A foundation radar three-dimensional deformation solving method and system

Abstract

The disclosure provides a foundation radar three-dimensional deformation solving method and system, belonging to the technical field of foundation radar deformation measurement, which comprises: based on multiple radars, multi-angle observation of deformation information of the same observation target is carried out to obtain multi-angle one-dimensional line-of-sight deformation data of the same observation target; based on the parameterized least square method, the multi-angle one-dimensional line-of-sight deformation data is comprehensively solved to obtain target three-dimensional deformation data of the same observation target; wherein the solving weight matrix of the parameterized least square method is determined by the variance component estimation method. The three-dimensional deformation solving method and system provided by the disclosure can improve the three-dimensional deformation solving precision, thereby improving the monitoring precision and early warning capability of geological disasters and infrastructure health detection.

Description

TECHNICAL FIELD

[0001] The present disclosure belongs to the technical field of ground-based radar deformation measurement, and more particularly to a ground-based radar three-dimensional deformation solving method and system. BACKGROUND

[0002] Ground-based radar has the advantages of flexible observation angle, non-contact, high measurement accuracy, etc., and is an important technical means for high-precision deformation monitoring, and is widely used in open-pit mine slope, landslide, infrastructure health monitoring and other fields. However, it can only obtain one-dimensional deformation in the direction of the line connecting the radar and the target, and cannot accurately reflect the true three-dimensional deformation information of the target, which is not conducive to analyzing the true status of the target. In addition, the current three-dimensional deformation solving method generally uses equal weight solving, and less considers the influence of the error between different radar data on the solving accuracy, and there is an unreasonable situation of weight matrix setting, which leads to low solving accuracy, affecting the monitoring accuracy and disaster warning ability. SUMMARY

[0003] The purpose of the present disclosure is to provide a ground-based radar three-dimensional deformation solving method and system to improve the three-dimensional deformation solving accuracy, thereby improving the monitoring accuracy and warning ability of disasters.

[0004] In a first aspect, the present disclosure provides a ground-based radar three-dimensional deformation solving method, comprising:

[0005] Based on the multi-angle observation of the deformation information of the same observation target by multiple radars, multi-angle one-dimensional line-of-sight deformation data of the same observation target is obtained.

[0006] Based on the parameterized least squares method, the multi-angle one-dimensional line-of-sight deformation data is comprehensively solved to obtain target three-dimensional deformation data of the same observation target; wherein the solving weight matrix of the parameterized least squares method is determined by the variance component estimation method.

[0007] In a second aspect, the present disclosure provides a ground-based radar three-dimensional deformation solving system, comprising:

[0008] The data acquisition module is configured to perform multi-angle observation on the deformation information of the same observation target based on multiple radars, and obtain multi-angle one-dimensional line-of-sight deformation data of the same observation target.

[0009] The solving module is configured to comprehensively solve the multi-angle one-dimensional line-of-sight deformation data based on the parameterized least squares method to obtain target three-dimensional deformation data of the same observation target; wherein the solving weight matrix of the parameterized least squares method is determined by the variance component estimation method.

[0010] In a third aspect, the present disclosure provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the ground-based radar three-dimensional deformation calculation method.

[0011] In a fourth aspect, the present disclosure provides a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the steps of the three-dimensional deformation calculation method.

[0012] The three-dimensional deformation calculation method and system provided by the present disclosure have the following advantages:

[0013] The present disclosure uses the parameterized least square method and the variance component estimation method to determine the calculation weight matrix for three-dimensional deformation calculation, which can comprehensively consider the influence of various factors on different data, reasonably allocate weights, make the calculation process more accurate, and improve the reliability of the target three-dimensional deformation data. It can be accurately used in infrastructure health detection and monitoring, geological disaster warning and other fields, and provide strong and accurate data support for related decision-making. Therefore, the present disclosure can improve the three-dimensional deformation calculation precision, thereby improving the monitoring precision and early warning ability of geological disasters. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative labor.

