High-precision lunar tracking method based on image processing

Abstract

The application discloses a high-precision moon tracking method based on image processing, which comprises the following steps: according to local GPS information, the height angle and the azimuth angle of the moon relative to the local position are calculated by using an astronomical algorithm and are sent to an execution module for pre-adjustment, so that the moon is preliminarily tracked; the moon target in the gray-scale image is recognized by using a Hough transform, the position of the moon center in the image and the moon radius are obtained, the weight of each pixel point in the image to the moon brightness is calculated, the weighted center is obtained, the accurate position of the moon brightness center is obtained, the azimuth deviation and the pitch angle deviation of the moon brightness center and the optical axis are calculated, and if the deviation data exceeds a preset threshold value, the deviation data is sent to the execution module for correction and adjustment. The moon image is collected in real time by using a CCD, the moon center and the radius are recognized by using an image processing method, the accurate position of the moon brightness center and the deviation of the moon brightness center and the optical axis are calculated by using parameters, the high-precision real-time tracking of the moon is realized, and the method has the advantages of high precision, rapid response and the like.

Description

Technical Field

[0001] This invention relates to the field of lunar image tracking and positioning, and more particularly to a high-precision lunar tracking method based on image processing. Background Technology

[0002] The Moon is the outermost object with the largest apparent angle besides the Sun, and its reflectivity varies at a rate of approximately 10⁻⁸ / year, exhibiting remarkably stable reflectivity characteristics. This makes the Moon highly suitable as a reference radiation source for satellites in the solar reflection band. Furthermore, the Moon can serve as a nighttime radiation source for nighttime infrared low-cloud remote sensing and atmospheric optical thickness inversion. Therefore, the measurement and study of lunar radiation characteristics has been a subject of considerable interest. Many research institutions have conducted extensive work on ground-based lunar observations, using two-dimensional turntables with spherical photometers and commercial cameras to acquire lunar spectral reflectivity data. Because the Earth and Moon are in constant relative motion, the validity of the measurement data is closely related to the accuracy of the spectroscopic instruments aligned with the Moon; therefore, the measuring instruments need to be precisely aligned with the Moon.

[0003] Currently, traditional methods for lunar tracking include astronomical algorithms and four-quadrant probe tracking. Astronomical algorithms use local GPS information, including time, longitude, latitude, and altitude, to calculate the moon's altitude and azimuth relative to the local area, achieving lunar tracking. However, this method is susceptible to atmospheric refraction and has low accuracy, with a lunar tracking accuracy of approximately ±0.5 degrees, especially when the moon's altitude angle is low. The four-quadrant probe tracking method, based on astronomical algorithms, uses a four-quadrant probe and the principle of pinhole imaging. It determines the deviation of the two-dimensional turntable in azimuth and elevation angles based on the voltage signals of the four quadrants. However, due to the low lunar irradiance, the tracking sensitivity is low, the response is slow, and it is easily affected by external light sources, resulting in still low tracking accuracy. Therefore, a high-precision lunar tracking method based on image processing is needed. Astronomical algorithms are susceptible to atmospheric refraction and have low accuracy; the four-quadrant probe tracking method, due to the low lunar irradiance, has low tracking sensitivity, slow response, and still low tracking accuracy, and is easily affected by external light sources. Summary of the Invention

[0004] The purpose of this invention is to provide a high-precision lunar tracking method based on image processing.

[0005] To achieve the above objectives, the present invention is implemented according to the following technical solution:

[0006] The first aspect of this invention provides a high-precision lunar tracking method based on image processing, comprising:

[0007] S1. Obtain the local Greenwich Observatory time, longitude, latitude and altitude information, use astronomical algorithms to calculate the current azimuth and elevation angles of the moon, calculate the rotation angles of the azimuth and elevation mechanisms of the two-dimensional turntable based on the azimuth and elevation angles, and drive the two-dimensional turntable to rotate to the initial position;

[0008] S2. Convert the lunar image at the initial position of the two-dimensional turntable into grayscale image data, and perform Gaussian filtering on the grayscale image data;

[0009] S3. Identify the lunar target in the grayscale image using Hough transform, and obtain the position of the lunar center in the image and the lunar radius;

[0010] S4. Calculate the weight of each pixel in the image to the brightness of the moon, find the weighted center, and obtain the precise location of the center of the moon's brightness;

[0011] S5. Verify the validity of the stated lunar center position. If the preset validity conditions are met, determine that the center position is valid; otherwise, return to step 1) to use an astronomical algorithm to drive the two-dimensional turntable for tracking.

