A beacon light centroid positioning method, device and equipment based on improved wavelet threshold in optical communication

Abstract

The application discloses a beacon light centroid positioning method and device based on improved wavelet threshold in optical communication, and relates to the technical field of optical communication. The method comprises the following steps: acquiring multiple beacon light images; performing median filtering processing and discrete wavelet transform on each image, acquiring multiple wavelet decomposition multi-layer coefficient matrices, and obtaining a denoising matrix by using a VisuShrink method; performing inverse discrete wavelet transform on the denoising matrix to obtain multiple denoising light spot images; calculating the centroid coordinates of light spots in each denoising light spot image by using a gray centroid method; and performing summation and average processing on the centroid coordinates of the multiple images to obtain beacon light centroid positioning coordinates. By improving the wavelet threshold processing, the application effectively removes noise interference, improves the accuracy and stability of beacon light centroid positioning, and provides reliable technical support for accurate positioning in optical communication.

Description

Technical Field

[0001] This application relates to the field of optical communication technology, and in particular to a method, apparatus and equipment for beacon centroid localization in optical communication based on an improved wavelet threshold. Background Technology

[0002] With the rapid development of aerospace technology and increasingly stringent requirements for the speed, distance, and stability of space communication, satellite optical communication, with its significant advantages such as high speed, ultra-long distance, low power consumption, and strong anti-interference, has become a core technological support for building an integrated space-ground information network. As a key signal for alignment in satellite optical communication links, the centroid positioning accuracy of beacon light directly determines whether the communication terminal can achieve dynamic alignment at the micro-arc level in complex space environments, and is a key technical indicator for ensuring the stable and reliable operation of satellite optical communication systems.

[0003] However, in actual operation, satellite optical communication systems face a complex mix of interference sources, including pointing deviations caused by micro-vibrations and orbital maneuvers of the satellite platform, beam jitter and distortion caused by atmospheric turbulence, and strong interference from space background light. In related technologies, beacon centroid positioning methods primarily rely on the direct output of the beam grayscale distribution from a photodetector, calculating position coordinates using a weighted centroid algorithm. However, under the aforementioned complex interference scenarios, these technologies suffer from the following significant shortcomings: the beam image is severely contaminated by noise, leading to large deviations in centroid calculation; it cannot effectively distinguish between valid signals and interference noise; and it struggles to balance millisecond-level response speeds with sub-microradian-level positioning accuracy under dynamic disturbances, resulting in frequent laser link interruptions and excessively long link recovery times.

[0004] Therefore, this application proposes a beacon centroid localization method in optical communication based on improved wavelet thresholding to solve the above problems, improve the accuracy and stability of beacon centroid localization, and ensure the efficient operation of satellite optical communication systems. Summary of the Invention

[0005] To improve the beam distortion caused by atmospheric turbulence in satellite optical communication links, achieve stable operation of optical communication, and enable fast, accurate, and stable connection and maintenance of laser links, this application aims to provide a method, apparatus, and device for beacon beam centroid localization in optical communication based on an improved wavelet thresholding method, leveraging the unique advantages of wavelet thresholding for image denoising. This method can quickly identify the centroid coordinates of the beam in beacon beam images, thereby ensuring the stability and reliability of optical communication laser links.

[0006] To achieve the above objectives, this application provides the following solution:

[0007] In a first aspect, this application provides a method for beacon centroid localization in optical communication based on an improved wavelet threshold, comprising:

[0008] Acquire multiple beacon light images;

[0009] Median filtering and discrete wavelet transform are performed on each beacon light image to obtain the wavelet decomposition multi-layer coefficient matrix corresponding to each beacon light image;

[0010] The VisuShrink method is used to denoise each wavelet decomposition multi-level coefficient matrix separately, resulting in multiple wavelet decomposition multi-level coefficient denoising matrices.

[0011] Discrete wavelet inverse transform is performed on each wavelet decomposition multi-layer coefficient denoising matrix to obtain multiple denoised spot images;

[0012] The centroid coordinates of the light spot in each denoised light spot image are calculated using the gray-scale centroid method.

[0013] The centroid coordinates of the beacon beams are summed and averaged in multiple denoised beam images to obtain the beacon beam centroid positioning coordinates.

[0014] Secondly, this application provides a beacon centroid positioning device in optical communication based on an improved wavelet threshold, comprising:

[0015] The image acquisition module is used to acquire multiple beacon light images;

[0016] The filtering and wavelet decomposition module is used to perform median filtering and discrete wavelet transform on each beacon light image to obtain the wavelet decomposition multi-layer coefficient matrix corresponding to each beacon light image.