[0015] Figure 1 A flowchart of a ground-based radar three-dimensional deformation calculation method provided by an embodiment of the present disclosure is shown in the figure.

[0016] FIG. 2 is a radar line-of-sight observation result calculated by an embodiment of the present disclosure; wherein FIG. 2(a) is a radar 1 line-of-sight observation result, FIG. 2(b) is a radar 2 line-of-sight observation result, and FIG. 2(c) is a radar 3 line-of-sight observation result;

[0017] FIG. 3 is a calculation result of the present disclosure; wherein FIG. 3(a) is a calculation result of the parameterized least square method in the X direction based on the variance component estimation, FIG. 3(b) is a calculation result of the parameterized least square method in the Y direction based on the variance component estimation, and FIG. 3(c) is a calculation result of the parameterized least square method in the Z direction based on the variance component estimation;

[0018] Figure 4 is a real deformation of an observation target provided by an embodiment of the present disclosure; wherein Figure 4(a) is the real deformation in the X direction, Figure 4(b) is the real deformation in the Y direction, and Figure 4(c) is the real deformation in the Z direction;

[0019] Figure 5 Figure 5 is a specific flowchart of a ground-based radar three-dimensional deformation calculation method provided by an embodiment of the present disclosure;

[0020] Figure 6 Figure 6 is a structural block diagram of a ground-based radar three-dimensional deformation calculation system provided by an embodiment of the present disclosure;

[0021] Figure 7 Figure 7 is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0022] In the following description, specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. However, persons having ordinary skill in the art will appreciate that embodiments of the present disclosure can be practiced without these specific details. In other instances, well-known systems, structures, circuits, and processes have not been described in detail in order to avoid obscuring the description of the present disclosure.

[0023] In order to make the objects, technical solutions and advantages of the present disclosure clearer, the following will be described by specific embodiments in conjunction with the accompanying drawings.

[0024] Reference should be made to Figure 1 , Figure 1 Figure 5 is a flowchart of a ground-based radar three-dimensional deformation calculation method provided by an embodiment of the present disclosure, which comprises:

[0025] S101: Based on the multi-radar multi-angle observation of the deformation information of the same observation target, the multi-angle one-dimensional line-of-sight deformation data of the same observation target is obtained.

[0026] In the present embodiment, the deformation information of a certain target is observed. If the observation target is a mountain landslide, the one-dimensional line-of-sight deformation data of the mountain landslide is calculated.

[0027] The deformation information is a description of the relevant situation embodied by the change of the shape, size, etc. of the target object (i.e. the observation target) under the action of various internal and external forces.

[0028] The one-dimensional line-of-sight deformation data is data that only embodies the deformation of the target object in a single dimension. In actual application, the line-of-sight is often used as a single dimension to obtain data, i.e. only the information of the displacement or deformation of the target object in a certain straight line direction is concerned, such as the straight line direction determined by the radar wave emission direction and the reflected wave receiving direction when the radar is observed.

[0029] Therefore, the radar collects the line-of-sight deformation information of the target object, and one-dimensional line-of-sight deformation data of the target object can be obtained. In addition, the embodiment can use multiple radars to obtain multi-angle one-dimensional line-of-sight deformation data from multiple directions.

[0030] S102: Based on the parameterized least squares method, the multi-angle one-dimensional line-of-sight deformation data is solved to obtain the target three-dimensional deformation data of the same observation target; wherein the solution weight matrix of the parameterized least squares method is determined by the variance component estimation method.

[0031] In the embodiment, the parameterized least squares method is a method for estimating model parameters by minimizing the sum of squares of residuals between observed values and model predicted values.

[0032] Solving is a process of inversely deducing unknown parameters or physical quantities in a model according to known observation data and mathematical models. In the embodiment, the target three-dimensional deformation data of the observation target is solved by the parameterized least squares method according to the calculated one-dimensional line-of-sight deformation data.

