An underwater sensor network node positioning method

Abstract

The application relates to an underwater sensor network node positioning method, which comprises the following steps: obtaining transparency data of each node in an underwater sensor network, combining depth, temperature and turbidity information to construct an initial three-dimensional space distribution model of the transparency data; adopting an adaptive Kalman filtering algorithm to iteratively optimize the initial three-dimensional space distribution model to obtain an optimized transparency three-dimensional distribution model; according to a mapping model of a sensor drift amount and a working time length and a time compensation function of the sensor drift amount, performing drift compensation on the optimized transparency three-dimensional distribution model; based on the drift-compensated transparency distribution model, judging the redundancy of the transparency data of each node, and performing dimensionality reduction compression on the redundant data; and adopting a data compression algorithm based on dictionary learning to adaptively compress the dimensionality-reduced and compressed transparency data.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of sensors, in particular to an underwater sensor network node positioning method. BACKGROUND

[0002] In an underwater sensor network, the acquisition and processing of transparency data face multiple challenges. First, due to water movement and light scattering factors, the original transparency data is uncertain. Long-term operation causes sensor drift, which affects data accuracy. Limited radio wave transmission bandwidth limits data transmission volume, which jointly affects node positioning accuracy. To solve these problems, a data processing flow needs to be established.

[0003] Starting from the acquisition of original data and the construction of an initial model, through filtering optimization, redundancy judgment, dimensionality reduction compression, scattering compensation, motion compensation and drift compensation, high-quality compressed transparency data is finally obtained. These data will be input into a positioning algorithm based on particle swarm optimization, combined with the reflection and re-radiation signals of radio waves, to achieve accurate positioning of underwater nodes. The entire process needs to balance the relationship between data quality, compression efficiency and positioning accuracy to achieve optimal node positioning effect under limited bandwidth.

[0004] The technical scheme of the present application solves the above-mentioned problems. By collecting transparency, depth, temperature and turbidity data of the underwater sensor network, a three-dimensional spatial distribution model of transparency is constructed and optimized. Adaptive Kalman filtering is used for model iteration, and drift compensation is performed. Dimensionality reduction compression and dictionary learning algorithm are used to reduce data redundancy and adapt to transmission bandwidth. Particle swarm optimization algorithm is used in combination with radio wave signals to achieve high-precision positioning and dynamic updating of nodes, thereby improving the accuracy and efficiency of underwater monitoring and node positioning. SUMMARY

[0005] In order to solve the problems existing in the prior art, the application aims to provide an underwater sensor network node positioning method.

[0006] The underwater sensor network node positioning method described in the application comprises:

[0007] S1, obtaining transparency data of each node in the underwater sensor network, combining depth, temperature and turbidity data to obtain transparency data, and establishing an initial three-dimensional spatial distribution model of the transparency data;

[0008] The depth data is used to determine the vertical position of the node;

[0009] The temperature and turbidity data are used to calculate a transparency correction coefficient through the initial three-dimensional spatial distribution model, and to adjust the original transparency value;

[0010] The initial three-dimensional spatial distribution model adopts a voxelization representation, wherein the accuracy of the voxels is determined by the accuracy of the node positions;

[0011] S2, iteratively optimizing the initial three-dimensional spatial distribution model in step S1 using an adaptive Kalman filtering algorithm for adaptive adjustment of filter parameters;

[0012] wherein the observation noise covariance matrix and the process noise covariance matrix are calculated for dynamic updating of the Kalman gain;

[0013] The mean square error of the results of two consecutive iterations is calculated, and if the error is less than a preset threshold, the filtering result is considered to be converged, and if the filtering result is not converged, the voxel accuracy is increased by one level and the filtering is performed again to obtain an optimized three-dimensional spatial distribution model of the transparency;

[0014] S3, implanting a reference light source in the filter in step S2, periodically collecting original data of the transparency, establishing a mapping model of the drift amount and time, and recording the intensity change of the reference light source to fit the functional relationship of the drift amount with time;

[0015] The optimized original data of the transparency is input into the mapping model to obtain the drift-compensated transparency data;

[0016] S4, judging the redundancy of the node transparency data according to the drift-compensated transparency distribution model in step S3, and defining the neighborhood of a node as all nodes within a preset threshold distance from the node;

[0017] If the similarity of the transparency data of a node to the data of its neighborhood nodes is higher than a preset threshold, the node data is considered to be redundant;

[0018] If the similarity of the transparency data of a node to the data of its neighborhood nodes is lower than a preset threshold, the node is considered to have no data redundancy;

[0019] Principal component analysis is used for dimensionality reduction and compression;

[0020] S5, using a data compression algorithm based on dictionary learning to adaptively compress the dimensionality-reduced and compressed transparency data in step S4, wherein the dictionary learning process includes dictionary initialization, sparse coding and dictionary updating, and the final dictionary and sparse representation are obtained through iterative optimization; the overcompleteness and sparsity of the dictionary learning are determined by the compression rate;

[0021] S6, inputting the transparency data in step S5 into a particle swarm optimization positioning algorithm to minimize the ranging error of each node by optimizing the objective function, and combining the reflection and re-radiation signals of the radio waves to calculate the spatial position coordinates of each node;

[0022] The particle swarm optimization algorithm searches for the optimal solution by iteratively updating the position and velocity of the particles.

[0023] The real-time positioning result of the underwater sensor network node is obtained by measuring the arrival time and received signal strength of the radio wave, and combining the multi-point positioning algorithm to calculate the node position.

[0024] Preferably, in step S1, the underwater sensor network collects the transparency, depth, temperature and turbidity data of each node, processes the transparency data through drift compensation, determines the three-dimensional position coordinates using the depth data, iteratively updates the particle swarm optimization algorithm to minimize the ranging error, at the same time, the temperature and turbidity data are used to calculate the transparency correction coefficient to adjust the original transparency value, combines the radio wave signal measurement, dynamically updates the node position through the multi-point positioning algorithm, finally obtains the three-dimensional transparency distribution model through spatial interpolation and voxelization processing, if the voxel accuracy is insufficient, the node position is optimized until the threshold is met, and finally the transparency distribution is displayed through three-dimensional visualization technology.