[0017] The denoising module is used to denoise each wavelet decomposition multi-level coefficient matrix separately using the VisuShrink method to obtain multiple wavelet decomposition multi-level coefficient denoising matrices.

[0018] The discrete wavelet inverse transform module is used to perform discrete wavelet inverse transform on each wavelet decomposition multi-layer coefficient denoising matrix to obtain multiple denoised spot images.

[0019] The spot centroid calculation module is used to calculate the centroid coordinates of the spot in each denoised spot image using the gray-scale centroid method.

[0020] The centroid coordinate summation and averaging module is used to sum and average the centroid coordinates of the light spots in multiple denoised light spot images to obtain the beacon light centroid positioning coordinates.

[0021] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the beacon centroid localization method in optical communication based on the improved wavelet threshold as described above.

[0022] According to the specific embodiments provided in this application, this application has the following technical effects:

[0023] This application provides a method, apparatus, and device for beacon centroid localization in optical communication based on improved wavelet thresholding. By acquiring multiple beacon light images and performing median filtering and discrete wavelet transform on each image, a wavelet decomposition multi-level coefficient matrix corresponding to each beacon light image is obtained. This solves the problem of noise and spot signal mixing in beacon light images, achieving effective separation of noise and spot signals in the frequency domain while preserving multi-scale feature information of the spot. The VisuShrink method is used to denoise each wavelet decomposition multi-level coefficient matrix, resulting in multiple wavelet decomposition multi-level coefficient denoising matrices. This solves the problem of traditional denoising methods falsely suppressing weak spot signals in beacon light images, achieving maximum preservation of spot signal integrity while removing noise. The method further addresses the issue of noise and spot signal mis-suppression in traditional denoising methods by performing median filtering and discrete wavelet transform on each wavelet decomposition multi-level coefficient matrix. By performing discrete wavelet inverse transform on the multi-layer coefficient denoising matrix, multiple denoised spot images are obtained, solving the problem of spot shape and position distortion during image reconstruction after denoising. This achieves high-quality spot image reconstruction and provides an accurate image foundation for subsequent centroid calculation. The centroid coordinates of the spots in each denoised spot image are calculated using the gray-scale centroid method, addressing the problem of poor adaptability to irregular spot shapes in centroid calculation and achieving high-precision centroid positioning for spots of arbitrary shapes. Furthermore, by summing and averaging the centroid coordinates of the spots in multiple denoised spot images, the beacon centroid positioning coordinates are obtained, solving the problems of insufficient reliability of centroid positioning from a single image and difficulty in fusing centroid information from multiple images. This achieves optimal integration of centroid information from multiple images, improving the stability and accuracy of beacon centroid positioning. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is an application environment diagram of a beacon centroid method based on improved wavelet thresholding in optical communication according to an embodiment of this application;

[0026] Figure 2 A flowchart illustrating a beacon centroid method in optical communication based on an improved wavelet threshold, provided as an embodiment of this application;

[0027] Figure 3 A schematic diagram of the functional modules of a beacon optical centroid device based on an improved wavelet threshold in optical communication is provided as an embodiment of this application;

[0028] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0029] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0030] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] The beacon centroid method based on improved wavelet thresholding in optical communication provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server. Terminal 102 can send multiple beacon light images to server 104. After receiving the multiple beacon light images, server 104 performs median filtering and discrete wavelet transform on each beacon light image to obtain the wavelet decomposition multi-level coefficient matrix corresponding to each beacon light image; it then denoises each wavelet decomposition multi-level coefficient matrix using the VisuShrink method to obtain multiple wavelet decomposition multi-level coefficient denoising matrices; it performs inverse discrete wavelet transform on each wavelet decomposition multi-level coefficient denoising matrix to obtain multiple denoised light spot images; it uses the gray-scale centroid method to calculate the centroid coordinates of the light spots in each denoised light spot image; and it sums and averages the centroid coordinates of the light spots in the multiple denoised light spot images to obtain the beacon light centroid positioning coordinates. Server 104 can feed back the beacon centroid positioning coordinates to terminal 102. Furthermore, in some embodiments, the beacon centroid method in optical communication based on improved wavelet thresholding can also be implemented independently by server 104 or terminal 102. For example, terminal 102 can directly perform beacon centroid positioning on the beacon image, or server 104 can obtain the beacon image from the data storage system and perform beacon centroid positioning on the beacon image.