[0033] The solution weight matrix is a matrix used to measure the importance of different observation data or model parameters in the solution process of the parameterized least squares method. Each element in the solution weight matrix corresponds to the weight of the corresponding observation data or parameter. The greater the weight, the greater the influence of the data or parameter in the fitting process, and the more important the contribution to the final result. Therefore, by reasonably setting the solution weight matrix, the parameterized least squares method can pay more attention to some key data or parameters, thereby improving the accuracy and reliability of the solution.

[0034] The variance component estimation method is a statistical method for estimating the variance components of different observation data sources or different error sources. In the case of multiple observation data, different types of data can have different accuracy and reliability. The variance component estimation method can analyze and determine the variance of each group of data according to the actual situation of the observation data, and then reasonably allocate the weight according to the variance to improve the accuracy of the overall data processing and analysis.

[0035] Specifically, the steps of the embodiment can be as follows:

[0036] First, the calculated multi-angle one-dimensional line-of-sight deformation data is used as input, and the parameterized least squares method is used for solving;

[0037] Second, the initial three-dimensional deformation data is solved;

[0038] Then, in the solution process of the parameterized least squares method, there is a key step of determining the solution weight matrix;

[0039] Finally, the determination of the solving weight matrix can be obtained by the variance component estimation method, and the determination of the solving weight matrix is beneficial to subsequently obtaining target three-dimensional deformation data more accurate than the initial three-dimensional deformation data.

[0040] In this embodiment, the solving weight matrix is determined according to the variance component estimation method, and the target three-dimensional deformation data of the observed target can be obtained by updating the parameterized least square method through the solving weight matrix. Therefore, the initial three-dimensional deformation data is also updated. Compared with the initial three-dimensional deformation data, the accuracy, reliability and fitting degree of the target three-dimensional deformation data to the actual observed three-dimensional deformation data of the object are improved.

[0041] From the above, it can be concluded that the disclosure adopts the parameterized least square method and determines the solving weight matrix by means of the variance component estimation method for three-dimensional deformation solving, which can comprehensively consider the influence of various factors on different data, reasonably allocate weights, make the solving process more accurate, and improve the reliability of the target three-dimensional deformation data. It can be accurately used in infrastructure health detection monitoring, geological disaster warning and other fields, and provide strong and accurate data support for related decision-making. Therefore, the disclosure can improve the three-dimensional deformation solving precision, thereby improving the monitoring precision and early warning ability of geological disasters.

[0042] In an embodiment of the disclosure, the deformation information of the same observed target is observed by multiple radars at multiple angles to obtain multi-angle one-dimensional line-of-sight deformation data of the same observed target, including:

[0043] The real deformation of the observed target point is projected to the line-of-sight deformation to obtain multi-direction line-of-sight deformation data, and the multi-direction line-of-sight deformation data is multi-angle one-dimensional line-of-sight deformation data;

[0044] Among them, the radar has at least three.

[0045] In this embodiment, the radar can emit electromagnetic wave signals and receive signals reflected back by the target object, so as to monitor the deformation of the target object. For example, in the mountain landslide deformation monitoring scene, it can be used to observe the displacement of the target point on the mountain landslide.

[0046] The line-of-sight deformation data is calculated based on the observation principle of the radar. The displacement, deformation and other data reflecting the deformation of the target object generated by the target point in the direction of the straight line (i.e. the line-of-sight direction) determined by the radar wave emission direction and the reflected wave receiving direction. At the same time, the line-of-sight deformation data is also one-dimensional line-of-sight deformation data.

[0047] The radar involved in the embodiment has three parts, and one-dimensional line-of-sight deformation data in three directions can be obtained, which can be seen from FIG. 2. FIG. 2(a) is the line-of-sight observation result of radar 1, FIG. 2(b) is the line-of-sight observation result of radar 2, and FIG. 2(c) is the line-of-sight observation result of radar 3.

[0048] As can be seen from the above, multiple radars can accurately monitor from a specific direction, providing basic data support for subsequent three-dimensional deformation calculation, which helps to quickly carry out deformation analysis of the observation target.

