Gravity vector extraction method, device, computer equipment and storage medium

Abstract

The present application belongs to the field of gravity vector extraction technology, and relates to gravity vector extraction methods, devices, computer equipment and storage media. The method includes: obtaining carrier navigation data and gravity measurement data to obtain gravity vector information, substituting it into the solved normal gravity field to obtain the sampling value of the gravity disturbance vector; according to the carrier navigation data, using the ultra-high-order global gravity field spherical harmonic model to obtain the simulation value of the gravity disturbance vector; performing sequential sampling and segmentation to obtain multiple sampling segments; approximating the sampling value and simulation value of the gravity disturbance vector in each sampling point in any sampling segment to obtain a sequence group of gravity disturbance vectors corresponding to the sampling segment in different directions; performing linear transformation on the sequence group respectively to obtain the transformation value of the gravity disturbance vector corresponding to the sampling segment in different directions; traversing all sampling segments to obtain the predicted value of the gravity disturbance vector. The use of this method can improve the accuracy of the gravity vector measurement results.

Description

Technical Field

[0001] The present application relates to the technical field of gravity vector extraction, and in particular to a gravity vector extraction method, apparatus, computer equipment, and storage medium. Background Art

[0002] With the development of gravity measurement equipment, gravity measurement plays an increasingly important role in fields such as geophysical exploration and has developed rapidly in recent years.

[0003] Currently, most research on gravity measurement, both domestically and internationally, is limited to obtaining scalar information about vertical gravity anomalies. Gravity perturbations acting on inertial devices are equivalent to the effects of acceleration on these devices. Since the inertial device errors in high-precision inertial navigation systems are already controlled at a certain level, the interference of gravity perturbations on high-precision inertial navigation outputs will have a significant impact on navigation results. Accurately measuring gravity and performing targeted compensation will significantly improve the navigation accuracy of high-precision inertial navigation systems. Furthermore, the navigation field directly requires horizontal gravity perturbation measurements for navigation information, so obtaining gravity vector information becomes a key challenge.

[0004] By modeling the gravity disturbance vector and using a SINS / GNSS gravity measurement system, the gravity disturbance vector can be extracted using a direct difference method to obtain the resulting gravity disturbance vector. Limited by the accuracy of inertial devices and external positioning results, the accuracy of attitude estimation, and the influence of noise in the extracted data, the vector information extracted by this method has poor horizontal repeatability. (Gravity measurement results are often evaluated using the internal or external coincidence accuracy of the repeating line. Good repeatability indicates high coincidence accuracy, i.e., accurate measurement.)

[0005] In the existing technology, in order to improve the accuracy of gravity vector measurement results, the following two aspects are usually taken into consideration: first, to solve the problem from the principle of error generation, that is, to model the errors of inertial devices, introduce the main influencing factors into the error model, and reduce the error of gravity vector information extraction by compensating for the device errors; second, to correct the divergent errors by introducing external reference values, improve the consistency of gravity vector information extraction results, and enhance measurement accuracy.

[0006] However, these two methods have their own shortcomings: the first method requires detailed modeling of measurement elements such as inertial devices, which is theoretically optimal. However, it is very difficult to model both the gravity perturbation model and the inertial device error. This leads to inaccurate modeling, the estimation results cannot be guaranteed, and it is impossible to judge whether the estimation is accurate through external information in a timely manner. If the modeling fails, the results may be worse than the normal solution results; the second method uses an external model for correction. This method can effectively control the degree of error divergence and timely correct the gravity vector extraction results. However, this method is based on the overall trend of the default external model and the accuracy of low-frequency information. This leads to if the low-frequency information of the model is wrong, the compensated results will have large systematic differences. Summary of the Invention

[0007] Based on this, it is necessary to provide a gravity vector extraction method, device, computer equipment and storage medium to address the above technical problems, which can improve the accuracy of gravity vector measurement results.