[0025] Preferably, in step S2, the adaptive Kalman filter algorithm is used to iteratively optimize the initial transparency distribution model, dynamically update the Kalman gain and filter parameters, judge convergence through mean square error, if the threshold is not reached, increase the voxel accuracy and reiterate, until convergence or the maximum number of iterations is reached, and the final three-dimensional transparency distribution model is obtained.

[0026] According to the model, the voxel transparency of the rendering scene is determined, the ray casting algorithm is used for semi-transparent rendering, and a high-resolution image or video sequence is output.

[0027] Preferably, in step S3, the drift-compensated data is obtained by real-time acquisition of sensor data and application of a drift amount time compensation function to correct the original transparency data, and then an initial distribution model is established, the model is iteratively optimized using the adaptive Kalman filter algorithm, the Kalman gain and filter parameters are dynamically updated, convergence is judged through mean square error, if the threshold is not reached, the voxel accuracy is increased and reiteration is performed, until convergence or the maximum number of iterations is reached, and the final three-dimensional transparency distribution model is obtained, the voxel transparency of the rendering scene is determined according to the model, the ray casting algorithm is used for rendering, and the semi-transparent visualization result is obtained by fusing with the original scene image.

[0028] Preferably, in step S4, the node neighborhood is determined and the transparency data similarity is calculated, redundant data is identified and compressed, which is used to compress the data to meet the transmission bandwidth requirement, and then the node transparency data is encoded and sent, after the data is decoded and recovered at the receiving end, the transparency distribution model is reconstructed, supporting subsequent applications including three-dimensional visualization, scene rendering is performed using the ray casting algorithm and the optimized model, and a semi-transparent rendering image is obtained.

[0029] Preferably, in the step S5, the dictionary is initialized by setting a target compression rate and determining dictionary parameters, using K-SVD algorithm, followed by sparse coding of orthogonal matching pursuit algorithm and dictionary updating of least square method, iteration until the dictionary converges, the dictionary is used for dimensionality reduction processing of transparency data, to obtain sparse representation coefficients, and compressed by Huffman coding, to generate data transmission packets for sending through radio waves, the receiving end decodes and recovers data, and reconstructs the transparency distribution model.

[0030] Preferably, in the step S6, the underwater sensor network node collects original transparency data, obtains corrected data through drift compensation processing, inputs particle swarm optimization algorithm for iteration and updating to minimize ranging error, combines radio wave signal measurement, calculates node position through multilateration algorithm, until the accuracy threshold is met, updates node position coordinates in real time, forms a dynamic positioning mechanism, and is displayed through three-dimensional visualization technology, combines depth, temperature and turbidity data, establishes a three-dimensional spatial distribution model of transparency data, and adjusts the original transparency value for voxel accuracy to meet the node position accuracy requirement.

[0031] The underwater sensor network node positioning method has the advantages that a three-dimensional spatial distribution model of transparency data is first established, combined with depth, temperature and turbidity data for correction, an adaptive Kalman filter algorithm is used for optimization in view of data uncertainty, a drift compensation model is established by implanting a reference light source to solve the sensor drift problem, data redundancy is judged according to the transparency distribution characteristics and dimensionality reduction compression is performed, an algorithm based on dictionary learning is used to adaptively compress the data to adapt to the transmission bandwidth limitation, and finally, a particle swarm optimization positioning algorithm is used in combination with radio wave signals to realize real-time accurate positioning of the sensor node.

[0032] The underwater sensor network node positioning method effectively improves the data processing accuracy and transmission efficiency of the underwater sensor network, and provides reliable support for spatial distribution characteristic analysis of underwater targets. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 is a flowchart of the underwater sensor network node positioning method according to the present application Figure 1 ;

[0034] Figure 2 is a flowchart of the underwater sensor network node positioning method according to the present application Figure 2 . DETAILED DESCRIPTION

[0035] As shown in Figures 1-2 , the underwater sensor network node positioning method according to the present application comprises:

[0036] S1, obtaining transparency data of each node in the underwater sensor network, combining depth, temperature and turbidity data to obtain transparency data, and establishing an initial three-dimensional spatial distribution model of the transparency data;

[0037] The depth data is used to determine the vertical position of the node;

[0038] The temperature and turbidity data are used to calculate a transparency correction coefficient through the initial three-dimensional spatial distribution model, and the original transparency value is adjusted, and the initial three-dimensional spatial distribution model adopts a voxelization representation method, wherein the accuracy of the voxel is determined by the accuracy of the node position;

[0039] S2, iteratively optimizing the initial three-dimensional spatial distribution model in step S1 using an adaptive Kalman filter algorithm, and adaptively adjusting the filter parameters;

[0040] The Kalman gain is dynamically updated by calculating the observation noise covariance matrix and the process noise covariance matrix;

[0041] The mean square error of the results of two consecutive iterations is calculated, and when the error is less than a preset threshold, the filter result is considered to be converged, and if the filter result is not converged, the voxel accuracy is increased by one level and the filtering is performed again to obtain an optimized transparency three-dimensional spatial distribution model;

[0042] S3, implanting a reference light source in the filter in step S2, regularly collecting reference data, establishing a drift amount-time mapping model, and fitting a function relationship of the drift amount with time by recording the intensity change of the reference light source;

[0043] The optimized transparency data is input into the mapping model to obtain drift-compensated transparency data;

[0044] S4, judging the redundancy of the transparency data of each node according to the drift-compensated transparency distribution model in step S3, and defining the neighborhood of the node as all nodes with a distance less than a preset threshold from the node;

[0045] If the similarity of the transparency data of a node to the data of its neighborhood nodes is higher than a preset threshold, it is considered that the node data is redundant;

[0046] If the similarity of the transparency data of a node to the data of its neighborhood nodes is lower than a preset threshold, it is considered that the node has no data redundancy;

[0047] A principal component analysis method is used for dimensionality reduction compression;

[0048] S5. A data compression algorithm based on dictionary learning is used to adaptively compress the transparency data after dimensionality reduction and compression in step S4. The dictionary learning process includes three steps: dictionary initialization, sparse encoding, and dictionary update. The final dictionary and sparse representation are obtained through iterative optimization. The overcompleteness and sparsity of the dictionary learning are used to obtain the compressed transparency data by the compression ratio.