[0032] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0033] In one exemplary embodiment, such as Figure 2 As shown, a method for beacon centroid in optical communication based on improved wavelet thresholding is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 206. Wherein:

[0034] Step 201: Acquire multiple beacon light images. A CMOS camera can be used to acquire the beacon light images, and the acquired beacon light images can be stored sequentially.

[0035] Step 202: Perform median filtering and discrete wavelet transform on each beacon light image to obtain the wavelet decomposition multi-layer coefficient matrix corresponding to each beacon light image.

[0036] Step 203: Denoise each wavelet decomposition multi-level coefficient matrix using the VisuShrink method to obtain multiple wavelet decomposition multi-level coefficient denoising matrices.

[0037] Step 204: Perform discrete wavelet inverse transform on each wavelet decomposition multi-layer coefficient denoising matrix to obtain multiple denoised spot images.

[0038] Step 205: The centroid coordinates of the light spot in each denoised light spot image are calculated using the gray-scale centroid method.

[0039] Step 206: Sum and average the centroid coordinates of the spots in multiple denoised spot images to obtain the beacon centroid positioning coordinates. Sum the x and y coordinates of all obtained spot centroid coordinates to obtain the sum of the centroid x coordinates and the sum of the centroid y coordinates; divide the sum of the centroid x coordinates and the sum of the centroid y coordinates by the number of denoised spot images to obtain the beacon centroid positioning coordinates.

[0040] By implementing steps 201 to 206 above, this application can effectively improve the accuracy and reliability of beacon centroid positioning in optical communication.

[0041] In another exemplary embodiment of this application, the acquired beacon light image is processed sequentially. First, median filtering is applied to the spot image to effectively suppress random noise, providing a favorable foundation for the subsequent accurate localization of the spot centroid. To further optimize the feature extraction and noise suppression effects of the beacon light image and improve the accuracy and adaptability of wavelet decomposition, step 202 specifically includes:

[0042] The median-filtered image was subjected to a discrete wavelet transform with a wavelet basis of db7 and a decomposition level of 3. The selection of the wavelet basis and the number of decomposition levels were determined through simulation analysis. At this level, the processed image achieved optimal results in MSE (Mean Squared Error), PSNIR (Peak Signal-to-Noise Ratio), and energy loss. Specifically, the following formula was used to perform a discrete wavelet transform on each beacon light image after median filtering, resulting in multiple intermediate matrices:

[0043]

[0044] Where, x m,n Let represent the wavelet coefficients in the m-th row and n-th column of the intermediate matrix; R represents the integration region; f(t) represents the beacon light image after median filtering at time t; <,> represent the inner product operation; j represents the scaling parameter of the wavelet function; k represents the translation parameter of the wavelet transform; ψ j,k (t) represents the wavelet function after transformation by scaling parameter j and translation parameter k at time t; Represents ψ m,n The complex conjugate of (t); a0 represents the initial scaling factor; b0 represents the initial translation factor.

[0045] The following formulas are used to perform trigonometric function transformations on each intermediate matrix to obtain multiple wavelet decomposition multi-layer coefficient matrices:

[0046]

[0047] Where g() represents the change function; c max c represents the maximum value of the wavelet coefficients. min The minimum value of the wavelet coefficients is represented by α; α represents the adjustment parameter. By adjusting the value of α, the variation of the wavelet coefficients near the threshold can be changed, thereby enabling the noise figure to be separated from the useful figure more effectively and improving the denoising performance. Simulation analysis shows that the denoising effect is optimal when α is 1.

[0048] In another exemplary embodiment of this application, in order to further improve the accuracy and adaptability of beacon light image denoising, optimize the denoising effect, and better preserve the characteristics of the light spot signal, the above step 203 is replaced by the following steps 301 to 303:

[0049] Step 301: Determine the coefficient threshold of each wavelet decomposition coefficient transformation matrix using the VisuShrink method. HL is employed. J LH J HH J The wavelet coefficients (J = 1, 2, 3, representing the wavelet decomposition level) are calculated using the VisuShrink method to obtain the level coefficients HL of the J-th level. J Vertical coefficient LH J Diagonal coefficient HH J The corresponding threshold is calculated using the vertical coefficient LH3 of the third layer to determine the threshold of the low-frequency wavelet coefficients. The threshold function is the wavelet threshold denoising function of the proposed new comprehensive function.

