Ground-based radar three-dimensional deformation resolving method and system

Abstract

The invention provides a ground-based radar three-dimensional deformation resolving method and system, and belongs to the technical field of ground-based radar deformation measurement, and the method comprises the steps: carrying out the multi-angle observation of the deformation information of a same observation target based on a plurality of radars, and obtaining the multi-angle one-dimensional line-of-sight deformation data of the same observation target; performing comprehensive calculation on the multi-angle one-dimensional sight line direction deformation data based on a parameterized least square method to obtain target three-dimensional deformation data of the same observation target; wherein a resolving weight matrix of the parameterized least square method is determined by a variance component estimation method. According to the ground radar three-dimensional deformation resolving method and system provided by the invention, the three-dimensional deformation resolving precision can be improved, so that the monitoring precision and early warning capability of geological disaster and infrastructure health detection are improved.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of ground-based radar deformation measurement, and more specifically, to a ground-based radar three-dimensional deformation solution method and system. Background Art

[0002] Ground-based radar has the advantages of flexible observation angle, non-contact, and high measurement accuracy. It is an important technical means for high-precision deformation monitoring and is widely used in open-pit mine slopes, landslides, 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 solution method generally adopts equal weight solution, and rarely considers the impact of errors between different radar data on the solution accuracy. There is a situation where the weight matrix is ​​set unreasonably, resulting in low solution accuracy, affecting monitoring accuracy and disaster warning capabilities. Summary of the invention

[0003] The purpose of the present invention is to provide a ground-based radar three-dimensional deformation solution method and system to improve the three-dimensional deformation solution accuracy, thereby improving the disaster monitoring accuracy and early warning capability.

[0004] According to a first aspect of the embodiments of the present disclosure, a method for solving three-dimensional deformation of a ground-based radar is provided, comprising: Based on multiple radars, deformation information of the same observation target is observed from multiple angles 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 based on the parameterized least squares method 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.

[0005] A second aspect of the embodiments of the present disclosure provides a ground-based radar three-dimensional deformation solution system, including: A data acquisition module, based on multiple radars, performs multi-angle observation on the deformation information of the same observation target to obtain multi-angle one-dimensional line-of-sight deformation data of the same observation target; A solution module performs a comprehensive solution on the multi-angle one-dimensional line-of-sight deformation data based on a parameterized least squares method to obtain the target three-dimensional deformation data of the same observed target; wherein the solution weight matrix of the parameterized least squares method is determined by a variance component estimation method.

[0006] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, 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 above-mentioned method for solving three-dimensional deformation of a ground-based radar when executing the computer program.

[0007] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned three-dimensional deformation solution method are implemented.

[0008] The beneficial effects of the three-dimensional deformation solution method and system provided by the embodiments of the present disclosure are: The present disclosure adopts parameterized least squares method and uses variance component estimation method to determine the solution weight matrix for three-dimensional deformation solution, which can comprehensively consider the influence of various factors on different data, reasonably allocate weights, make the solution process more accurate, and improve the reliability of target three-dimensional deformation data. It can be accurately used in infrastructure health detection and monitoring, geological disaster early warning and other fields, and provide powerful and accurate data support for relevant decision-making. Therefore, the present disclosure can improve the accuracy of three-dimensional deformation solution, thereby improving the monitoring accuracy and early warning capability of geological disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic diagram of a flow chart of a method for solving three-dimensional deformation of a ground-based radar provided in one embodiment of the present disclosure; FIG2 is a radar line-of-sight observation result calculated by an embodiment of the present disclosure; wherein FIG2(a) is a radar line-of-sight observation result of radar 1, FIG2(b) is a radar line-of-sight observation result of radar 2, and FIG2(c) is a radar line-of-sight observation result of radar 3; FIG3 is a solution result of the present disclosure; wherein FIG3(a) is a solution result of the parameterized least square method based on variance component estimation in the X direction, FIG3(b) is a solution result of the parameterized least square method based on variance component estimation in the Y direction, and FIG3(c) is a solution result of the parameterized least square method based on variance component estimation in the Z direction; FIG4 is a diagram showing the real deformation of an observation target according to an embodiment of the present disclosure; FIG4(a) is the real deformation in the X direction, FIG4(b) is the real deformation in the Y direction, and FIG4(c) is the real deformation in the Z direction; Figure 5 A schematic diagram of a specific process of a ground-based radar three-dimensional deformation solution method provided by an embodiment of the present disclosure; Figure 6 A structural block diagram of a ground-based radar three-dimensional deformation solution system provided by an embodiment of the present disclosure; Figure 7 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0011] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0012] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0013] Please refer to Figure 1 , Figure 1 A schematic diagram of a flow chart of a method for solving three-dimensional deformation of a ground-based radar provided in an embodiment of the present disclosure, the method comprising: S101: Perform multi-angle observation on deformation information of the same observation target based on multiple radars to obtain multi-angle one-dimensional line-of-sight deformation data of the same observation target.