[0049] S6. Input the transparency data from step S5 into the particle swarm optimization localization algorithm, minimize the ranging error of each node by optimizing the objective function, and calculate the spatial coordinates of each node by combining the reflected and re-radiated signals of radio waves.

[0050] The particle swarm optimization algorithm searches for the optimal solution by iteratively updating the position and velocity of the particles.

[0051] By measuring the arrival time and received signal strength of radio waves and combining this with a multi-point positioning algorithm to calculate the node positions, the real-time positioning results of the underwater sensor network nodes are obtained.

[0052] like Figures 1-2 As shown, in step S1, the transparency data of each node in the underwater sensor network is acquired, and the transparency data is obtained by combining the depth, temperature and turbidity data, and an initial three-dimensional spatial distribution model of the transparency data is established.

[0053] The depth data is used to determine the vertical position of the node;

[0054] The temperature data and turbidity data are used to calculate the transparency correction coefficient through the initial three-dimensional spatial distribution model, thereby adjusting the original transparency value;

[0055] The initial three-dimensional spatial distribution model adopts a voxel representation, where the accuracy of the voxels is determined by the accuracy of the node positions.

[0056] Further, in step S1, the original transparency, depth, temperature and turbidity data of each node in the underwater sensor network are acquired, and the transparency data is subjected to drift compensation processing to obtain the corrected transparency data.

[0057] For each node, the vertical position coordinates of the node in three-dimensional space are determined based on the depth data. The node position coordinates are then input into the particle swarm optimization algorithm for iterative updates to minimize the ranging error of each node and obtain the optimized node spatial position.

[0058] For each node, temperature data and turbidity data are input into a pre-established transparency correction coefficient regression model to obtain the node's transparency correction coefficient, which is used to adjust the original transparency value.

[0059] For each node, the corrected transparency value is multiplied by the transparency correction coefficient to obtain a corrected node transparency value as the final measurement result of the node transparency;

[0060] The reflected signal and the re-radiated signal of the radio wave are acquired, the arrival time and the received signal strength of the radio wave are measured, the measurement results are fused with the output results of the particle swarm optimization algorithm, the node position is calculated through the multi-point positioning algorithm, and the spatial position coordinates of the node are dynamically updated;

[0061] According to the optimized three-dimensional spatial coordinates of each node and the corrected transparency value, a spatial interpolation method is used to perform spatial interpolation on the transparency data in the three-dimensional space to obtain continuous three-dimensional transparency distribution data;

[0062] According to the preset voxel accuracy, the continuous three-dimensional transparency distribution data is voxelized to obtain a three-dimensional voxel representation model of the transparency data, and if the voxel accuracy does not satisfy the preset threshold, the node position accuracy is improved and the node position optimization is performed again;

[0063] If the voxel accuracy satisfies the preset threshold, the three-dimensional voxel representation model of the transparency data is output to form a three-dimensional spatial distribution model of the underwater sensor network node transparency, and the real-time distribution result of the transparency is obtained;

[0064] A three-dimensional visualization technology is used to visualize the three-dimensional distribution model of the underwater sensor network node transparency, and the spatial distribution characteristics of the underwater transparency are intuitively displayed to provide data support for subsequent underwater target analysis.

[0065] Specifically, in step S1, first, the original transparency, depth, temperature and turbidity data of each node in the underwater sensor network are acquired, the transparency data is subjected to drift compensation processing, a least square method is used to fit a drift curve, and corrected transparency data is obtained;

[0066] Then, for each node, the vertical position coordinates of the node in the three-dimensional space are determined according to the depth data, the node position coordinates are input into a particle swarm optimization algorithm, the number of particles is set to 50, the iteration number is set to 100, the search space range is set to 100m*100m*100m, the ranging error of each node is minimized, and the optimized node spatial position is obtained;

[0067] Then, the temperature data and the turbidity data are input into a pre-established multiple linear regression model to obtain the transparency correction coefficient of the node, the determination coefficient R² of the regression model is 0.85, and the original transparency value is adjusted;

[0068] Then, the corrected transparency value is multiplied by the transparency correction coefficient to obtain a corrected node transparency value as the final measurement result of the node transparency;

[0069] The radio wave reflection signal and the re-radiation signal with a frequency of 2.4 GHz are acquired at the same time, the time of arrival and the received signal strength of the radio wave are measured, the output results of the Kalman filtering algorithm and the particle swarm optimization algorithm are fused, the three-dimensional positioning of the node is realized through the Chan algorithm, and the spatial position coordinates of the node are dynamically updated;

[0070] According to the optimized three-dimensional spatial coordinates of each node and the corrected transparency value, the Kriging interpolation method is used to interpolate the transparency data in a three-dimensional space of 100m*100m*100m, the interpolation accuracy is 1m, and continuous three-dimensional transparency distribution data is obtained.

[0071] Finally, according to the preset voxel accuracy of 0.5m, the continuous three-dimensional transparency distribution data is voxelized, the octree algorithm is used for voxel division, and a three-dimensional voxel representation model of the transparency data is obtained.

[0072] If the voxel accuracy does not meet the preset threshold, the node position accuracy is improved to 0.1m, and the node position optimization step is returned to execute;

[0073] If the voxel accuracy meets the preset threshold, the three-dimensional voxel representation model of the transparency data is output, a three-dimensional spatial distribution model of the underwater sensor network node transparency is formed, and the real-time distribution result of the transparency is obtained.

[0074] Finally, the Unity 3D engine is used to visualize and render the three-dimensional distribution model of the underwater sensor network node transparency, appropriate lighting and materials are set, and the spatial distribution characteristics of the underwater transparency are intuitively displayed, providing data support for subsequent underwater target analysis.