[0012] S6. If the azimuth and pitch deviations between the moon's brightness center and the optical axis exceed the preset threshold of 0.05°, the deviation data is sent to the two-dimensional turntable device to drive the azimuth and pitch mechanisms to rotate and correct the angle deviations. After correction, the moon image is acquired again and steps S2)-5) are repeated. If the deviations do not exceed the threshold, it is determined that high-precision tracking of the moon has been achieved, and the acquisition operation is performed.

[0013] Furthermore, the method for calculating the weight of each pixel in the image to the brightness of the moon and obtaining the precise location of the center of the moon's brightness by calculating the weighted center specifically includes:

[0014] Calculate the weight of each pixel;

[0015] Calculate the location of the lunar center, its corresponding weight, and the total weight.

[0016] The final coordinates of the lunar center are obtained by weighted averaging.

[0017] Furthermore, the Hough transform employs a gradient method to identify circular arcs, with the accumulator set to 1 to improve accuracy. The target spacing is set to 200 to ensure the effectiveness of target identification. The Canny algorithm uses a double threshold, with the upper threshold set to 0.4 times the maximum value of 255 and the lower threshold set to 20. The maximum radius is set to 100 to match the radius of the moon and leave a margin as tolerance. The moon's position (x, y) and radius r are:

[0018] Moon(x,y,r)=Hough(IMG,Gradient,1,100,102,20,100) (6)

[0019] The data region used to calculate the center of the moon is expanded by 20 pixels, and the selected range is the expanded area that includes the pixels on the edge of the moon; low-response pixels are filtered out by taking 0.1 times the maximum gray value of 255 as the threshold and removing stray light background pixels below the threshold.

[0020] After Hough transform, the position (x, y) and radius r of the moon in the image are obtained. The data region is expanded to omit some pixels that originally belonged to the moon. The expansion size is set to 20 pixels. The array Db is used to calculate the data region Φ at the center of the moon, and the selected range is:

[0021] Φ={0≤xr-20≤i≤x+r+20<1280,0≤yr-20≤j≤y+r+20<960} (7)

[0022] Furthermore, the formula for calculating the pixel weight is as follows:

[0023] W(i,j)=Db(i,j) / 255 (i,j∈Φ) (9)

[0024] Next, the weights Wx and Wy corresponding to the lunar center positions x and y, as well as the total weight Wt, are calculated:

[0025] Wx=∑i·W(i,j) (i,j∈Φ) (10)

[0026] Wy=∑j·W(i,j) (i,j∈Φ) (11)

[0027] Wt=Wt+W(i,j) (i,j∈Φ) (12)

[0028] Finally, by using a weighted average of the coordinates, the final lunar center positions x' and y' are obtained:

[0029] x'=Wx / Wt (13)

[0030] y'=Wy / Wt (14).

[0031] Furthermore, the formulas for calculating the azimuth deviation Δα and the elevation deviation Δβ are as follows:

[0032]

[0033] Set a deviation threshold T of 0.05°. If |Δα|≤T, the azimuth direction is considered to have no deviation; if |Δβ|≤T, the pitch direction is considered to have no deviation.

[0034] Furthermore, the azimuth and elevation deviations of the lunar brightness center from the optical axis are calculated. Based on the CD pixel center distance and objective lens focal length, the deviation angles of the moon relative to the optical axis in the vertical and horizontal directions are calculated using the arctangent function.

[0035] On the other hand, a high-precision lunar tracking system based on image processing includes:

[0036] The GPS module is used to obtain the local Greenwich Observatory time, longitude, latitude, and altitude information.

[0037] The image processing module acquires a lunar image of the initial position of the two-dimensional turntable of the execution module, converts it into grayscale image data, and performs Gaussian filtering on the grayscale image data.

[0038] The determination module is used to calculate the moon's altitude and azimuth relative to the local area using astronomical algorithms based on local GPS information and send them to the execution module for pre-adjustment to achieve initial tracking of the moon. It identifies the lunar target in the grayscale image through Hough transform, obtains the position of the moon's center in the image and the moon's radius, calculates the weight of each pixel in the image to obtain the weighted center to obtain the precise position of the moon's brightness center, and calculates the azimuth and pitch deviations between the moon's brightness center and the optical axis. If the deviation data exceeds the preset threshold, it sends the deviation data to the execution module for correction and adjustment.