[0049] In an embodiment of the present disclosure, the multi-angle one-dimensional line-of-sight deformation data is calculated based on a parameterized least squares method to obtain target three-dimensional deformation data of the same observation target, including:

[0050] determining a least squares observation equation;

[0051] determining a fusion space parameter;

[0052] determining a parameterized least squares observation equation based on the least squares observation equation and the fusion space parameter; the parameterized least squares observation equation is a parameterized least squares method;

[0053] inputting the multi-angle one-dimensional line-of-sight deformation data into the parameterized least squares method to determine the target three-dimensional deformation data of the observation target.

[0054] In the embodiment, the least squares observation equation is:

[0055]

[0056]

[0057] wherein, represents a radar one-dimensional line-of-sight deformation data of the target object observed at each time point, , represents a unit direction matrix, represents the three-dimensional deformation data to be solved, represents a residual error, represents one-dimensional line-of-sight deformation data observed by radar 1, represents one-dimensional line-of-sight deformation data observed by radar 2, represents one-dimensional line-of-sight deformation data observed by radar 3.

[0058] The fusion space parameter is a parameter related to the space characteristics. In the scene of three-dimensional deformation of the target object, the space characteristics involve the relationship between different directions (such as east-west, north-south, vertical direction, etc.) and different positions.

[0059] Specifically, the fusion space parameter is determined as follows: ),include:

[0060] Define a row vector As a fusion spatial parameter The submatrix in the matrix, where k represents the number of target points, k={1,2,…,N}. G represents the coordinates of the target point, and is defined as follows:

[0061]

[0062] Fusion spatial parameters Also for designing matrices.

[0063] The parameterized least squares observation equation is:

[0064]

[0065] in, Indicates radar For the one-dimensional line-of-sight deformation data of the observed target point, Represents the coefficient matrix. This represents the design matrix.

[0066]

[0067] Represented as basis functions The linear superposition of can be expressed as:

[0068]

[0069] in, This represents a three-dimensional deformation vector, i.e., three-dimensional deformation data. Represents the design matrix. Represents a parameter vector. Indicates the parameter value. Indicates the degree of the introduced polynomial. express The Column vector.

[0070] It is a block matrix, composed of composition, Indicates the target scatterer to each radar The unit vector matrix, and As shown below:

[0071]

[0072]

[0073] The parameter least square method aims to solve the following equation to :

[0074]

[0075] Taking the multi-angle one-dimensional line-of-sight deformation data as the input of the parameterized least square method, the target three-dimensional deformation data can be determined.

[0076] The target three-dimensional deformation data is the solution of the present disclosure, which can be seen from FIG. 3, wherein FIG. 3(a) is the solution of the parameterized least square method in the X direction based on variance component estimation, FIG. 3(b) is the solution of the parameterized least square method in the Y direction based on variance component estimation, and FIG. 3(c) is the solution of the parameterized least square method in the Z direction based on variance component estimation.

[0077] As can be seen from the above, the parameterized least square method observation equation and the corresponding method constructed by closely combining the spatial parameters and the three-dimensional spatial characteristics can effectively process the solution of the multi-department radar one-dimensional line-of-sight deformation data to the target three-dimensional deformation data, and provide reliable initial data support for subsequent precise analysis of the overall deformation trend of the observed target, evaluation of stability, and prediction of geological disaster risk.

[0078] In an embodiment of the present disclosure, a ground-based radar three-dimensional deformation solving method further comprises:

[0079] Determining an initial variance based on the parameterized least square method;

[0080] Determining an initial weight matrix based on the initial variance.

[0081] In the present embodiment, a weight matrix is introduced, is a submatrix of , and is a diagonal matrix.

[0082]

[0083]

[0084] According to the known conditions above, the parameter vector can be solved by , and then can be solved, and the three-dimensional deformation result can be solved, and the weight matrix represents the initial weight matrix, which is composed of the initial variance.