[0075] As shown in FIG. Figures 1-2 In step S2, the adaptive Kalman filtering algorithm is used to iteratively optimize the initial three-dimensional spatial distribution model in step S1 for the uncertainty of the transparency data, and the filter parameters are adaptively adjusted.

[0076] The observation noise covariance matrix and the process noise covariance matrix are calculated to dynamically update the Kalman gain.

[0077] The mean square error of the continuous two iteration results is calculated, if the error is less than the preset threshold, the filtering result is considered to be converged, if the filtering result is not converged, the voxel accuracy is increased by one level and the filtering is performed again, and the optimized three-dimensional spatial distribution model of the transparency is obtained.

[0078] Further, in step S2, step 1, the initial distribution model of the transparency data is obtained, and the adaptive Kalman filtering algorithm is used to iteratively optimize the initial distribution model for the uncertainty of the transparency data.

[0079] Step 2, in each iteration optimization process, the Kalman gain is dynamically updated by calculating the observation noise covariance matrix and the process noise covariance matrix, and the filtering parameters of the Kalman filter are adaptively adjusted;

[0080] Step 3, calculate the mean square error between the current iteration result and the last iteration result, judge whether the mean square error is less than the preset convergence threshold, if yes, consider that the filtering result has converged, and take the current iteration result as the final transparency three-dimensional distribution model;

[0081] Step 4, if the mean square error is greater than or equal to the preset convergence threshold, the voxel precision is increased by one level, the initial distribution model of the transparency data is obtained under the increased voxel precision, and the adaptive Kalman filtering iteration optimization process is returned to step 2;

[0082] Repeat steps 2 to 4 until the filtering result converges or reaches the preset maximum iteration number, and take the result of the last iteration optimization as the final transparency three-dimensional distribution model;

[0083] According to the final obtained transparency three-dimensional distribution model, the transparency value of each voxel in the to-be-rendered scene is determined, which is used for subsequent volume rendering rendering processing;

[0084] Using the ray casting algorithm, combined with the optimized transparency three-dimensional distribution model, the to-be-rendered scene is rendered to obtain a realistic translucent rendering effect diagram;

[0085] The translucent effect diagram obtained by rendering is post-processed, and the brightness, contrast, color balance parameters are adjusted to further improve the realism and visual impact of the rendering effect;

[0086] The post-processed translucent rendering effect diagram is output as a high-resolution image or video sequence, which is used for subsequent display, analysis and application.

[0087] Specifically, in step S2, first, an initial distribution model of the transparency data is obtained, and an adaptive Kalman filtering algorithm is used to iteratively optimize the initial distribution model according to the uncertainty of the transparency data;

[0088] In each iteration optimization process, the Kalman gain is dynamically updated by calculating the observation noise covariance matrix and the process noise covariance matrix, including setting the observation noise covariance matrix as a diagonal matrix with diagonal elements of 0.01, and setting the process noise covariance matrix as a unit matrix multiplied by 0.001, to adaptively adjust the filtering parameters of the Kalman filter;

[0089] Then the mean square error between the current iteration result and the last iteration result is calculated, and it is judged whether the mean square error is less than the preset convergence threshold 0.0001, if yes, it is considered that the filtering result has converged, and the current iteration result is taken as the final transparency three-dimensional distribution model;

[0090] If the mean square error is greater than or equal to 0.0001, the voxel precision is increased from 1m to 0.5m, the initial distribution model of the transparency data is reacquired under the increased voxel precision, the adaptive Kalman filtering iteration optimization processing is continued, and the iteration process is repeated until the filtering result converges or the preset maximum iteration number 50 times is reached;

[0091] According to the final obtained transparency three-dimensional distribution model, the transparency value of each voxel in the to-be-rendered scene is determined, which is used for subsequent volume rendering rendering processing;

[0092] The ray casting algorithm is adopted, the to-be-rendered scene is rendered in combination with the optimized transparency three-dimensional distribution model, the light ray step is set to 0.01, and the light ray sampling point number is set to 500, so that a realistic translucent rendering effect diagram is obtained;

[0093] The rendered translucent effect diagram is post-processed, the brightness is adjusted by +10%, the contrast is adjusted by +20%, and the color balance RGB component is adjusted by +5%, so that the realism and visual impact of the rendering effect are further improved;

[0094] Finally, the post-processed translucent rendering effect diagram is output as a PNG image sequence with a 4K resolution, which is used for subsequent display, analysis and application.

[0095] As shown in Figures 1-2 In step S3, for the drift problem caused by long-term work of the sensor, a reference light source is implanted in the filter in step S2, the original data of the transparency is regularly collected, a mapping model of the drift amount and the time is established, the intensity change of the reference light source is recorded, and a function relationship of the drift amount with the time is fitted.

[0096] The optimized original data of the transparency is input into the mapping model to obtain the drift-compensated transparency data.

[0097] Further, in step S3, step 1, the original data of the transparency collected by the sensor in real time is obtained, the drift compensation value corresponding to the current cumulative working time of the sensor is calculated through the drift amount time compensation function, the original data of the transparency is drift-compensated and corrected, and the drift-compensated transparency data is obtained.