[0014] In this embodiment, the deformation information of a certain target is observed. If the observed target is a landslide, one-dimensional line-of-sight deformation data of the landslide is calculated.

[0015] Deformation information is a description of the changes in the shape, size, etc. of the target object (i.e., the observed target) when it is subjected to various internal and external forces.

[0016] One-dimensional line-of-sight deformation data is data that reflects the deformation of the target object in a single dimensional direction. In practical applications, line-of-sight is often used as a single dimension to obtain data, that is, only the displacement or deformation information 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 received reflected wave direction during radar observation.

[0017] Therefore, the radar collects the deformation information of the target object in the line of sight and can obtain the one-dimensional line of sight deformation data of the target object. In addition, this embodiment can use multiple radars to obtain multi-angle one-dimensional line of sight deformation data from multiple directions.

[0018] S102: Solving the multi-angle one-dimensional sight-line deformation data based on the parameterized least squares method 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.

[0019] In this embodiment, parameterized least squares is a method for estimating model parameters by minimizing the sum of squared residuals between observed values ​​and model predicted values.

[0020] Solving is the process of inferring unknown parameters or physical quantities in the model based on known observation data and mathematical models. This embodiment solves the target three-dimensional deformation data of the observed target through parameterized least squares method based on the calculated one-dimensional line-of-sight deformation data.

[0021] The solution weight matrix is ​​a matrix used to measure the importance of different observation data or model parameters in the process of solving the parameterized least squares method. Each element in the solution weight matrix corresponds to the weight of the corresponding observation data or parameter. The larger 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, this embodiment can make the parameterized least squares method pay more attention to certain key data or parameters, thereby improving the accuracy and reliability of the solution.

[0022] Variance component estimation is a statistical method used to estimate the variance components of different observation data sources or different error sources. When there are multiple observation data involved, different types of data can have different accuracy and reliability. Variance component estimation can analyze and determine the variance of each group of data based on the actual situation of the observation data, and then reasonably allocate weights based on the variance to improve the accuracy of overall data processing and analysis.

[0023] Specifically, the steps of this embodiment may be as follows: Firstly, the calculated multi-angle one-dimensional line-of-sight deformation data is used as input and solved by using the parameterized least squares method; Secondly, the initial three-dimensional deformation data is solved; Then, in the solution process of parameterized least squares method, a key step is to determine the solution weight matrix; Finally, the determination of the solution weight matrix can be obtained through the variance component estimation method, and the determination of the solution weight matrix is ​​conducive to the subsequent acquisition of target three-dimensional deformation data that is more accurate than the initial three-dimensional deformation data.

[0024] In this embodiment, the 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 squares method through the weight matrix. Therefore, the initial three-dimensional deformation data is also updated. Compared with the initial three-dimensional deformation data, the accuracy, reliability and fit of the target three-dimensional deformation data with the actual observed object three-dimensional deformation data are improved.

[0025] From the above, it can be concluded that the present disclosure adopts the parameterized least squares method and uses the variance component estimation method to determine the solution weight matrix for three-dimensional deformation solution, which can comprehensively consider the impact of various factors on different data, reasonably allocate weights, make the solution process more accurate, and improve the reliability of the target three-dimensional deformation data. It can be accurately used in the fields of infrastructure health detection and monitoring, geological disaster warning, etc., and provide powerful and accurate data support for related decisions. Therefore, the present disclosure can improve the accuracy of three-dimensional deformation solution, thereby improving the monitoring accuracy and early warning capability of geological disasters.