[0039] The execution module includes a two-dimensional turntable with independent azimuth and pitch mechanisms. It is used to drive the load to rotate the azimuth and pitch angles according to the instructions of the judgment module, and communicates with the judgment module to receive instructions and provide feedback on the motion status.

[0040] Furthermore, lunar images are acquired in real time by a CCD module mounted on a two-dimensional turntable. The CCD module includes an imaging objective lens and a CCD image sensor. The imaging objective lens is an achromatic lens, and the CCD image sensor is a 1 / 8-inch, 1280×960 pixel, 16-bit grayscale area array sensor.

[0041] Furthermore, the orientation mechanism of the two-dimensional turntable has a rotation range of -5 to 350°, and the pitch mechanism has a rotation range of -5 to 100°.

[0042] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0043] The method of this invention acquires lunar images in real time using a CCD and identifies the center and radius of the moon through image processing. Taking advantage of the small geometric size of CCD pixels, it calculates the precise position of the moon's brightness center and its deviation from the optical axis using parameters, and drives a two-dimensional turntable to achieve high-precision real-time tracking of the moon. Compared with traditional methods, this invention has the advantages of high precision and rapid response. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating the steps of a high-precision lunar tracking method based on image processing in an embodiment of the present invention.

[0045] Figure 2 This is a hardware structure diagram of a high-precision lunar tracking method based on image processing in an embodiment of the present invention. Detailed Implementation

[0046] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0047] Reference Figure 1 and 2 As shown, this invention provides a high-precision lunar tracking method based on image processing, comprising:

[0048] S1. Obtain the local Greenwich Observatory time, longitude, latitude and altitude information, use astronomical algorithms to calculate the current azimuth and elevation angles of the moon, calculate the rotation angles of the azimuth and elevation mechanisms of the two-dimensional turntable based on the azimuth and elevation angles, and drive the two-dimensional turntable to rotate to the initial position;

[0049] S2. Convert the lunar image at the initial position of the two-dimensional turntable into grayscale image data, and perform Gaussian filtering on the grayscale image data;

[0050] S3. Identify the lunar target in the grayscale image using Hough transform, and obtain the position of the lunar center in the image and the lunar radius;

[0051] S4. Calculate the weight of each pixel in the image to the brightness of the moon, find the weighted center, and obtain the precise location of the center of the moon's brightness;

[0052] S5. Verify the validity of the stated lunar center position. If the preset validity conditions are met, determine that the center position is valid; otherwise, return to step 1) to use an astronomical algorithm to drive the two-dimensional turntable for tracking.

[0053] S6. If the azimuth and pitch deviations between the moon's brightness center and the optical axis exceed the preset threshold of 0.05°, the deviation data is sent to the two-dimensional turntable device to drive the azimuth and pitch mechanisms to rotate and correct the angle deviations. After correction, the moon image is acquired again and steps S2)-5) are repeated. If the deviations do not exceed the threshold, it is determined that high-precision tracking of the moon has been achieved, and the acquisition operation is performed.

[0054] The system block diagram of the present invention is as follows: Figure 2 As shown, it mainly includes an image sensor (CCD) imaging module, a global positioning system (GPS) module, a microprocessor (PC104) module, and a two-dimensional turntable device.

[0055] [Beginning of the document][Automatic section break](1)GPS module

[0056] This technology uses a GPS module to acquire local GMT (Greenwich Mean Time), longitude, latitude, and altitude information in real time for coarse positioning of the lunar altitude and azimuth. The GPS module selected in this system uses a high-sensitivity chipset, capable of receiving signals from up to 20 satellites with high performance, enabling fast and accurate acquisition of positioning data. This module is integrated into a two-dimensional turntable device.

[0057] (2) CCD module

[0058] This technology employs a CCD module, including an imaging objective and a CCD image sensor, mounted on a two-dimensional turntable. It serves as one of the loads on the turntable, and its optical axis is parallel to the optical axis of the spectral measurement instrument mounted on it. The imaging objective selected in this system has a diameter of 25.4 mm and a focal length of 50 mm, and is an achromatic lens composed of N-BAF10 and SF10 glass materials. The selected CCD image sensor is 1 / 8 inch in size, with a pixel count of 1280×960 and a sampling bit depth of 16-bit grayscale, exhibiting high response sensitivity.

