring view. This can achieve efficient multi-point and multi-period visibility assessment, and help surveyors to quantitatively compare and select favorable observation positions and appropriate observation periods between multiple measurement control points based on the number of global navigation satellite system satellites tracked and observed, which can effectively improve measurement efficiency and accuracy.

[0021] Other advantages, objectives and features of the present invention will be embodied in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a panoramic image diagram of the surrounding environment of the measurement control point of the present invention;

[0023] Figure 2 It is a spherical projection diagram of the surrounding environment of the measurement control point of the present invention;

[0024] Figure 3 It is a spherical projection binary image of the surrounding environment of the measurement control point of the present invention;

[0025] Figure 4 A sky map of satellites of a global navigation satellite system visible at a certain time period of a measurement control point of the present invention;

[0026] Figure 5 The spherical projection binary image of the present invention is superimposed with the visible satellite sky image. DETAILED DESCRIPTION

[0027] The present invention is described in detail and completely below in conjunction with the accompanying drawings. A person of ordinary skill in the art will be able to implement the present invention based on these descriptions. Before describing the present invention in conjunction with the accompanying drawings, it should be particularly pointed out that the technical solutions and technical features provided in each part of the present invention, including the following description, can be combined with each other without conflict.

[0028] In addition, the embodiments of the present invention involved in the following description are generally only a part of the embodiments of the present invention, rather than all the embodiments. Therefore, based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0029] Example

[0030] Step 1: The surveyor takes photos with a mobile phone at a proposed measurement control point to synthesize a 360° panoramic image

[0031] In order to implement the control point observation condition evaluation method of this technology, the first step is to take a set of 360-degree panoramic photos near the proposed measurement control point. These photos will capture the entire visible area around the control point. These photos can be taken with a smartphone or other camera device, but it is necessary to ensure that the photos can capture the location of the control point. It is recommended to use a camera with a pixel count of not less than 20 million to obtain better image quality.

[0032] When taking photos, you need to ensure that the overlapping area of ​​the photos is as large as possible, so that they can be stitched into a complete 360-degree panoramic image during subsequent processing. It is generally recommended that the overlapping area in the horizontal direction is not less than 30%, and the overlapping area in the vertical direction is not less than 20%. In order to ensure the quality of the photos, it is recommended to manually focus and expose before shooting.

[0033] After the shooting is completed, you need to use the corresponding image processing software to stitch the photos into a 360-degree panoramic image, such as Figure 1 As shown. You can use the panorama stitching software on the market, or you can write your own stitching algorithm. After the stitching is completed, the image needs to be corrected and processed to ensure the accuracy of the spherical equiangular projection.

[0034] After shooting, use the mobile phone (built-in tilt sensor) to aim at the top of the obstacle with the maximum obstruction height angle around the measurement control point, read and record the vertical tilt data as the obstruction height angle of the obstacle. Use the navigation and positioning software of the mobile phone (built-in GNSS sensor) to observe and record the approximate longitude and latitude of the measurement control point.

[0035] Editing the stitched panoramic image mainly includes two operations: first, intercepting the part above the horizontal line of the height of the mobile phone when shooting, and recording the pixel height of the intercepted image; second, using the mobile phone's tilt sensor to obtain the occlusion height angle of the obstacle, calculating the "pixel height / elevation angle" ratio, and filling the occlusion height angle corresponding to the highest obstacle to 90° in the range according to the above ratio with a supplementary sky area of ​​sufficient pixel height. The color of the supplementary sky area is the same as the representative color of the sky in the stitched panoramic image, and the final 360-degree panoramic image is obtained after editing;

[0036] We can use the image processing technology in computer vision to analyze the 360-degree surround image by spherical conformal projection. We can use the existing image processing library or write our own algorithm. This can be achieved through the spherical conformal projection algorithm to convert the pixel coordinates of the spherical projection image into the pixel coordinates of the plane image.

[0037] Step 2: Using Computer Vision for Occlusion Recognition

[0038] In step 1, the 360° panoramic image around the control point is obtained as a plane image after spherical projection, as shown in Figure 2 As shown, the spherical projection image is now binarized, the occluder and the sky are separated, and the occluder mask layer is generated, as shown in Figure 3 As shown. The image can be divided into two parts using a threshold segmentation algorithm: the sky and the occluder. Traditional image segmentation algorithms such as threshold segmentation, edge detection, etc. can be used here, or deep learning techniques can be used for segmentation. The choice of thresholds needs to be adjusted according to the shape and size of the occluder to ensure that the occluder and sky areas are correctly separated. Occluders can be further subdivided into complete occluders (such as houses), partial occluders (such as sparsely leafed trees, iron towers), and sky areas. To achieve this step, morphological operation techniques in image processing can be used, such as dilation, erosion, and opening and closing operations. These operations can be adjusted according to the shape and size of the occluder to ensure that the complete occlusion, partial occlusion, and complete visibility areas are correctly separated.