[0026] In one embodiment of the present disclosure, based on multiple radars, multi-angle observation of deformation information of the same observation target is performed to obtain multi-angle one-dimensional line-of-sight deformation data of the same observation target, including: The real deformation of the observation target point is measured and projected into the deformation in the sight direction to obtain multi-directional sight direction deformation data, which are multi-angle one-dimensional sight direction deformation data. Among them, there are at least three radars.

[0027] In this embodiment, the radar can emit electromagnetic wave signals and receive signals reflected by the target object to monitor the deformation of the target object. For example, in a landslide deformation monitoring scenario, it can be used to observe the displacement of the target point on the landslide.

[0028] The line-of-sight deformation data is calculated based on the radar observation principle. It is the displacement and deformation of the target point in the straight line direction (i.e., the line-of-sight direction) determined by the radar wave emission direction and the received reflected wave direction, reflecting the deformation of the target object. At the same time, the line-of-sight deformation data is also one-dimensional line-of-sight deformation data.

[0029] There are three radars involved in this embodiment, which can obtain one-dimensional line-of-sight deformation data in three directions, as shown in Figure 2, where Figure 2 (a) is the line-of-sight observation result of radar 1, Figure 2 (b) is the line-of-sight observation result of radar 2, and Figure 2 (c) is the line-of-sight observation result of radar 3.

[0030] From the above, it can be concluded that multiple radars can accurately monitor from a specific direction, provide basic data support for subsequent three-dimensional deformation solution, and help to quickly carry out deformation analysis of the observed target.

[0031] In one embodiment of the present disclosure, multi-angle one-dimensional sight line deformation data is solved based on the parameterized least squares method to obtain target three-dimensional deformation data of the same observation target, including: Determine the least squares observation equation; Determine fusion space parameters; A parameterized least squares observation equation is determined based on the least squares observation equation and the fusion spatial parameter; the parameterized least squares observation equation is a 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 observed target.

[0032] In this embodiment, the least squares observation equation is:

[0033]

[0034] in, Indicates radar The one-dimensional line-of-sight deformation data of the target object observed at each time point, , represents the unit direction matrix, represents the desired three-dimensional deformation data, represents the residual, represents the one-dimensional line-of-sight deformation data observed by radar 1, represents the one-dimensional line-of-sight deformation data observed by radar 2, Indicates the one-dimensional line-of-sight deformation data observed by the radar 3.

[0035] The fusion space parameters are parameters related to spatial characteristics. In the scenario of three-dimensional deformation of the target object, the spatial characteristics involve different directions (such as east-west, north-south, vertical directions, etc.) and the relationship between different positions.

[0036] Specifically, determine the fusion space parameters ( ),include: Define a row vector , as the fusion space parameter The submatrix in , k represents the number of target points, k={1,2,…,N}, Represents the target point coordinate value, G is defined as follows:

[0037] Fusion space parameters Also the design matrix.

[0038] The parameterized least squares observation equation is:

[0039] in, Indicates radar For the one-dimensional line-of-sight deformation data of the observed target point, represents the coefficient matrix, represents the design matrix.

[0040]

[0041] Represented as basis functions The linear superposition of can be expressed as:

[0042] in, Represents a three-dimensional deformation vector, that is, three-dimensional deformation data, represents the design matrix, represents the parameter vector, Indicates the parameter value. represents the degree of the introduced polynomial, express No. Column vector.

[0043] is a block matrix consisting of composition, Represents the target scatterer to each radar The unit vector matrix of and As shown below:

[0044]

[0045] The purpose of the least squares method is to find to of :

[0046] The multi-angle one-dimensional line-of-sight deformation data is used as the input of the parameterized least squares method to determine the target three-dimensional deformation data.

[0047] The target three-dimensional deformation data is the solution result of the present invention, which can be seen in Figure 3, wherein Figure 3 (a) is the solution result of the parameterized least squares method based on variance component estimation in the X direction, Figure 3 (b) is the solution result of the parameterized least squares method based on variance component estimation in the Y direction, and Figure 3 (c) is the solution result of the parameterized least squares method based on variance component estimation in the Z direction.