[0050] As an optional implementation, step 301 specifically includes: determining the coefficient threshold of each wavelet decomposition coefficient transformation matrix using the following formula:

[0051]

[0052] Where T is the coefficient threshold of the J-th layer; σ is the noise standard deviation; M and N are the M-th row and N-th column pixels of the median-filtered beacon light image, respectively; s = [1, 2, 3]; s = 1, s = 2, and s = 3 correspond to the high-frequency subband coefficients in the horizontal direction, the vertical direction, and the diagonal direction, respectively; J represents the wavelet decomposition layer number. This threshold calculation method mainly improves upon the global threshold, utilizing wavelet coefficients in each layer and part to fully utilize wavelet data while improving denoising performance.

[0053] Step 302: Based on the coefficient threshold of each wavelet decomposition coefficient transformation matrix, wavelet denoising is performed using a preset threshold function.

[0054] As an optional implementation, the coefficient threshold calculated in the previous step is substituted into the newly proposed preset threshold function, and the wavelet coefficients are substituted into the preset threshold function to obtain the wavelet coefficients after wavelet threshold denoising. The preset threshold function in step 302 is:

[0055]

[0056] in, x represents the wavelet coefficient in the m-th row and n-th column of the intermediate matrix after denoising; m,nThis represents the wavelet coefficient in the m-th row and n-th column of the intermediate matrix; m′ and n′ represent the first and second constants, respectively, which are generally positive numbers used to control the wavelet coefficient transformation when the value is greater than the threshold. To ensure continuity, n = (1-T)·(1+e -m The preset threshold function firstly possesses continuity, ensuring the method's denoising effect and good visual results; it combines the advantages and disadvantages of the two classic methods, soft thresholding and hard thresholding, maximizing their strengths and minimizing their weaknesses, avoiding damage to effective information while filtering out noise coefficients as much as possible.

[0057] Step 303: Perform inverse trigonometric transformation on the wavelet coefficients after wavelet denoising to obtain multiple wavelet decomposition multi-layer coefficient denoising matrices.

[0058] As an optional implementation, the expression for the inverse trigonometric function transformation in step 303 is:

[0059]

[0060] Where g′() represents the inverse trigonometric function of the transformation; c max c represents the maximum value of the wavelet coefficients. min α represents the minimum value of the wavelet coefficients; α represents the adjustment parameter.

[0061] In another exemplary embodiment of this application, the discrete wavelet inverse transform in step 204 is:

[0062]

[0063] Where f′(t) represents the denoised spot image at time t; j represents the scaling parameter of the wavelet function; k represents the translation parameter of the wavelet transform; ψ j,k (t) represents the wavelet function after transformation by scaling parameter j and translation parameter k at time t.

[0064] At this point, the denoised spot image is obtained, completing the entire denoising process. This application applies median filtering and wavelet thresholding with a preset threshold function to the noisy image, effectively removing noise information and suppressing the influence of atmospheric turbulence and background light on the beacon light spot. Furthermore, the proposed preset threshold function is not complex, ensuring the overall real-time performance of the scheme and enabling rapid positioning.

[0065] In another exemplary embodiment of this application, step 205 specifically includes:

[0066]

[0067] Where, x c and y cLet x and y represent the x-coordinate and y-coordinate of the centroid of the spot in the denoised spot image, respectively. Let i and j′ represent the gray level of the pixel in the i-th row and j′-th column of the denoised spot image, respectively. Let f(i,j′) represent the frequency of the gray level in the i-th row and j′-th column, and S be the total number of pixels in the denoised spot image. Since the algorithm complexity of each part is not high, the running time is short, and the denoising effect is good, which can improve positioning accuracy and achieve fast positioning.

[0068] This application addresses image denoising of beacon light in laser links affected by atmospheric turbulence, ensuring the rapid, continuous, and stable operation of the laser link. It features good denoising effect, high accuracy, fast calculation speed, sub-pixel positioning accuracy, and real-time performance, thus effectively guaranteeing the stability and reliability of the laser communication link.