[0008] Gravity vector extraction method, including:

[0009] Acquire carrier navigation data and gravity measurement data;

[0010] According to the carrier navigation data and gravity measurement data, the gravity vector information is obtained, and substituted into the solved normal gravity field to obtain the sampling value of the gravity disturbance vector of the current sampling point in the navigation system;

[0011] According to the carrier navigation data, the ultra-high-order global gravity field spherical harmonic model is used to obtain the simulated value of the gravity disturbance vector at the current sampling point;

[0012] Sequential sampling and segmentation are performed according to the navigation trajectory to obtain multiple sampling segments, each sampling segment including multiple sampling points; the sampled value and the simulated value of the gravity disturbance vector in each sampling point in any sampling segment are approximated to obtain a sequence group of the gravity disturbance vector corresponding to the sampling segment in different directions;

[0013] The sequence groups of gravity disturbance vectors in different directions are linearly transformed to obtain transformation values ​​of the gravity disturbance vectors corresponding to the sampling segments in different directions; all sampling segments are traversed to obtain predicted values ​​of the gravity disturbance vectors.

[0014] In one embodiment, gravity vector information is obtained based on carrier navigation data and gravity measurement data, and substituted into the solved normal gravity field to obtain a sampling value of the gravity disturbance vector of the current sampling point in the navigation system, including:

[0015]

[0016] Where, δg n1is the sampling value of the gravity disturbance vector, is the satellite's acceleration, is the direction cosine matrix, f b is the measured value of the specific force under the load system, is the projection of the Earth's rotational angular velocity in the local geographic coordinate system, is the projection of the angular velocity of the local geographic coordinate system relative to the earth coordinate system in the local geographic coordinate system, is the satellite's velocity, γ n1 is the normal gravity vector.

[0017] In one embodiment, based on the carrier navigation data, a very high-order global gravity field spherical harmonic model is used to obtain a simulated value of the gravity disturbance vector of the current sampling point, including:

[0018]

[0019] Where, δg n1* is the simulated value of the gravity perturbation vector, G is the universal gravitational constant, M is the mass of the earth, r is the radial length from the current point to the center of the reference ellipsoid, n is the order of the spherical harmonic model, m is the degree of the spherical harmonic model, a is the length of the major semi-axis of the reference ellipsoid, is the coefficient of the nth order and mth order perturbation gravity spherical harmonic model, λ is the longitude, is the coefficient of the nth order and mth order perturbation gravity spherical harmonic model, is the nth order and mth degree fully regularized Legendre function, and θ is the pole distance.

[0020] In one embodiment, the sampled value and the simulated value of the gravity disturbance vector in each sampling point in any sampling segment are approximated to obtain a sequence group of gravity disturbance vectors corresponding to the sampling segment in different directions, including:

[0021] The gravity disturbance vector includes: the north component of gravity disturbance, the east component of gravity disturbance and the vertical component of gravity disturbance;

[0022] For any sampling segment of the north component of the gravity disturbance, the sampled values ​​of the gravity disturbance vector at each sampling point constitute a sampling sequence, and the simulated values ​​of the gravity disturbance vector at each sampling point constitute a simulation sequence. The sampling sequence and the simulation sequence are approximated to obtain the north sequence group corresponding to the gravity disturbance vector and the sampling segment;

[0023] For any sampling segment of the eastward component of the gravity disturbance, the sampled values ​​of the gravity disturbance vector at each sampling point constitute a sampling sequence, and the simulated values ​​of the gravity disturbance vector at each sampling point constitute a simulation sequence. The sampling sequence and the simulation sequence are approximated to obtain the eastward sequence group corresponding to the gravity disturbance vector and the sampling segment;

[0024] For any sampling segment of the vertical component of the gravity disturbance, the sampling values ​​of the gravity disturbance vector in each sampling point constitute a sampling sequence, and the simulated values ​​of the gravity disturbance vector in each sampling point constitute a simulation sequence. The sampling sequence and the simulation sequence are approximated to obtain the vertical sequence group corresponding to the gravity disturbance vector and the sampling segment.

[0025] In one embodiment, linear transformation is performed on sequence groups of gravity disturbance vectors in different directions to obtain transformation values ​​of the gravity disturbance vectors corresponding to the sampling segments in different directions, including:

[0026] For the sequence groups of gravity disturbance vectors in different directions, the simulated values ​​of the gravity disturbance vectors are linearly fitted by rotation or translation to obtain the fitting parameters from the sampled values ​​to the simulated values ​​in different directions;

[0027] According to the fitting parameters, a rigid body transformation is performed on the sampling values ​​of the gravity disturbance vector to obtain transformation values ​​of the gravity disturbance vector corresponding to the sampling segments in different directions.