[0085] ​From the above, it can be concluded that the embodiment determines the initial variance to quantify the dispersion degree of data, and understands the fluctuation characteristics of data. The initial weight matrix is constructed based on the initial variance, and different weights are given according to the difference between the stability and reliability of data. Therefore, in the solving process, the more stable and reliable data is strengthened, and the unstable data is weakened, which effectively improves the accuracy and reliability of the solution.

[0086] In an embodiment of the present disclosure, the solution weight matrix of the parameterized least square method is determined by the variance component estimation method, comprising:

[0087] Iterating the initial variance based on the variance component estimation method;

[0088] If the difference between the adjacent two variances is greater than or equal to the first threshold value, continue iterating;

[0089] If the difference between the adjacent two variances is less than the first threshold value, stop iterating;

[0090] After iteration is completed, the target variance is obtained;

[0091] The solution weight matrix is determined based on the target variance.

[0092] In the embodiment, The covariance matrix is represented, and is defined as follows:

[0093]

[0094] Wherein, The initial standard deviation is represented, and is obtained by the parameterized least square method, The initial weight matrix of the bth radar observation value is represented, and is generally composed of a unit matrix, The unit matrix is represented, and the size is , The observation time value is represented.

[0095]

[0096] The residual vector is calculated depending on the residual projection matrix ,

[0097] The residual vector is defined as,

[0098] The matrix and the vector are constructed:

[0099]

[0100] Wherein, each element is defined as follows:

[0101]

[0102]

[0103] wherein,

[0104] the variance is updated by the following formula:

[0105] Iterate this process until the difference between

[0106] is less than the threshold value The threshold value can be determined according to the magnitude of the empirical variance of various observations. With the iteration of the algorithm, the updated variance component is used to recalculate the weight matrix and is used again to calculate the regression coefficient, so as to ensure that the regression model can be optimized step by step. Using the updated variance value, a new weight matrix is obtained, that is, the weight matrix is solved, which is used for subsequent three-dimensional deformation calculation.

[0107] From the above, it can be seen that the weight matrix is updated by the variance component estimation method in the embodiment, which can solve the problem of unreasonable setting of the weight matrix. Then, the determined calculation weight matrix can reasonably allocate data weights in the parameterized least squares method, improve the accuracy of subsequent three-dimensional deformation calculation, and is beneficial to geological research and disaster warning.

[0108] In an embodiment of the present disclosure, a three-dimensional deformation calculation method of ground-based radar further comprises:

[0109] obtaining a target parameter vector based on the calculation weight matrix;

[0110] updating the initial three-dimensional deformation data based on the target parameter vector to determine target three-dimensional deformation data.

[0111] In the embodiment, first, each weight value in the calculation weight matrix will have different degrees of influence on different data elements or parameters, thereby guiding the generation of the target parameter vector.

[0112] Then, the obtained target parameter vector is used to update the initial three-dimensional deformation data. The deformation values (such as displacement values in different directions, strain values, etc.) in each dimension of the initial three-dimensional deformation data are corrected and improved. Since the target parameter vector is generated by comprehensively reflecting the more accurate data weight relationship reflected by the target weight matrix, it can provide more reasonable and more actual basis for the update of the initial three-dimensional deformation data.

[0113] Finally, the target three-dimensional deformation data which is more accurate and can reflect the actual three-dimensional deformation of the observed target object is determined. The target three-dimensional deformation data can be used for subsequent important application scenarios such as infrastructure health detection, geological disaster warning, etc., and provides reliable data support for related decision-making.

[0114] In an embodiment of the present disclosure, a ground-based radar three-dimensional deformation solving method further comprises:

[0115] determining the real three-dimensional deformation data of the observed target;

[0116] Based on the real three-dimensional deformation data and the target three-dimensional deformation data, the accuracy of the solving result is evaluated.

[0117] In this embodiment, the real three-dimensional deformation data is used as a standard or reference three-dimensional deformation data to evaluate the solving accuracy of the target three-dimensional deformation data.