[0069] This application also provides an application scenario in which the aforementioned beacon centroid method based on improved wavelet thresholding in optical communication is applied. Specifically, the beacon centroid method based on improved wavelet thresholding in optical communication provided in this embodiment can be applied to beam alignment and tracking scenarios in optical communication systems. Beam alignment and tracking scenarios include an optical signal transmission stage, an optical signal reception stage, and an optical signal processing stage. The optical signal enters the optical signal transmission stage from the optical signal transmission stage, passes through various interferences during the optical signal transmission process (such as atmospheric turbulence, noise, etc.), and finally reaches the optical signal reception and processing stage. The beacon centroid localization method based on improved wavelet thresholding in optical communication provided in this embodiment belongs to the spot centroid localization stage within the optical signal reception and processing stage. Specifically, in the optical signal reception and processing stage, beacon light in the optical signal can be processed based on an improved wavelet threshold denoising and centroid calculation method. This involves a series of steps including acquiring multiple beacon light images, filtering, wavelet transform, denoising, inverse transform, centroid calculation, and summation and averaging. This significantly improves the accuracy and reliability of centroid positioning, providing a more accurate basis for beam alignment and tracking. By optimizing the denoising and centroid calculation process, this method can effectively address various interferences encountered by the optical signal during transmission, ensuring the efficient operation of the optical communication system.

[0070] Based on the same inventive concept, this application also provides a beacon centroid device for implementing the aforementioned beacon centroid method based on improved wavelet thresholding in optical communication. The solution provided by this device is similar to the implementation described in the above method. Therefore, the specific limitations of one or more embodiments of the beacon centroid device based on improved wavelet thresholding in optical communication provided below can be found in the limitations of the beacon centroid method based on improved wavelet thresholding in optical communication described above, and will not be repeated here.

[0071] In one exemplary embodiment, such as Figure 3 As shown, a beacon centroid device for optical communication based on an improved wavelet threshold is provided, comprising:

[0072] Image acquisition module 401 is used to acquire multiple beacon light images.

[0073] The filtering and wavelet decomposition module 402 is used to perform median filtering and discrete wavelet transform on each beacon light image to obtain the wavelet decomposition multi-layer coefficient matrix corresponding to each beacon light image.

[0074] The denoising module 403 is used to denoise each wavelet decomposition multi-level coefficient matrix separately using the VisuShrink method to obtain multiple wavelet decomposition multi-level coefficient denoising matrices.

[0075] The discrete wavelet inverse transform module 404 is used to perform discrete wavelet inverse transform on each wavelet decomposition multi-layer coefficient denoising matrix to obtain multiple denoised spot images.

[0076] The spot centroid calculation module 405 is used to calculate the centroid coordinates of the spot in each denoised spot image using the grayscale centroid method.

[0077] The centroid coordinate summation and averaging module 406 is used to sum and average the centroid coordinates of the light spots in multiple denoised light spot images to obtain the beacon light centroid positioning coordinates.

[0078] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores beacon light image processing data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a beacon light centroid method in optical communication based on an improved wavelet threshold.

[0079] Those skilled in the art will understand that Figure 4The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment to which the present application is applied. Specific computer equipment may include, for example, [the following is a list of possible additional structures]. Figure 4 The embodiments show more or fewer components, combinations of certain components, or different component arrangements. In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, which the processor executes to implement the steps in the above-described method embodiments.

[0080] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0081] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0082] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0083] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0084] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0085] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0086] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for beacon centroid localization in optical communication based on improved wavelet thresholding, characterized in that, The beacon centroid localization method in optical communication based on improved wavelet thresholding includes: Acquire multiple beacon light images; Median filtering and discrete wavelet transform are performed on each beacon light image to obtain the wavelet decomposition multi-level coefficient matrix corresponding to each beacon light image. Specifically, the following formula is used to perform discrete wavelet transform on each beacon light image after median filtering to obtain multiple intermediate matrices: ； ； in, This represents the wavelet coefficient in the m-th row and n-th column of the intermediate matrix; Indicates the integration region; This represents the beacon light image after median filtering at time t; Indicates inner product operation; This represents the scaling parameter of the wavelet function; Represents the translation parameters of the wavelet transform; Indicates the scaling parameter at time t. Translation parameters The transformed wavelet function; express The complex conjugate; Indicates the initial scaling factor; Indicates the initial translation factor; The following formulas are used to perform trigonometric function transformations on each intermediate matrix to obtain multiple wavelet decomposition multi-layer coefficient matrices: ； in, Represents a function of change; This represents the maximum value of the wavelet coefficients; This represents the minimum value of the wavelet coefficients; Indicates the adjustment parameter; The VisuShrink method is used to denoise each wavelet decomposition multi-level coefficient matrix to obtain multiple wavelet decomposition multi-level coefficient denoising matrices. Specifically, the VisuShrink method is used to determine the coefficient threshold of each wavelet decomposition coefficient transformation matrix. Based on the coefficient thresholds of each wavelet decomposition coefficient transformation matrix, wavelet denoising is performed using a preset threshold function; the preset threshold function is: ； in, This represents the wavelet coefficient in the m-th row and n-th column of the intermediate matrix after denoising. This represents the wavelet coefficient in the m-th row and n-th column of the intermediate matrix; and Let these represent the first constant and the second constant, respectively. Perform inverse trigonometric transformation on the wavelet coefficients after wavelet denoising to obtain multiple wavelet decomposition multi-layer coefficient denoising matrices. Discrete wavelet inverse transform is performed on each wavelet decomposition multi-layer coefficient denoising matrix to obtain multiple denoised spot images; The centroid coordinates of the light spot in each denoised light spot image are calculated using the gray-scale centroid method. The centroid coordinates of the beacon beams are summed and averaged in multiple denoised beam images to obtain the beacon beam centroid positioning coordinates.