[0028] In one embodiment, obtaining carrier navigation data and gravity measurement data includes:

[0029] Obtain satellite data results and solve them to obtain satellite position information; perform differential calculations on the satellite position information to obtain satellite velocity and acceleration; perform combined inertial navigation and satellite navigation based on the satellite velocity and acceleration to obtain carrier navigation data;

[0030] The gravimeter is used to perform measurement and data processing to obtain gravity measurement data.

[0031] A gravity vector extraction device, comprising:

[0032] An acquisition module, used to acquire carrier navigation data and gravity measurement data;

[0033] The sampling module is used to obtain the gravity vector information based on the carrier navigation data and gravity measurement data, substitute it into the solved normal gravity field, and obtain the sampling value of the gravity disturbance vector of the current sampling point in the navigation system;

[0034] The simulation module is used to obtain the simulated value of the gravity disturbance vector of the current sampling point based on the carrier navigation data using the ultra-high-order global gravity field spherical harmonic model;

[0035] A fitting module is configured to sequentially sample and segment the navigation trajectory to obtain a plurality of sampling segments, each of which includes a plurality of sampling points; approximate the sampled value and the simulated value of the gravity disturbance vector at each sampling point in any sampling segment to obtain a sequence group of gravity disturbance vectors corresponding to the sampling segment in different directions;

[0036] The transformation module is used to linearly transform the sequence groups of the gravity disturbance vector in different directions to obtain the transformation values ​​of the gravity disturbance vector corresponding to the sampling segments in different directions; and traverse all sampling segments to obtain the predicted value of the gravity disturbance vector.

[0037] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0038] Acquire carrier navigation data and gravity measurement data;

[0039] According to the carrier navigation data and gravity measurement data, the gravity vector information is obtained, and substituted into the solved normal gravity field to obtain the sampling value of the gravity disturbance vector of the current sampling point in the navigation system;

[0040] According to the carrier navigation data, the ultra-high-order global gravity field spherical harmonic model is used to obtain the simulated value of the gravity disturbance vector at the current sampling point;

[0041] Sequential sampling and segmentation are performed according to the navigation trajectory to obtain multiple sampling segments, each sampling segment including multiple sampling points; the sampled value and the simulated value of the gravity disturbance vector in each sampling point in any sampling segment are approximated to obtain a sequence group of the gravity disturbance vector corresponding to the sampling segment in different directions;

[0042] The sequence groups of gravity disturbance vectors in different directions are linearly transformed to obtain transformation values ​​of the gravity disturbance vectors corresponding to the sampling segments in different directions; all sampling segments are traversed to obtain predicted values ​​of the gravity disturbance vectors.

[0043] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0044] Acquire carrier navigation data and gravity measurement data;

[0045] According to the carrier navigation data and gravity measurement data, the gravity vector information is obtained, and substituted into the solved normal gravity field to obtain the sampling value of the gravity disturbance vector of the current sampling point in the navigation system;

[0046] According to the carrier navigation data, the ultra-high-order global gravity field spherical harmonic model is used to obtain the simulated value of the gravity disturbance vector at the current sampling point;

[0047] Sequential sampling and segmentation are performed according to the navigation trajectory to obtain multiple sampling segments, each sampling segment including multiple sampling points; the sampled value and the simulated value of the gravity disturbance vector in each sampling point in any sampling segment are approximated to obtain a sequence group of the gravity disturbance vector corresponding to the sampling segment in different directions;

[0048] The sequence groups of gravity disturbance vectors in different directions are linearly transformed to obtain transformation values ​​of the gravity disturbance vectors corresponding to the sampling segments in different directions; all sampling segments are traversed to obtain predicted values ​​of the gravity disturbance vectors.