[0059] (3) PC104 module

[0060] This technology utilizes a PC104 module with a processor boasting a 1.83GHz clock speed and 4GB of memory, enabling rapid processing of lunar images acquired by the CCD module. It serves as the core computing unit of the entire system. This module supports multiple RS-232 and USB interfaces, facilitating communication with the CCD module and the 2D turntable.

[0061] (3) Two-dimensional turntable device

[0062] This technology employs a two-dimensional turntable device to drive the load for lunar tracking. It comprises two independent drive mechanisms, each capable of controlling the load's rotation in azimuth and pitch angles. Through communication with the PC104 module, it receives commands, performs movements, and provides corresponding signal feedback. The azimuth mechanism has a rotation range of -5° to 350°, and the pitch mechanism has a rotation range of -5° to 100°, meeting the rotation range requirements for lunar tracking.

[0063] In this invention, the software backend mainly performs operations such as GPS signal collection, image processing, lunar brightness center analysis, and azimuth and elevation angle deviation calculation, ultimately driving the two-dimensional turntable device to achieve high-precision lunar tracking. The specific implementation method is as follows:

[0064] (1) Lunar tracking using a two-dimensional turntable

[0065] The lunar tracking of the two-dimensional turntable is driven by GPS signals or lunar images acquired by CCD, with priority given to using lunar images for tracking.

[0066] (a) Initial Tracking of the 2D Turntable: When the 2D turntable is in the zero position, the optical axis of the CCD module's objective lens points due north. At this time, the moon cannot enter the objective lens's field of view, so lunar images cannot be acquired. Based on the time, longitude, latitude, and altitude information from the GPS signal, an astronomical algorithm is used to calculate the current lunar azimuth angle Az and elevation angle Ei. Let the current azimuth and elevation angles of the 2D turntable be α and β, respectively. Then, the angles Δα and Δβ that the azimuth and elevation mechanisms of the 2D turntable need to rotate are:

[0067] Δα=Az-α (1)

[0068] Δβ=Ei-β (2)

[0069] (b) When the moon is outside the field of view of the objective lens of the CCD module or is obscured by clouds, and there is no image of the moon in the acquired image, (a) is still used to drive the two-dimensional turntable to track the moon.

[0070] (c) When the moon is within the objective field of view of the CCD module and the acquired image contains a complete image of the moon, then (2) is used to drive the two-dimensional turntable to track the moon.

[0071] (2) Lunar image processing

[0072] The lunar images acquired by the CCD module need to be transmitted to the PC104 module for a series of processing steps. The module calculates the required rotation angles Δα and Δβ for the azimuth and pitch mechanisms of the two-dimensional turntable, drives the two-dimensional turntable to achieve high-precision lunar tracking, and completes the corresponding scientific data acquisition.

[0073] (a) Generating an image

[0074] The raw data acquired by the CCD module is 1280×960 pixels with a sampling precision of 16 bits, denoted as CCD(1280, 960). Db(1280, 960) is taken as a double-precision array, and equation (3) is used to convert the 16-bit binary data into double-precision data:

[0075]

[0076] Simultaneously, a grayscale image data IMG(1280, 960) is generated and converted using equation (4).

[0077] IMG(i,j)=(int)Db(i,j) (0≤i<1280,0≤j<960) (4)

[0078] (b) Gaussian filtering

[0079] Gaussian filtering is applied to the grayscale image IMG with a filter window size of 5×5 and a standard deviation of 2.

[0080] IMG=Gaussian(IMG,size(5,5),2) (5)

[0081] The lunar image after Gaussian filtering can effectively remove noise interference, which helps to improve the accuracy of identifying the lunar edge and obtain the accurate lunar center and radius.

[0082] (c) Hough Transform

[0083] A Hough transform is performed on the grayscale image IMG to identify the lunar target in the image and obtain the position of the lunar center and the lunar radius. Based on the lens parameters of the CCD module, the maximum lunar radius in the image is approximately 62 pixels, and the lunar edge is an arc. Therefore, the Hough transform uses a gradient method to identify the arc. The accumulator is set to 1 to improve accuracy, and the target spacing is set to 200 to ensure the effectiveness of target identification. The Canny algorithm uses a double threshold, with the upper threshold set to 0.4 times the maximum value of 255, the lower threshold set to 20, and the maximum radius set to 100 to match the lunar radius and leave a certain margin for tolerance. The lunar position (x, y) and radius r are:

[0084] Moon(x,y,r)=Hough(IMG,Gradient,1,100,102,20,100) (6)

[0085] (c) Extended data area

[0086] After Hough transform, the position (x, y) and radius r of the moon in the image are obtained. To ensure the accuracy of the final moon center position, the data region is expanded to omit some pixels that originally belonged to the moon in the image. The expansion size is set to 20 pixels. Therefore, the selection range of the data region Φ used by the array Db to calculate the moon center is:

[0087] Φ={0≤xr-20≤i≤x+r+20<1280,0≤yr-20≤j≤y+r+20<960} (7)

[0088] (d) Threshold transformation to determine valid data

[0089] To reduce the impact of stray light backgrounds such as city lights at night, it is necessary to filter out some low-response pixels. Therefore, 0.1 times the maximum value of 255 is taken as the threshold.