[0048] From the above, it can be concluded that the parameterized least squares observation equation and the corresponding method constructed by the close combination of fusion spatial parameters and three-dimensional spatial characteristics can effectively process the solution of one-dimensional line-of-sight deformation data of multiple radars to three-dimensional deformation data of the target, and provide reliable initial data support for the subsequent precise analysis of the overall deformation trend of the observed target, assessment of stability, and prediction of geological disaster risks.

[0049] In one embodiment of the present disclosure, a ground-based radar three-dimensional deformation solution method further includes: Determine the initial variance based on the parametric least squares method; An initial weight matrix is ​​determined based on the initial variance.

[0050] In this embodiment, a weighting matrix is ​​introduced , for The sub-matrix of is a diagonal matrix.

[0051]

[0052]

[0053] According to the above known conditions, The parameter vector can be obtained, and then the , the three-dimensional deformation result can be solved, the weighted matrix Represents the initial weight matrix, which is composed of the initial variance.

[0054] From the above, it can be concluded that the initial variance determined in this embodiment can quantify the degree of data dispersion and understand the volatility characteristics of the data. By constructing the initial weight matrix based on the initial variance, different weights can be assigned according to the stability and reliability differences of the data. Therefore, in the solution process, more stable and reliable data can be strengthened, and the influence of unstable data is weakened, effectively improving the accuracy and reliability of the solution.

[0055] In one embodiment of the present disclosure, the weight matrix of the parameterized least squares method is determined by a variance component estimation method, including: Iterate the initial variance based on the variance component estimation method; If the difference between two adjacent variances during iteration is greater than or equal to the first threshold, continue iterating; If the difference between two adjacent variances during iteration is less than the first threshold, the iteration is stopped; After the iteration is completed, the target variance is obtained; The solution weight matrix is ​​determined based on the target variance.

[0056] In this embodiment, represents the covariance matrix and is defined as follows:

[0057] in, represents the initial standard deviation, obtained by the parametric least squares method, The initial weight matrix representing the bth radar observation value is generally composed of the unit matrix, represents the identity matrix, with a size of , Indicates the observation time value.

[0058]

[0059] Residual vector The calculation depends on the residual projection matrix ,

[0060] The residual vector is defined as,

[0061] Constructing the Matrix and vector :

[0062] The elements are defined as follows:

[0063]

[0064]

[0065] in, Represents the quadratic vector of observation corrections, and the variance is updated by the following formula:

[0066] This process is iterated until The difference is less than the threshold , the selection of thresholds can be determined based on the empirical variance magnitude of each type of observation. As the algorithm iterates, the updated variance components are used to recalculate the weight matrix and then used again to calculate the regression coefficients to ensure that the regression model can be gradually optimized. Using the updated variance values, the new weight matrix is ​​obtained. , that is, solving the weight matrix, which is used for subsequent three-dimensional deformation solution.

[0067] It can be concluded from the above that this embodiment can solve the problem of unreasonable weight matrix setting by updating the weight matrix through the variance component estimation method. Afterwards, the determined solution weight matrix can reasonably allocate data weights in the parameterized least squares method, improve the accuracy of subsequent three-dimensional deformation solution, and is beneficial to geological research and disaster warning.

[0068] In one embodiment of the present disclosure, a ground-based radar three-dimensional deformation solution method further includes: Obtain the target parameter vector based on the solved weight matrix; The initial three-dimensional deformation data is updated based on the target parameter vector to determine the target three-dimensional deformation data.

[0069] In this embodiment, first, each weight value in the weight matrix is ​​solved to have different degrees of influence on different data elements or parameters, thereby guiding the generation of a target parameter vector.

[0070] Then, the initial three-dimensional deformation data is updated using the obtained target parameter vector. The deformation values ​​of each dimension in the initial three-dimensional deformation data (such as displacement and strain values ​​in different directions) are corrected and improved. Since the target parameter vector is generated by integrating the more accurate data weight relationship reflected by the target weight matrix, it can provide a more reasonable and practical basis for updating the initial three-dimensional deformation data.

[0071] Finally, the target 3D deformation data that is more accurate and can truly reflect the actual 3D deformation of the observed target object is determined. The target 3D deformation data can be used in subsequent important application scenarios such as infrastructure health detection and geological disaster warning, providing reliable data support for relevant decision-making.