[0049] The above-mentioned gravity vector extraction method, device, computer equipment and storage medium solve the problems of insufficient accuracy and unclear details of the extraction results of the gravity field spherical harmonic model, and time-varying errors and measurement system differences in the measured data. This application extracts the gravity vector by introducing an external reference value. Compared with the traditional gravity vector extraction method, it retains the high-frequency characteristics of the measured gravity vector information. At the same time, it combines the advantages of the low-frequency trend stability of the global ultra-high-order gravity field spherical harmonic model to control the error of the gravity vector within a certain range. Since the focus of the gravity vector information is the gravity disturbance information in the horizontal direction, and the gravity disturbance in the horizontal direction is actually very small, the range of the system difference will not be very large, and the overall trend of the model is relatively reliable. In addition, a variety of variations are proposed to improve the efficiency of the calculation while ensuring accuracy, and ensure the real-time performance of the gravity vector calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a diagram of an application scenario of a gravity vector extraction method in one embodiment;

[0051] Figure 2 1 is a flow chart of a gravity vector extraction method according to an embodiment;

[0052] Figure 3 Schematic diagram of rigid body approximate fitting in a gravity vector extraction method in one embodiment;

[0053] Figure 4 Schematic diagram of the architecture of a gravity vector extraction method in one embodiment;

[0054] Figure 5 Schematic diagrams of variations of a gravity vector extraction method in one embodiment, wherein (a) is a schematic diagram of mean alignment of the first variation, (b) is a schematic diagram of head alignment of the first variation, (c) is an enlarged schematic diagram of mean alignment of the first variation, (d) is an enlarged schematic diagram of head alignment of the first variation, and (e) is a schematic diagram of head alignment of the third variation;

[0055] Figure 6 is a structural block diagram of a gravity vector extraction device in one embodiment;

[0056] Figure 7 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely for explaining this application and are not intended to limit this application. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in this application without creative work are within the scope of protection of this application.

[0058] It should be noted that all directional indications in the embodiments of the present application (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0059] In addition, the terms "first," "second," and so on, used in this application are for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, features specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "multiple groups" means at least two groups, such as two groups, three groups, and so on, unless otherwise specifically defined.

[0060] In this application, unless otherwise specified or limited, the terms "connect," "fix," etc. should be understood in a broad sense. For example, "fix" can mean a fixed connection, a detachable connection, or an integral connection; it can mean a mechanical connection, an electrical connection, a physical connection, or a wireless communication connection; it can mean a direct connection or an indirect connection through an intermediate medium; it can mean internal communication between two elements or an interaction between two elements, unless otherwise specified. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0061] In addition, the technical solutions between the various embodiments of the present application can be combined with each other, but it must be based on the fact that ordinary technicians in this field can implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0062] The method provided in this application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 via a network. The terminal 102 may include but is not limited to various personal computers, laptops, smart phones, tablet computers, and portable wearable devices. The server 104 may be a server corresponding to various portal websites or a backend of a work system.

[0063] This application provides a gravity vector extraction method, such as Figure 2 As shown, in one embodiment, the method is applied to Figure 1 The following example illustrates the terminal in the example, including:

[0064] Step 202: Acquire carrier navigation data and gravity measurement data.

[0065] Specifically:

[0066] Obtain satellite data results and solve them to obtain satellite position information; perform differential calculations on the satellite position information to obtain satellite velocity and acceleration; perform combined inertial navigation and satellite navigation based on the satellite velocity and acceleration to obtain high-precision carrier navigation data;

[0067] The gravimeter is used to perform measurement and data processing to obtain gravity measurement data.

[0068] It should be noted that obtaining and solving satellite data results, performing differential calculations on satellite position information, performing combined navigation of inertial navigation and satellites, and performing gravimeter measurements and preprocessing are all existing technologies.

[0069] Step 204 : Obtain gravity vector information based on the carrier navigation data and gravity measurement data, substitute it into the solved normal gravity field, and obtain the sampling value of the gravity disturbance vector of the current sampling point in the navigation system.

[0070] Specifically:

[0071]

[0072] Where, δg n1 is the sampling value of the gravity disturbance vector in the navigation system (i.e., the local geographic coordinate system), is the acceleration of the satellite (i.e., carrier), is the direction cosine matrix, f b is the measured value of the specific force under the load system, is the projection of the Earth's rotational angular velocity in the local geographic coordinate system, is the projection of the angular velocity of the local geographic coordinate system relative to the earth coordinate system in the local geographic coordinate system, is the speed of the satellite (i.e., carrier), γ n1 is the normal gravity vector;

[0073] It should be noted that the subscript n1 is the navigation system, e is the earth system, b is the carrier system, and i is the inertial system; and f b Obtained through the inertial navigation system, Calculated from the carrier position and velocity, Acquired via satellite.