2. The beacon centroid localization method in optical communication based on improved wavelet thresholding according to claim 1, characterized in that, The determination of the coefficient threshold for each wavelet decomposition coefficient transformation matrix using the VisuShrink method specifically includes: The coefficient thresholds for each wavelet decomposition coefficient transformation matrix are determined using the following formula: ； ； in, The coefficient threshold of the J-th wavelet decomposition layer; denoted as the noise standard deviation; M and N are the M-th row and N-th column pixels of the median-filtered beacon light image, respectively; s=[1, 2, 3]; s=1, s=2, and s=3 correspond to the high-frequency subband coefficients in the horizontal direction, the vertical direction, and the diagonal direction, respectively.

3. The beacon centroid localization method in optical communication based on improved wavelet thresholding according to claim 1, characterized in that, The expression for the inverse trigonometric function transformation is: ； in, Represents the inverse trigonometric function; This represents the maximum value of the wavelet coefficients; This represents the minimum value of the wavelet coefficients; This indicates the adjustment parameter.

4. The beacon centroid localization method in optical communication based on improved wavelet thresholding according to claim 1, characterized in that, The inverse discrete wavelet transform is: ； in, Represents the denoised spot image at time t; This represents the scaling parameter of the wavelet function; Represents the translation parameters of the wavelet transform; Indicates the scaling parameter at time t. Translation parameters The transformed wavelet function.

5. The beacon centroid localization method in optical communication based on improved wavelet thresholding according to claim 1, characterized in that, The method of using grayscale centroids to calculate the centroid coordinates of the light spots in each denoised light spot image specifically includes: ； in, and Let i and y represent the x-coordinate and y-coordinate of the centroid of the spot in the denoised spot image, respectively. Represent the gray levels of the pixels in the i-th row and the i-th row of the denoised spot image, respectively. Column pixel grayscale levels, Indicates the i-th row and the first... The frequency of gray levels is listed, and S is the total number of pixels in the denoised spot image.

6. A beacon centroid positioning device in optical communication based on an improved wavelet threshold, characterized in that, The beacon centroid localization device in optical communication based on improved wavelet threshold is used to implement the beacon centroid localization method in optical communication based on improved wavelet threshold as described in any one of claims 1-5. The beacon centroid localization device in optical communication based on improved wavelet threshold comprises: The image acquisition module is used to acquire multiple beacon light images; The filtering and wavelet decomposition module is used to perform median filtering and discrete wavelet transform on each beacon light image to obtain the wavelet decomposition multi-layer coefficient matrix corresponding to each beacon light image. The denoising module is used to denoise each wavelet decomposition multi-level coefficient matrix separately using the VisuShrink method to obtain multiple wavelet decomposition multi-level coefficient denoising matrices. The discrete wavelet inverse transform module is used to perform discrete wavelet inverse transform on each wavelet decomposition multi-layer coefficient denoising matrix to obtain multiple denoised spot images. The spot centroid calculation module is used to calculate the centroid coordinates of the spot in each denoised spot image using the gray-scale centroid method. The centroid coordinate summation and averaging module is used to sum and average the centroid coordinates of the light spots in multiple denoised light spot images to obtain the beacon light centroid positioning coordinates.

7. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the beacon centroid localization method in optical communication based on an improved wavelet threshold, as described in any one of claims 1-5.

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