[0118] The real three-dimensional deformation data of the target observation point is shown in FIG. 4, wherein FIG. 4(a) is the real deformation in the X direction, FIG. 4(b) is the real deformation in the Y direction, and FIG. 4(c) is the real deformation in the Z direction.

[0119] The target three-dimensional deformation data can be solved by steps S101-S102.

[0120] In this embodiment, the root mean squared error (RMSE) is calculated to evaluate the accuracy of the solving result.

[0121]

[0122] wherein, represents the real three-dimensional deformation data, represents the target three-dimensional deformation data, represents the observation time value.

[0123] As can be seen from the above, by comparing and calculating the real three-dimensional deformation data with the target three-dimensional deformation data solved, the solving accuracy can be evaluated, the accuracy of the solving result can be intuitively tested, and reliable basis can be provided for geological disaster warning, so that the relevant departments can make accurate decisions and reduce disaster losses.

[0124] Referring to Figure 6 , Figure 6 is a specific flowchart of a ground-based radar three-dimensional deformation solving method.

[0125] Specifically, the steps of the parameterized least squares method are as follows:

[0126] First, calculate the one-dimensional deformation data of the line of sight of the multiple radars;

[0127] Second, the least squares model is established;

[0128] Third, the spatial parameters are introduced and the parameter vector ;

[0129] Fourth, the three-dimensional displacement vector is obtained by linear superposition;

[0130] Fifth, the design matrix : the number of observation points k and the polynomial degree , are defined as the unit vector matrix;

[0131] Sixth, the initial variance and the initial weight matrix are calculated

[0132] Seventh, the parameter vector is obtained;

[0133] Eighth, the three-dimensional displacement vector is obtained.

[0134] Specifically, the steps of the variance component estimation method are as follows:

[0135] First, the covariance matrix is constructed: ;

[0136] Second, the residual vector , the matrix , and the vector are calculated;

[0137] Third, the variance component is updated

[0138] Fourth, if is not satisfied, the third step is continued;

[0139] Fifth, if is satisfied, the variance and the weight matrix are updated.

[0140] In this embodiment, the initial variance and the initial weight matrix are updated, that is, the updated variance (target variance) and the weight matrix (solved weight matrix), and the new parameter vector can be obtained, and finally the target three-dimensional deformation data is obtained.

[0141] From the above, it can be seen that the target parameter vector is obtained based on the solved weight matrix, which not only can reasonably set the weight, but also can make the target parameter vector more accurate, can improve the final three-dimensional deformation calculation accuracy, and is beneficial to the prediction and prevention of geological disasters.

[0142] A three-dimensional deformation calculation method of ground-based radar corresponding to the above embodiment,Figure 6 A structural block diagram of a ground-based radar three-dimensional deformation solving system is provided for an embodiment of the present disclosure. For ease of illustration, only parts related to the embodiments of the present disclosure are shown. With reference to Figure 6 The ground-based radar three-dimensional deformation solving system 20 includes a data acquisition module 21 and a solving module 22.

[0143] The data acquisition module 21 performs multi-angle observation on deformation information of the same observation target by multiple radars to obtain multi-angle one-dimensional line-of-sight deformation data of the same observation target.

[0144] The solving module 22 comprehensively solves the multi-angle one-dimensional line-of-sight deformation data based on a parameterized least squares method to obtain target three-dimensional deformation data of the same observation target. The solving weight matrix of the parameterized least squares method is determined by a variance component estimation method.

[0145] In an embodiment of the present disclosure, the data acquisition module 21 is specifically further configured to:

[0146] measure the real deformation of the observation target point projected to the line-of-sight to obtain multi-direction line-of-sight deformation data, which is the multi-angle one-dimensional line-of-sight deformation data.

[0147] The radars are at least three.

[0148] In an embodiment of the present disclosure, the solving module 22 is specifically further configured to:

[0149] determine a least squares method observation equation;

[0150] determine a fusion space parameter;

[0151] determine a parameterized least squares method observation equation based on the least squares method observation equation and the fusion space parameter. The parameterized least squares method observation equation is the parameterized least squares method.