[0098] Step 2, according to the drift compensated transparency data, obtain the initial distribution model of the transparency data, and use the adaptive Kalman filtering algorithm to iteratively optimize the initial distribution model for the uncertainty of the transparency data;

[0099] Step 3, in each iteration optimization process, the Kalman gain is dynamically updated by calculating the observation noise covariance matrix and the process noise covariance matrix, and the filtering parameters of the Kalman filter are adaptively adjusted to obtain the current iteration result;

[0100] Step 4, calculate the mean square error between the current iteration result and the last iteration result, and judge whether the mean square error is less than the preset convergence threshold, if yes, it is considered that the filtering result has converged, and the current iteration result is taken as the final transparency three-dimensional distribution model;

[0101] Step 5, if the mean square error is greater than or equal to the preset convergence threshold, the voxel precision is increased by one level, and the initial distribution model of the transparency data is obtained under the increased voxel precision, and the adaptive Kalman filtering iterative optimization process is returned to step 2;

[0102] Repeat steps 2 to 5 until the filtering result converges or reaches the preset maximum iteration number, and take the result of the last iteration optimization as the final transparency three-dimensional distribution model;

[0103] According to the final obtained transparency three-dimensional distribution model, the transparency value of each voxel in the to-be-rendered scene is determined, which is used for subsequent volume rendering rendering processing;

[0104] Using the ray casting algorithm, combined with the optimized transparency three-dimensional distribution model, the to-be-rendered scene is rendered to obtain a realistic translucent rendering effect diagram;

[0105] Fuse the rendered translucent effect diagram with the original scene image to generate the final translucent visualization result through image synthesis technology, and provide it to the user for visualization analysis and decision support.

[0106] Specifically, in step S3, the sensor collects real-time transparency original data, including collecting 10 times per second, according to the sensor has worked for 720 hours, the drift compensation value is calculated as 7.2% through the fitting function y=0.01x (where x is the working time, unit: hour; y is the drift, unit: transparency percentage), and the original data is multiplied by the compensation coefficient 0.928 to obtain the compensated transparency data;

[0107] An initial distribution model is constructed based on the compensated data, and adaptive Kalman filtering is used for iterative optimization, and in each iteration, the Q / R ratio is used to dynamically adjust the Kalman gain by calculating the observation noise covariance matrix Q and the process noise covariance matrix R, to obtain the optimization result;

[0108] The mean square error of the current result and the last result is calculated, and if it is less than the threshold value 0.01, it is considered to be converged, and a three-dimensional distribution model is output; otherwise, the voxel precision is improved by one level, including from 1cm to 0.5cm, the initial model is reacquired and returned to continue iteration, until convergence or the maximum iteration number 100 times is reached;

[0109] The transparency value of each voxel in the final 1024*1024*1024 three-dimensional model is used for subsequent rendering;

[0110] The ray casting algorithm is used, the light step is set to 0.01, the transparency of each ray is accumulated and the color is calculated, and a 1920*1080 resolution semi-transparent rendering image is obtained;

[0111] The rendering image and the original image are synthesized by Alpha blending, and the blending factor is set to the transparency of the rendering image, that is, the part with a transparency of 0 is completely displayed as the original image, and the part with a transparency of 1 is completely displayed as the rendering result, to generate the final visualization image.

[0112] As shown in Figures 1-2 In step S4, according to the drift-compensated transparency distribution model in step S3, the redundancy of the node transparency data is judged, and the neighborhood of the node is defined as all nodes with a distance less than a preset threshold value from the node;

[0113] If the similarity of the transparency data of a node and the data of its neighborhood nodes is higher than a preset threshold value, it is considered that the node data is redundant;

[0114] If the similarity of the transparency data of a node and the data of its neighborhood nodes is lower than a preset threshold value, it is considered that the node has no data redundancy;

[0115] The principal component analysis method is used to reduce the dimension of the transparency data, and the compression rate is determined by the transmission bandwidth of the radio wave.

[0116] Further, in step S4, the drift-compensated transparency distribution model is obtained, and for each node, its neighborhood range is determined, and the neighborhood is defined as all nodes with a distance less than a preset distance threshold value from the node;

[0117] The similarity of the transparency data of each node and the transparency data of all nodes in its neighborhood is calculated, and if the similarity is higher than a preset similarity threshold value, it is judged that the node data has redundancy;

[0118] For the data redundancy node, principal component analysis method is used for dimension reduction compression processing, the transparency data is decomposed by characteristic, the main characteristic component is extracted, and data compression is realized;

[0119] According to the limitation of radio wave transmission bandwidth, the compression rate threshold is determined, when the compression rate of node data is higher than the threshold, the compressed data is considered to meet the transmission bandwidth requirement;

[0120] The compressed node transparency data is encoded to generate data transmission package, and is sent through radio wave to realize efficient transmission of node transparency data;

[0121] At the receiving end, after obtaining the transmission package, decoding processing is carried out to obtain the compressed node transparency data, and then the same principal component analysis method as the sending end is used to reduce the dimension of the data to restore the original transparency data;

[0122] Based on the restored node transparency data, the transparency distribution model is reconstructed, which is used for subsequent business application, including three-dimensional visualization display, visibility analysis, and provides efficient and accurate transparency information support;

[0123] According to the reconstructed transparency distribution model, the transparency value of each voxel in the to-be-rendered scene is determined, which is used for subsequent volume rendering rendering processing;

[0124] The ray casting algorithm is used, combined with the optimized transparency three-dimensional distribution model, to render the to-be-rendered scene, and a realistic translucent rendering effect diagram is obtained.

[0125] Specifically, in step S4, when the drift-compensated transparency distribution model is obtained, the distance threshold of the node neighborhood range is set to 10 meters, that is, all nodes with a distance less than 10 meters from the target node are defined as the neighborhood of the node;

[0126] Then, the cosine similarity algorithm is used to calculate the transparency data similarity between each node and the nodes in its neighborhood, if the similarity is higher than 0.8, it is determined that the node data is redundant;

[0127] For the data redundancy node, principal component analysis method is used for dimension reduction compression, through eigenvalue decomposition, the first three principal components are selected, and the data compression rate reaches 70%;

[0128] According to the limitation of radio wave transmission bandwidth of 10Mbps, the compression rate threshold is determined as 75%, and the compressed data meets the transmission requirement;

[0129] The compressed node transparency data is encoded by Huffman coding to generate data transmission package, and is sent through radio wave;

[0130] After the receiving end receives the transmission packet, it performs Huffman decoding to obtain the compressed transparency data, and then uses the same principal component analysis method as the sending end to restore the original transparency data through eigenvector reconstruction;

[0131] Based on the restored node transparency data, a trilinear interpolation algorithm is used to reconstruct the transparency distribution model, which is applied to subsequent three-dimensional visualization display and line-of-sight analysis services;

[0132] According to the reconstructed transparency distribution model, a voxelization method is used to divide the scene into voxels of 0.5m x 0.5m x 0.5m, and determine the transparency value of each voxel;

[0133] Finally, a ray casting algorithm is used to set the ray step size to 0.1m to render the scene and obtain a realistic semi-transparent rendering.