[0090]

[0091] (e) Coordinate-weighted average

[0092] Because the lunar surface is not uniformly bright, the response values ​​of each pixel in the corresponding lunar image are also different. It is necessary to weight each pixel based on its contribution to the brightness, and then use the sum of these weights to obtain the precise location of the lunar brightness center. First, the weight W(i, j) of each pixel is calculated:

[0093] W(i,j)=Db(i,j) / 255 (i,j∈Φ) (9)

[0094] Next, the weights Wx and Wy corresponding to the lunar center positions x and y, as well as the total weight Wt, are calculated:

[0095] Wx=∑i·W(i,j) (i,j∈Φ) (10)

[0096] Wy=∑j·W(i,j) (i,j∈Φ) (11)

[0097] Wt=Wt+W(i,j) (i,j∈Φ) (12)

[0098] Finally, by using a weighted average of the coordinates, the final lunar center positions x' and y' are obtained:

[0099] x'=Wx / Wt (13)

[0100] y'=Wy / Wt (14)

[0101] (f) Validity test of the lunar brightness center

[0102] To ensure the accuracy and validity of the lunar center position and improve the stability and reliability of lunar tracking, it is necessary to determine the validity of the lunar center positions x' and y':

[0103] efficient:

[0104] When x' and y' simultaneously satisfy equation (15), the center position is considered valid; otherwise, it is considered invalid.

[0105] When the center position is invalid, the two-dimensional turntable is driven by (1)(a) to track the moon.

[0106] When the center position is valid, calculate whether there is a deviation from the current azimuth and elevation angles based on the system parameters.

[0107] (g) Calculation of azimuth and elevation angle deviations

[0108] The azimuth deviation Δα and elevation deviation Δβ are calculated based on the positions of the lunar center x' and y' within the entire image. The total image resolution is 1280×960 pixels, therefore (640, 480) is chosen as the image center. The objective lens focal length f is 50mm, the pixels on the CCD detector are square, and the pixel center distance d is 0.00375mm. The formulas for calculating the azimuth deviation Δα and elevation deviation Δβ are as follows:

[0109]

[0110] Because the Moon and Earth are in relative motion, perfectly precise pointing to the Moon is impossible. A small deviation threshold T is typically set (e.g., T = 0.05°). If |Δα| ≤ T, the azimuth direction is considered to be without deviation; if |Δβ| ≤ T, the pitch direction is considered to be without deviation. Higher lunar tracking accuracy can be achieved by reducing the deviation threshold T.

[0111] (h) Handling of azimuth and elevation deviations

[0112] When |Δα|>T or |Δβ|>T, there is a deviation in the azimuth or elevation direction. The PC104 module sends this deviation data to the two-dimensional turntable device, driving it to rotate in the corresponding direction to correct the angular deviation. After the two-dimensional turntable completes its movement, it triggers the CCD module to acquire lunar images again, and this process is repeated continuously.

[0113] When both |Δα|≤T and |Δβ|≤T are satisfied, there are no deviations in the azimuth and elevation directions, achieving high-precision tracking of the moon. At this time, the PC104 module communicates with the accompanying spectroscopic instrument to perform operations such as spectral data acquisition and storage, completing the data acquisition work required for scientific research. After these tasks are completed, the CCD module is triggered to acquire lunar images again, and this process is continuously repeated.