[0072] In one embodiment of the present disclosure, a ground-based radar three-dimensional deformation solution method further includes: Determine the real three-dimensional deformation data of the observed target; Calculations are performed based on real 3D deformation data and target 3D deformation data to evaluate the accuracy of the solution results.

[0073] In this embodiment, the real three-dimensional deformation data is used as standard or reference three-dimensional deformation data to evaluate the calculation accuracy of the target three-dimensional deformation data.

[0074] The real three-dimensional deformation data of the target observation point is shown in Figure 4, where 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.

[0075] The target three-dimensional deformation data can be obtained by solving steps S101-S102.

[0076] In this embodiment, the root mean square error (RMSE) is calculated to evaluate the accuracy of the solution result.

[0077]

[0078] in, Represents real 3D deformation data, represents the target 3D deformation data, Represents the observation time value.

[0079] From the above, it can be concluded that this embodiment can evaluate the solution accuracy by comparing the real three-dimensional deformation data with the target three-dimensional deformation data obtained by solution, and can intuitively verify the accuracy of the solution results. It can provide a reliable basis for geological disaster warning, enable relevant departments to make accurate decisions, and reduce disaster losses.

[0080] See also Figure 6 , Figure 6 The figure is a schematic diagram of a specific process of solving three-dimensional deformation of ground-based radar.

[0081] Specifically, the steps of parameterized least squares method are as follows: First, calculate the one-dimensional deformation data of multiple radar lines of sight; Second, establish the least squares model; Third, the introduction of spatial parameters and parameter vector ; Fourth, three-dimensional displacement vector Depend on Linear superposition; Fifth, design matrix : Define the number of observation points k and the degree of the polynomial , is the unit vector matrix; Sixth, calculate the initial variance and initial weight matrix Seventh, find the parameter vector ; Eighth, solve the three-dimensional displacement vector .

[0082] Specifically, the steps of the variance component estimation method are as follows: First, construct the covariance matrix: ; Second, calculate the residual vector ,matrix ,vector ; Third, update the variance components

[0083] Fourth, if you are not satisfied , then continue to the third step; Fifth, if satisfied , then update the variance and weight matrices.

[0084] 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 a new parameter vector can be obtained to finally obtain the target three-dimensional deformation data.

[0085] From the above, it can be concluded that obtaining the target parameter vector based on the solution weight matrix can not only set the weight reasonably, but also make the target parameter vector more accurate, which can improve the final three-dimensional deformation solution accuracy and is conducive to the prediction and prevention of geological disasters.

[0086] Corresponding to a ground-based radar three-dimensional deformation solution method in the above embodiment, Figure 6 This is a structural block diagram of a ground-based radar three-dimensional deformation solution system provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 6 The ground-based radar three-dimensional deformation solution system 20 includes: a data acquisition module 21 and a solution module 22.

[0087] The data acquisition module 21 performs multi-angle observation on the deformation information of the same observation target based on multiple radars to obtain multi-angle one-dimensional line-of-sight deformation data of the same observation target; The solution module 22 performs comprehensive solution on the multi-angle one-dimensional sight-line deformation data based on the parameterized least squares method 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.

[0088] In one embodiment of the present disclosure, the data acquisition module 21 is further configured to: The real deformation of the observation target point is measured and projected into the deformation in the sight direction to obtain multi-directional sight direction deformation data, which are multi-angle one-dimensional sight direction deformation data. Among them, there are at least three radars.

[0089] In one embodiment of the present disclosure, the solving module 22 is further configured to: Determine the least squares observation equation; Determine fusion space parameters; A parameterized least squares observation equation is determined based on the least squares observation equation and the fusion spatial parameter; the parameterized least squares observation equation is a 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.

[0090] In one embodiment of the present disclosure, a ground-based radar three-dimensional deformation solving system 20 further includes: an initial weight determination module; An initial weight determination module, used for determining an initial variance based on a parameterized least squares method; An initial weight matrix is ​​determined based on the initial variance.