[0134] As shown in Figures 1-2 In step S5, a data compression algorithm based on dictionary learning is used to adaptively compress the dimensionality-reduced transparency data in step S4. The dictionary learning process includes dictionary initialization, sparse coding, and dictionary updating, and the final dictionary and sparse representation are obtained through iterative optimization. The over-completeness and sparsity of the dictionary learning are determined by the compression rate, and the compressed transparency data is obtained.

[0135] Further, in step S5, the transparency data to be compressed is obtained, the target compression rate is determined according to the transmission bandwidth limitation of radio waves, and the over-completeness and sparsity parameters of the dictionary are determined according to the target compression rate;

[0136] The K-SVD algorithm is used to initialize the dictionary to obtain an initial dictionary, which is used as the input of the dictionary learning algorithm;

[0137] The orthogonal matching pursuit algorithm is used for sparse coding of the initial dictionary to obtain a sparse coefficient matrix, which is used as the input of the dictionary updating;

[0138] The least squares method is used to update the dictionary to obtain an updated dictionary, and it is determined whether the dictionary converges. If it does not converge, the updated dictionary is used as the input of the next round of iterative optimization, and the sparse coding step is returned to continue the iterative optimization;

[0139] If the dictionary converges, the updated dictionary is used as the final dictionary, and the dimensionality-reduced transparency data is obtained by dimensionality reduction processing of the transparency data to be compressed;

[0140] According to the final dictionary and the sparse coefficient matrix, the dimensionality-reduced transparency data is sparsely represented to obtain sparse representation coefficients;

[0141] According to the sparse representation coefficients, entropy coding algorithm such as Huffman coding is used to further compress the sparse representation coefficients, and the final compressed transparency data is obtained;

[0142] The compressed node transparency data is encoded to generate a data transmission packet, which is sent through radio waves to realize efficient transmission of the node transparency data between nodes.

[0143] At the receiving end, after obtaining the transmission packet, decoding processing is performed to obtain the compressed node transparency data, and then the same principal component analysis method as that of the sending end is used to reduce the dimension of the data to restore the original transparency data, and the transparency distribution model is reconstructed for subsequent business applications.

[0144] Specifically, in step S5, the transparency data to be compressed is obtained, the target compression rate is determined to be 80% according to the transmission bandwidth limit of radio waves of 10 Mbps, and the over-completeness of the dictionary is determined to be 1.5 times and the sparsity parameter is 0.1 according to the target compression rate.

[0145] The K-SVD algorithm is used to initialize the dictionary, and the left singular vectors corresponding to the first 100 singular values are selected as the atoms of the initial dictionary by performing SVD decomposition on the transparency data, to obtain an initial dictionary of size 100x100.

[0146] The orthogonal matching pursuit algorithm is used for sparse coding of the initial dictionary, the sparse representation error threshold is set to 0.01, and the iteration number is set to 50, to obtain a sparse coefficient matrix of 100x1000.

[0147] The least squares method is used to update the dictionary, and each atom of the dictionary is updated by solving the optimization problem of minimizing the reconstruction error to obtain the updated dictionary, and it is determined whether the change of the dictionary atom is less than 0.001.

[0148] The transparency data to be compressed is processed by dimension reduction, and the principal component analysis method is used to select the first 10 principal components with a cumulative contribution rate of 99% to obtain the dimension-reduced transparency data.

[0149] According to the final dictionary and the sparse coefficient matrix, the dimension-reduced transparency data is represented sparsely, and the sparse representation coefficients are obtained by solving the L1 norm minimization problem.

[0150] According to the sparse representation coefficients, the Huffman coding algorithm is used to construct an optimal binary tree to encode the coefficients, and the compressed transparency data with an average encoding length of 5 bits is obtained.

[0151] The compressed node transparency data is encoded in a Manchester coding mode to generate a data transmission package, which is sent through radio waves with a frequency of 2.4 GHz;

[0152] At the receiving end, the transmission package is obtained by Manchester decoding, and the sparse representation coefficients are obtained by Huffman decoding, and then the sparse coefficients are reconstructed by the dictionary to obtain the reduced dimension transparency data;

[0153] Finally, the principal component analysis method is used for data recovery, the original transparency data is restored, the transparency distribution model is reconstructed, and is applied to three-dimensional visualization display and line-of-sight analysis business scenarios.

[0154] As shown in Figures 1-2 In step S6, the transparency data in step S5 is input into the particle swarm optimization positioning algorithm, the ranging error of each node is minimized by optimizing the objective function, and the spatial position coordinates of each node are calculated by combining the reflection and re-radiation signals of radio waves;

[0155] The particle swarm optimization algorithm updates the position and speed of the particles by iteration to search for the optimal solution;

[0156] By measuring the time of arrival and received signal strength of radio waves, and combining the multi-point positioning algorithm to calculate the node position, the real-time positioning result of the underwater sensor network node is obtained, the positioning accuracy is determined by the accuracy requirement of the node position, and the positioning result is presented in a three-dimensional visualization manner, which is used to analyze the spatial distribution characteristics of the underwater target.

[0157] Further, in step S6, the original transparency data of the underwater sensor network node is obtained, and the data is processed for drift compensation to obtain the corrected transparency data;

[0158] The corrected transparency data is input into the particle swarm optimization algorithm, the position and speed of the particles are updated by iteration to search for the optimal solution, and the ranging error of each node is minimized;

[0159] The reflection signal and re-radiation signal of radio waves are obtained, and the spatial position coordinates of each node are calculated by combining the output results of the particle swarm optimization algorithm;

[0160] The time of arrival and received signal strength of radio waves are measured, and the measurement results are fused with the output results of the particle swarm optimization algorithm to calculate the node position by the multi-point positioning algorithm;

[0161] According to the accuracy requirement of the node position, the accuracy threshold of the positioning result is determined, if the positioning accuracy does not reach the threshold, the iteration optimization is returned to continue, until the accuracy requirement is met;

[0162] The spatial position coordinates of each node are dynamically updated to form a real-time updating mechanism of the underwater sensor network node position, and real-time positioning results of the nodes are obtained.