[0039] Finally, we will get a mask layer, in which non-zero pixels form a color block to indicate complete occlusion, non-zero pixels form a closed area with 0 pixels inside to indicate partial occlusion, and continuous 0 pixels form a color block to indicate a fully visible area.

[0040] Step 3: If Figures 3 to 5 As shown in the figure, the sky map of the navigation satellite system is drawn based on the predicted ephemeris, and the number of visible satellites is analyzed in combination with the mask.

[0041] After generating the mask layer in step 2, we need to combine it with the multi-constellation satellite trajectory sky map to calculate the number of visible satellites at a specified time of day at the control point location. Visible satellites are satellites with an altitude angle greater than a certain angle (such as 15°).

[0042] First, the predicted ephemeris data is used to calculate the altitude and azimuth of each satellite in the navigation satellite system at the measurement control point within a specified time period. The altitude cutoff angle (usually 15°) and certain satellite constellations can be customized as needed, and ephemeris prediction software or Python and other languages ​​can be used to draw sky maps of navigation satellite systems such as GPS, GLONASS, GALILEO and BEIDOU. Figure 4 As shown. Then, the mask layer is overlaid to analyze the occlusion situation. Through layer overlay, we can determine which areas or directions of the control points can continuously receive global navigation satellite system signals, which can receive partial satellite signals, and which cannot receive satellite signals. Each satellite is divided into three situations: full occlusion, partial occlusion, or full visibility.

[0043] Finally, based on the condition that the altitude angle is greater than 15°, the number of visible satellites is counted through superposition analysis, such as Figure 5 as shown and record the results in the data table.

[0044] Step 4: Generate a table of the number of visible satellites for multiple control points in batches to guide surveyors in selecting appropriate observation periods

[0045] The table of satellite numbers visible at multiple control points in several time periods generated in step 3 can guide surveyors to choose appropriate observation periods. Surveyors select multiple proposed survey control points according to actual conditions to take photos, synthesize and edit the 360° panoramic image of each control point, and use Python and other programming languages ​​to automatically batch generate a table of satellite numbers visible at multiple control points in the same time period based on the approximate longitude and latitude of the control points and the 360° panoramic image. Based on this, surveyors can choose a period with a large number of visible satellites for field static measurement, thereby improving measurement efficiency and accuracy.

[0046] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the embodiments shown and described herein.

Claims

1. A method for judging the number of visible satellites of a global navigation satellite system at a measurement control point, characterized in that: The following steps are involved: S1. Take panoramic photos with a standing camera at the proposed measurement control point, stitch them into a 360° panoramic image, intercept the image of the range above the horizontal plane of the center of the global navigation satellite system receiver antenna, and perform spherical conformal projection to form a spherical projection map; in the step S1, after intercepting the image from the horizontal line to a certain range above, record the altitude angle of the highest obstruction, wherein the altitude angle of the highest obstruction is obtained by recording the altitude angle at the measurement control point to the top of the highest obstruction or by calculating the plane distance and height difference measured in the three-dimensional model, and the range from the highest obstacle to the zenith is filled with sky color; S2, identifying the spherical projection image, distinguishing the occlusion and the sky area, classifying them into complete occlusion, partial occlusion and sky area, and generating a mask layer; S3, using the predicted ephemeris to generate a multi-constellation satellite trajectory sky map at a specified time point or time period on a certain day at the location of the measurement control point, placing it under the above-mentioned mask layer, and calculating the number of visible satellites of each constellation in the sky range; S4. According to the above steps, a table of changes in the number of visible satellites of the global navigation satellite system for multiple measurement control points within a certain day and a certain period of time is generated in batches in a loop.

2. The method for judging the number of visible satellites of the global navigation satellite system at a measurement control point according to claim 1, characterized in that: The 360° surround view image in S1 is synthesized by taking photos or generated according to an existing three-dimensional model.

3. The method for judging the number of visible satellites of the global navigation satellite system at a measurement control point according to claim 1, characterized in that: The image captured from the horizontal line to a certain range above in step S1 is specifically an image from the horizontal line of the shooting position to the zenith or an image from the horizontal line to the highest obstruction.

4. The method for judging the number of visible satellites of the global navigation satellite system at a measurement control point according to claim 1, characterized in that: In step S2, a threshold segmentation algorithm / deep learning method is used to distinguish between the obstruction and the sky area.

5. The method for judging the number of visible satellites of the global navigation satellite system at a measurement control point according to claim 1, characterized in that: The step S3 specifically includes: Use the predicted ephemeris data to calculate the altitude and azimuth of each satellite at the control point within a specified time period; According to the occlusion in the mask layer, each satellite is divided into three situations: full occlusion, partial occlusion or full visibility; According to the condition that the altitude angle is greater than 15°, the number of visible satellites is counted and the results are recorded in the data table; Batch generate a table of the number of visible satellites for multiple control points.

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