[0114] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A high-precision lunar tracking method based on image processing, characterized in that, S1. Obtain the local Greenwich Observatory time, longitude, latitude and altitude information, use astronomical algorithms to calculate the current azimuth and elevation angles of the moon, calculate the rotation angles of the azimuth and elevation mechanisms of the two-dimensional turntable based on the azimuth and elevation angles, and drive the two-dimensional turntable to rotate to the initial position; S2. Convert the lunar image at the initial position of the two-dimensional turntable into grayscale image data, and perform Gaussian filtering on the grayscale image data; S3. Identify the lunar target in the grayscale image using Hough transform, and obtain the position of the lunar center in the image and the lunar radius; S4. Calculate the weight of each pixel in the image to the brightness of the moon, find the weighted center, and obtain the precise location of the center of the moon's brightness; S5. Verify the validity of the position of the lunar center in the image. If the preset validity conditions are met, determine that the center position is valid. Otherwise, return to step 1) and use an astronomical algorithm to drive the two-dimensional turntable tracking; S6. If the azimuth and pitch deviations between the moon's brightness center and the optical axis exceed the preset threshold of 0.05°, the deviation data is sent to the two-dimensional turntable device to drive the azimuth and pitch mechanisms to rotate and correct the angle deviations. After correction, acquire lunar images again and repeat steps S2)-5); If the target value is not exceeded, it is determined that high-precision tracking of the moon has been achieved, and data acquisition is performed.

2. The high-precision lunar tracking method based on image processing according to claim 1, characterized in that, The method for calculating the weight of each pixel in the image to the brightness of the moon and obtaining the precise location of the center of the moon's brightness by calculating the weighted center includes: Calculate the weight of each pixel; Calculate the location of the lunar center, its corresponding weight, and the total weight. The final coordinates of the lunar center are obtained by weighted averaging.

3. The high-precision lunar tracking method based on image processing according to claim 1, characterized in that, The Hough transform uses a gradient method to identify circular arcs. An accumulator of 1 is used to improve accuracy, and the target spacing is set to 200 to ensure effective target identification. The Canny algorithm uses a dual threshold: an upper threshold of 0.4 times the maximum value of 255, a lower threshold of 20, and a maximum radius of 100 to match the radius of the moon while allowing for a margin of error. The moon's position... and radius for (1); The data region used to calculate the center of the moon is expanded by 20 pixels, and the selected range is the expanded area that includes the pixels on the edge of the moon; low-response pixels are filtered out by taking 0.1 times the maximum gray value of 255 as the threshold and removing stray light background pixels below the threshold. The position of the moon in the image is obtained after Hough transform. and radius The data area is expanded to omit some pixels in the image that originally belonged to the moon. The expansion size is set to 20 pixels. Data area used to calculate the center of the moon The selection range is: (2); array It is a conversion of the raw data collected by the CCD module.

4. The high-precision lunar tracking method based on image processing according to claim 1, characterized in that, The azimuth and elevation deviations of the moon's brightness center relative to the optical axis are calculated. Based on the CCD pixel center distance and objective lens focal length, the deviation angles of the moon relative to the optical axis in the vertical and horizontal directions are calculated using the arctangent function.

5. A high-precision lunar tracking system based on image processing, characterized in that, include: The GPS module is used to obtain the local Greenwich Observatory time, longitude, latitude, and altitude information; The image processing module acquires a lunar image of the initial position of the two-dimensional turntable of the execution module, converts it into grayscale image data, and performs Gaussian filtering on the grayscale image data. The determination module is used to calculate the moon's altitude and azimuth relative to the local area using astronomical algorithms based on local GPS information and send them to the execution module for pre-adjustment to achieve initial tracking of the moon. It identifies the lunar target in the grayscale image through Hough transform, obtains the position of the moon's center in the image and the moon's radius, calculates the weight of each pixel in the image to obtain the weighted center to obtain the precise position of the moon's brightness center, and calculates the azimuth and pitch deviations between the moon's brightness center and the optical axis. If the deviation data exceeds the preset threshold, it sends the deviation data to the execution module for correction and adjustment. The execution module includes a two-dimensional turntable with independent azimuth and pitch mechanisms. It is used to drive the load to rotate the azimuth and pitch angles according to the instructions of the judgment module, and communicates with the judgment module to receive instructions and provide feedback on the motion status.

6. The high-precision lunar tracking system based on image processing according to claim 5, characterized in that, Lunar images are acquired in real time by a CCD module mounted on a two-dimensional turntable. The CCD module includes an imaging objective lens and a CCD image sensor. The imaging objective lens is an achromatic lens, and the CCD image sensor is a 1 / 8-inch, 1280×960 pixel, 16-bit grayscale area array sensor.

7. The high-precision lunar tracking system based on image processing according to claim 5, characterized in that, The orientation mechanism of the two-dimensional turntable has a rotation range of -5 to 350°, and the pitch mechanism has a rotation range of -5 to 100°.

Images