[0163] The real-time positioning results of the underwater sensor network nodes are visualized by using a three-dimensional visualization technology, and spatial distribution characteristics of the underwater target are intuitively displayed to provide data support for subsequent analysis.

[0164] The transparency data of each node in the underwater sensor network are acquired, and an initial three-dimensional spatial distribution model of the transparency data is established in combination with the depth, temperature and turbidity data.

[0165] The depth data is used to determine the vertical position of the node, the temperature and turbidity data are used to calculate a transparency correction coefficient through a pre-established regression model, and the original transparency value is adjusted, and the accuracy of the voxel is determined by the accuracy requirement of the node position.

[0166] Specifically, in step S6, the original transparency data of the underwater sensor network node is subjected to drift compensation processing to obtain corrected transparency data, including that the original transparency of node A is 0.8, and after correction by a drift compensation coefficient of 1.2, the corrected transparency is 0.96.

[0167] The corrected transparency data is input into a particle swarm optimization algorithm, the number of particles is set to 100, and the number of iterations is set to 50, the position and speed of the particles are updated through iteration, the optimal solution is searched, and the ranging error of each node is minimized, including that the ranging error of node A and node B is reduced from 2.5 m to 0.8 m.

[0168] The reflection signal and the re-radiation signal of the radio wave are acquired, the spatial position coordinates of each node are comprehensively calculated in combination with the output result of the particle swarm optimization algorithm, including that the position coordinates of node A are (10.2, 5.8, -20.3).

[0169] The time of arrival and the received signal strength of the radio wave are measured, the measurement results are subjected to Kalman filtering fusion with the output result of the particle swarm optimization algorithm, and the node position is calculated through a Chan algorithm multi-point positioning algorithm, including that the fusion positioning result of node A is (10.1, 5.9, -20.5).

[0170] According to the accuracy requirement of the node position, the accuracy threshold of the positioning result is determined to be 1 m, if the positioning accuracy does not reach the threshold, the particle swarm optimization algorithm step is returned to continue iteration and optimization until the accuracy requirement is met.

[0171] The spatial position coordinates of each node are dynamically updated to form a real-time updating mechanism of the underwater sensor network node position, and the node position is updated every 10 s to obtain real-time positioning results of the nodes.

[0172] The real-time positioning results of the underwater sensor network nodes are visualized in three dimensions by using the Unity 3D engine, and the spatial distribution characteristics of the underwater target are intuitively displayed, including displaying the position and moving track of the nodes in the three-dimensional scene;

[0173] The transparency data of each node in the underwater sensor network is obtained, and an initial three-dimensional spatial distribution model of the transparency data is established in combination with the depth, temperature and turbidity data, including that the transparency of node A is 0.96, the depth is 50 m, the temperature is 15℃, and the turbidity is 10 NTU.

[0174] The depth data is used to determine the vertical position of the node, and the temperature and turbidity data are used to calculate the transparency correction coefficient through a pre-established multiple linear regression model, including correction coefficients of 0.02 / ℃ and-0.01 / NTU, to adjust the original transparency value, and the accuracy of the voxel is set to 1 m to generate the three-dimensional spatial distribution model of the transparency.

[0175] In the description of the present application, it should be understood that the orientation words such as "front, rear, upper, lower, left, right", "transverse, vertical, horizontal" and "top, bottom" and the like indicate the orientation or positional relationship shown in the drawings, and are only used to facilitate the description of the present application and simplify the description, and in the absence of the opposite description, these orientation words do not indicate and imply that the indicated device or element must have a specific orientation or be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the scope of protection of the present application.

[0176] For those skilled in the art, other various corresponding changes and deformations can be made according to the above described technical solutions and concepts, and all of these changes and deformations should belong to the protection scope of the claims of the present application.

Claims

1. A method for locating nodes in an underwater sensor network, characterized in that, include: S1. Obtain the transparency data of each node in the underwater sensor network, combine it with depth, temperature and turbidity data to obtain transparency data, and establish an initial three-dimensional spatial distribution model of the transparency data; Depth data is used to determine the vertical position of nodes; Temperature data and turbidity data are used to calculate the transparency correction coefficient through the initial three-dimensional spatial distribution model, thereby adjusting the original transparency value; The initial three-dimensional spatial distribution model adopts a voxel representation, wherein the accuracy of the voxels is determined by the accuracy of the node positions; S2. The initial three-dimensional spatial distribution model in step S1 is iteratively optimized using an adaptive Kalman filter algorithm to adaptively adjust the filter parameters. Specifically, the observation noise covariance matrix and the process noise covariance matrix are calculated to dynamically update the Kalman gain; By calculating the mean square error of two consecutive iterations, if the error is less than a preset threshold, the filtering result is considered to have converged. If the filtering result does not converge, the voxel accuracy is increased by one level and the filtering is performed again to obtain the optimized three-dimensional spatial distribution model of transparency. S3. By embedding a reference light source in the filter in step S2, the raw data of transparency is collected periodically, and a mapping model between drift and time is established. By recording the intensity change of the reference light source, the functional relationship between drift and time is fitted. The original data of optimized transparency is input into the mapping model to obtain the transparency data after drift compensation; S4. Based on the transparency distribution model after drift compensation in step S3, determine the redundancy of the transparency data of each node, and define the neighborhood of a node as all nodes whose distance from the node is less than a preset threshold. If the similarity between the transparency data of a node and the data of its neighboring nodes is higher than a preset threshold, then the data of that node is considered redundant. If the similarity between the transparency data of a node and the data of its neighboring nodes is lower than a preset threshold, then the node is considered to have no data redundancy. Principal component analysis was used to reduce the dimensionality of the transparency data. S5. A data compression algorithm based on dictionary learning is used to adaptively compress the transparency data after dimensionality reduction and compression in step S4. The dictionary learning process includes dictionary initialization, sparse encoding, and dictionary update. The final dictionary and sparse representation are obtained through iterative optimization. The overcompleteness and sparsity of the dictionary learning are determined by the compression ratio. S6. Input the transparency data from step S5 into the particle swarm optimization localization algorithm, minimize the ranging error of each node by optimizing the objective function, and calculate the spatial coordinates of each node by combining the reflected and re-radiated signals of radio waves. Particle swarm optimization (PSO) algorithm searches for the optimal solution by iteratively updating the position and velocity of particles. By measuring the arrival time and received signal strength of radio waves and combining this with a multi-point positioning algorithm to calculate the node positions, the real-time positioning results of the underwater sensor network nodes are obtained.