[0152] input the multi-angle one-dimensional line-of-sight deformation data into the parameterized least squares method to determine the target three-dimensional deformation data of the same observation target.

[0153] In an embodiment of the present disclosure, a ground-based radar three-dimensional deformation solving system 20 further includes an initial weight determination module.

[0154] The initial weight determination module is configured to determine an initial variance based on the parameterized least squares method.

[0155] determine an initial weight matrix based on the initial variance.

[0156] In an embodiment of the present disclosure, the solving module 22 is specifically further configured to:

[0157] The initial variance is iterated based on the variance component estimation method;

[0158] If the difference between two adjacent variances is greater than or equal to the first threshold during iteration, then continue iterating;

[0159] If the difference between two adjacent variances is less than the first threshold during iteration, then stop the iteration.

[0160] After the iteration is complete, the target variance is obtained;

[0161] The weight matrix is ​​determined based on the target variance.

[0162] In one embodiment of this disclosure, the solution module 23 is further configured to:

[0163] The target parameter vector is obtained by solving the weight matrix;

[0164] The initial three-dimensional deformation data is updated based on the target parameter vector to determine the target three-dimensional deformation data.

[0165] In one embodiment of this disclosure, a ground-based radar three-dimensional deformation calculation system 20 further includes: a calculation result evaluation module;

[0166] The solution result evaluation module is used to determine the true three-dimensional deformation data of the observed target;

[0167] The accuracy of the solution results is evaluated based on the actual 3D deformation data and the target 3D deformation data.

[0168] See Figure 7 , Figure 7 This is a schematic block diagram of an electronic device provided according to an embodiment of the present disclosure. Figure 7 The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of each module / unit in the above system embodiments, for example... Figure 6 The functions of modules 21 and 22 shown.

[0169] It should be appreciated that in the embodiments of the present disclosure, the processor 301 can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0170] The input device 302 can include a touchpad, a fingerprint collection sensor (for collecting fingerprint information and direction information of a fingerprint of a user), a microphone, etc., and the output device 303 can include a display (LCD, etc.), a speaker, etc.

[0171] The memory 304 can include a read-only memory and a random access memory, and provide instructions and data for the processor 301. A portion of the memory 304 can also include a non-volatile random access memory. For example, the memory 304 can also store device type information.

[0172] In specific implementations, the processor 301, the input device 302, and the output device 303 described in the embodiments of the present disclosure can execute the implementation manners described in the first and second embodiments of the ground-based radar three-dimensional deformation calculation method provided by the embodiments of the present disclosure, and can also execute the implementation manners of the electronic device described in the embodiments of the present disclosure, which will not be described here.

[0173] In another embodiment of the present disclosure, a computer readable storage medium is provided, which stores a computer program. The computer program includes program instructions, which, when executed by a processor, implement all or part of the processes of the above-mentioned embodiment methods. The computer program can also instruct related hardware to complete the above-mentioned processes. The computer program can be stored in a computer readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate form. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0174] The computer readable storage medium can be an internal storage unit of the electronic device, such as a hard disk or a memory of the electronic device. The computer readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the computer readable storage medium can include both the internal storage unit and the external storage device of the electronic device. The computer readable storage medium is used to store the computer program and other programs and data required by the electronic device. The computer readable storage medium can also be used to temporarily store data that has been output or will be output.

[0175] Those skilled in the art can appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be realized by electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in general terms in the above description. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present disclosure.

[0176] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic device and the units described above can refer to the corresponding processes in the above-mentioned method embodiments, which will not be described here.

[0177] In several embodiments provided in the present application, it should be understood that the disclosed electronic device and method can be implemented in other manners. For example, the embodiments of the apparatus described above are merely illustrative. For example, the division of the units is only a logical function division. There can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection between the units can be indirect coupling or communication connection through some interfaces, or can be in electrical, mechanical or other forms.