[0091] In one embodiment of the present disclosure, the solving module 22 is further configured to: Iterate the initial variance based on the variance component estimation method; If the difference between two adjacent variances during iteration is greater than or equal to the first threshold, continue iterating; If the difference between two adjacent variances during iteration is less than the first threshold, the iteration is stopped; After the iteration is completed, the target variance is obtained; The solution weight matrix is ​​determined based on the target variance.

[0092] In one embodiment of the present disclosure, the solving module 23 is further configured to: Obtain the target parameter vector based on the solved weight matrix; The initial three-dimensional deformation data is updated based on the target parameter vector to determine the target three-dimensional deformation data.

[0093] In one embodiment of the present disclosure, a ground-based radar three-dimensional deformation solution system 20 further includes: a solution result evaluation module; The solution result evaluation module is used to determine the real three-dimensional deformation data of the observed target; Calculations are performed based on real 3D deformation data and target 3D deformation data to evaluate the accuracy of the solution results.

[0094] See also Figure 7 , Figure 7 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 7 The electronic device 300 in the embodiment shown 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 memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above-mentioned system embodiments, such as Figure 6The functions of modules 21 to 22 are shown.

[0095] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0096] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0097] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0098] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of a ground-based radar three-dimensional deformation solution method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0099] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0100] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0101] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0102] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0103] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0104] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0105] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0106] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for solving three-dimensional deformation of ground-based radar, characterized in that: include: Based on multiple radars, deformation information of the same observation target is observed from multiple angles 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 based on the parameterized least squares method 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.

2. A ground-based radar three-dimensional deformation solution method as claimed in claim 1, characterized in that: The method of performing multi-angle observation on deformation information of the same observation target based on multiple radars to obtain multi-angle one-dimensional line-of-sight deformation data of the same observation target includes: The real deformation of the observation target point is measured and projected into the deformation in the sight direction to obtain multi-directional sight direction deformation data, wherein the multi-directional sight direction deformation data is the multi-angle one-dimensional sight direction deformation data; Among them, there are at least three radars.

3. The method for solving three-dimensional deformation of ground-based radar according to claim 1, characterized in that: The method of solving the multi-angle one-dimensional sight line deformation data based on the parameterized least squares method to obtain the target three-dimensional deformation data of the same observation target includes: Determine the least squares observation equation; Determine fusion space parameters; Determine 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 the parameterized least squares method; The multi-angle one-dimensional sight-line deformation data is input into the parameterized least squares method to determine the target three-dimensional deformation data of the same observation target.

4. A ground-based radar three-dimensional deformation solution method as claimed in claim 3, characterized in that: Also includes: determining an initial variance based on the parameterized least squares method; An initial weight matrix is ​​determined based on the initial variance.

5. A method for solving three-dimensional deformation of ground-based radar as claimed in claim 4, characterized in that: The weight matrix of the parameterized least squares method is determined by the variance component estimation method, including: Iterating the initial variance based on the variance component estimation method; If the difference between two adjacent variances during iteration is greater than or equal to the first threshold, continue iterating; If the difference between two adjacent variances during iteration is less than the first threshold, the iteration is stopped; After the iteration is completed, the target variance is obtained; A solution weight matrix is ​​determined based on the target variance.

6. A method for solving three-dimensional deformation of ground-based radar as claimed in claim 5, characterized in that: Also includes: Obtaining a target parameter vector based on the solution weight matrix; The initial three-dimensional deformation data is updated based on the target parameter vector to determine the target three-dimensional deformation data.

7. A method for solving three-dimensional deformation of ground-based radar as claimed in claim 2, characterized in that: Also includes: Determining true three-dimensional deformation data of the observed target; Calculation is performed based on the real three-dimensional deformation data and the target three-dimensional deformation data, and the accuracy of the solution result is evaluated.

8. A ground-based radar three-dimensional deformation solution system, characterized in that: include: A data acquisition module, based on multiple radars, performs multi-angle observation on the deformation information of the same observation target to obtain multi-angle one-dimensional line-of-sight deformation data of the same observation target; A solution module performs a comprehensive solution on the multi-angle one-dimensional line-of-sight deformation data based on a parameterized least squares method to obtain the target three-dimensional deformation data of the same observed target; wherein the solution weight matrix of the parameterized least squares method is determined by a variance component estimation method.

9. 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, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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