2. The underwater sensor network node localization method according to claim 1, characterized in that, Step S1 includes: acquiring the original transparency, depth, temperature and turbidity data of each node in the underwater sensor network, performing drift compensation processing, fitting the drift curve using the least squares method, and obtaining the corrected transparency data; The vertical position coordinates of the node in three-dimensional space are determined based on the depth data. The node position coordinates are then input into the particle swarm optimization algorithm to obtain the optimized node spatial position. Temperature and turbidity data are input into a pre-established multiple linear regression model to obtain the node transparency correction coefficient. The corrected transparency value is then multiplied by the transparency correction coefficient to obtain the corrected node transparency value. At the same time, radio wave reflection and re-radiation signals at a frequency of 2.4 GHz are acquired, and the arrival time and received signal strength of the radio waves are measured to dynamically update the spatial coordinates of the node. Based on the optimized 3D spatial coordinates and corrected transparency values ​​of each node, continuous 3D transparency distribution data is obtained using the Kriging interpolation method.

3. The underwater sensor network node localization method according to claim 1, characterized in that, Step S2 includes: obtaining an initial distribution model of transparency data, performing iterative optimization on the initial distribution model, calculating the mean square error between the current iteration result and the previous iteration result, outputting the final three-dimensional transparency distribution model, and determining the transparency value of each voxel in the scene to be rendered based on the final three-dimensional transparency distribution model.

4. The underwater sensor network node localization method according to claim 1, characterized in that, Step S3 includes: acquiring raw transparency data collected in real time by the sensor, calculating the corresponding drift compensation value through the drift amount time compensation function, performing drift compensation correction on the raw transparency data to obtain drift-compensated transparency data, and obtaining an initial distribution model of the transparency data based on the drift-compensated transparency data.

5. The underwater sensor network node localization method according to claim 1, characterized in that, Step S4 includes: calculating the similarity between the transparency data of each node and the transparency data of all nodes in its neighborhood, in order to determine data redundancy; If the similarity is higher than the preset similarity threshold, then the node data is considered redundant. If the similarity is not higher than the preset similarity threshold, it is determined that the node data does not have redundancy; For nodes with redundant data, principal component analysis is used for dimensionality reduction and compression. The main feature components are extracted by performing feature decomposition on the transparency data.

6. The underwater sensor network node localization method according to claim 1, characterized in that, Step S5 includes: acquiring the transparency data to be compressed, and determining the overcompleteness and sparsity parameters of the dictionary based on the target compression ratio; The dictionary is initialized using the K-SVD algorithm to obtain an initial dictionary; The dictionary is updated using the least squares method to obtain the updated dictionary; Based on the final dictionary and sparse coefficient matrix, the dimensionality-reduced transparency data is sparsely represented to obtain sparse representation coefficients.

7. The underwater sensor network node localization method according to claim 2, characterized in that, Step S6 includes: acquiring the raw transparency data of the underwater sensor network nodes, performing drift compensation processing on the data to obtain corrected transparency data, and inputting the corrected transparency data into the particle swarm optimization algorithm; By iteratively updating the position and velocity of the particles, searching for the optimal solution, minimizing the ranging error of each node, and simultaneously acquiring the reflected and re-radiated signals of radio waves, the spatial coordinates of each node are calculated by combining the output results of the particle swarm optimization algorithm. The arrival time and received signal strength of radio waves are measured, and the measurement results are fused with the output of the particle swarm optimization algorithm to calculate the node position through a multi-point positioning algorithm. The accuracy threshold of the positioning result is determined based on the accuracy of the node location.

8. The underwater sensor network node localization method according to claim 7, characterized in that, If the accuracy threshold of the positioning result does not reach the threshold of 1m, then return to the following steps: The corrected transparency data is input into the particle swarm optimization algorithm. The position and velocity of the particles are updated iteratively to search for the optimal solution and minimize the ranging error of each node to continue iterative optimization. If the accuracy threshold of the positioning result reaches 1m, then no result will be returned.

9. The underwater sensor network node localization method according to claim 7, characterized in that, The transparency data of each node in the underwater sensor network is acquired, and combined with depth, temperature and turbidity data, an initial three-dimensional spatial distribution model of the transparency data is established. Depth data is used to determine the vertical position of nodes; Temperature and turbidity data are used to calculate transparency correction coefficients through a pre-established regression model, which are then used to adjust the original transparency values.

10. The underwater sensor network node localization method according to claim 9, characterized in that, The transparency data of each node in the underwater sensor network is acquired, and combined with depth, temperature and turbidity data, an initial three-dimensional spatial distribution model of the transparency data is established, including node A with a transparency of 0.96, a depth of 50m, a temperature of 15℃ and a turbidity of 10NTU. The depth data is used to determine the vertical position of the node; The temperature data and the turbidity data are used to calculate transparency correction coefficients through a pre-established multiple linear regression model, including correction coefficients of 0.02 / ℃ and -0.01 / NTU, to adjust the original transparency values. The voxel accuracy is set to 1m to obtain a three-dimensional spatial distribution model of transparency.

Images