[0178] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments of the present disclosure.

[0179] In addition, each functional unit in the various embodiments of the present disclosure can be integrated in one processing unit, or each unit can exist physically as a separate unit, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware, or in the form of a software functional unit.

[0180] The above is merely specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto, and any skilled person in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present disclosure, and these modifications or replacements should be covered in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

Claims

1. A method for calculating three-dimensional deformation of ground-based radar, characterized in that, include: Based on the deformation information of the same observation target from multiple radars, multi-angle observations are performed to obtain multi-angle one-dimensional line-of-sight deformation data of the same observation target; The multi-angle one-dimensional line-of-sight deformation data are comprehensively solved using the parametric least squares method to obtain the target three-dimensional deformation data of the same observed target; wherein, the solution weight matrix of the parametric least squares method is determined by the variance component estimation method. The method of comprehensively solving the multi-angle one-dimensional line-of-sight deformation data based on parametric least squares method yields the target three-dimensional deformation data of the same observed target, including: Determine the least squares observation equation; Determine the parameters of the fusion space; The parameterized least squares observation equation is determined based on the least squares observation equation and the fused spatial parameters; the parameterized least squares observation equation is the parameterized least squares method. The multi-angle one-dimensional line-of-sight deformation data are input into the parameterized least squares method to determine the target three-dimensional deformation data of the same observation target.

2. The method for calculating three-dimensional deformation of ground-based radar as described in claim 1, characterized in that, The method involves multi-angle observation of the deformation information of the same observation target from multiple radars to obtain multi-angle one-dimensional line-of-sight deformation data of the same observation target, including: The actual deformation of the observed target point is projected onto the deformation along the line of sight to obtain multi-directional line-of-sight deformation data, which is the multi-angle one-dimensional line-of-sight deformation data. The radar system comprises at least three units.

3. The method for calculating three-dimensional deformation of ground-based radar as described in claim 1, characterized in that, Also includes: The initial variance is determined based on the parameterized least squares method described above; The initial weight matrix is ​​determined based on the initial variance.

4. The method for calculating three-dimensional deformation of ground-based radar as described in claim 3, characterized in that, The weight matrix for the parameterized least squares method is determined by the variance component estimation method, including: The initial variance is iterated based on the variance component estimation method described above; If the difference between two adjacent variances is greater than or equal to the first threshold during iteration, then continue iterating; If the difference between two adjacent variances is less than the first threshold during iteration, then stop the iteration. After the iteration is complete, the target variance is obtained; The solution weight matrix is ​​determined based on the target variance.

5. The method for calculating three-dimensional deformation of ground-based radar as described in claim 4, characterized in that, Also includes: The target parameter vector is obtained based on the calculated weight matrix; The initial three-dimensional deformation data is updated based on the target parameter vector to determine the target three-dimensional deformation data.

6. The method for calculating three-dimensional deformation of ground-based radar as described in claim 2, characterized in that, Also includes: Determine the true three-dimensional deformation data of the observed target; The accuracy of the solution results is evaluated based on the actual three-dimensional deformation data and the target three-dimensional deformation data.

7. A ground-based radar three-dimensional deformation calculation system, characterized in that, include: The data acquisition module performs multi-angle observations of the deformation information of the same observation target from multiple radars to obtain multi-angle one-dimensional line-of-sight deformation data of the same observation target. The solution module performs comprehensive solution on the multi-angle one-dimensional line-of-sight deformation data based on the parametric least squares method to obtain the target three-dimensional deformation data of the same observed target; wherein, the solution weight matrix of the parametric least squares method is determined by the variance component estimation method; The solution module is specifically used to determine the least squares observation equations; Determine the parameters of the fusion space; The parameterized least squares observation equation is determined based on the least squares observation equation and the fused spatial parameters; the parameterized least squares observation equation is the parameterized least squares method. The multi-angle one-dimensional line-of-sight deformation data are input into the parameterized least squares method to determine the target three-dimensional deformation data of the same observation target.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

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