[0074] The sampling values ​​of the gravity disturbance vector are solved to obtain the sampling values ​​of the north component of the gravity disturbance, the east component of the gravity disturbance, and the vertical component of the gravity disturbance:

[0075]

[0076]

[0077]

[0078] Where, δg N is the sampling value of the north component of gravity disturbance, is the north component of acceleration, f N is the north component of the specific force, ω ie is the angular velocity of the Earth's rotation, v E is the eastward component of velocity, L is the latitude, v N is the north component of velocity, v D is the celestial component of velocity, R M is the radius of the Earth's meridian, h is the height, R N is the radius of the Earth's ecliptic circle, δg E is the sampling value of the eastward component of the gravity disturbance, is the eastward component of acceleration, f E is the eastward component of the specific force, δg D is the sampling value of the vertical component of gravity disturbance, is the celestial component of acceleration, f D is the celestial component of the specific force, and γ is the normal gravity.

[0079] Step 206 : Based on the carrier navigation data, a very high-order global gravity field spherical harmonic model is used to obtain a simulated value of the gravity disturbance vector at the current sampling point.

[0080] Specifically:

[0081] Establish the EIGEN-6C4 ultra-high-order global gravity field spherical harmonic model and obtain the simulated value of the gravity disturbance vector at the current sampling point;

[0082]

[0083] Where, δg n1* is the simulated value of the gravity perturbation vector, G is the universal gravitational constant, M is the mass of the earth, r is the radial length from the current point to the center of the reference ellipsoid, n is the order of the spherical harmonic model, m is the degree of the spherical harmonic model, a is the length of the major semi-axis of the reference ellipsoid, and are two sets of n-order m-order perturbation gravity spherical harmonic model coefficients, also known as fully normalized Stokes coefficients or earth potential coefficients, λ is the longitude, is the nth order and mth order fully normalized associated Legendre function, θ is the pole distance;

[0084] Need to explain, and It is obtained by subtracting the normal gravity coefficient from the ultra-high-order global gravity field spherical harmonic model coefficient (this coefficient is the existing technology), and the normal gravity coefficient is calculated using the WGS-84 reference ellipsoid.

[0085] The simulated value of the gravity disturbance vector at the current sampling point is solved to obtain the simulated value of the north component of the gravity disturbance, the simulated value of the east component of the gravity disturbance, and the simulated value of the vertical component of the gravity disturbance at the current sampling point:

[0086]

[0087]

[0088]

[0089] Where, is the simulated value of the north component of the gravity disturbance, is the simulated value of the eastward component of the gravity disturbance, is the simulated value of the vertical component of gravity disturbance,

[0090] Step 208: sequentially sample and segment the navigation trajectory to obtain multiple sampling segments, each of which includes multiple sampling points; approximate the sampled value and simulated value of the gravity disturbance vector in each sampling point in any sampling segment to obtain a sequence group of the gravity disturbance vector corresponding to the sampling segment in different directions.

[0091] Specifically:

[0092] Sampling is performed sequentially according to the navigation trajectory and the sampling points are segmented to obtain multiple sampling segments, each sampling segment including multiple sampling points; the sampling points of different sampling segments may overlap or may not overlap.

[0093] The gravity disturbance vector includes: the north component of gravity disturbance, the east component of gravity disturbance and the vertical component of gravity disturbance;

[0094] For any sampling segment of the north component of the gravity disturbance, the sampled values ​​of the gravity disturbance vector at each sampling point constitute a sampling sequence, and the simulated values ​​of the gravity disturbance vector at each sampling point constitute a simulation sequence. The sampling sequence and the simulation sequence are approximated to obtain the north sequence group corresponding to the gravity disturbance vector and the sampling segment;

[0095] For any sampling segment of the eastward component of the gravity disturbance, the sampled values ​​of the gravity disturbance vector at each sampling point constitute a sampling sequence, and the simulated values ​​of the gravity disturbance vector at each sampling point constitute a simulation sequence. The sampling sequence and the simulation sequence are approximated to obtain the eastward sequence group corresponding to the gravity disturbance vector and the sampling segment;

[0096] For any sampling segment of the vertical component of the gravity disturbance, the sampling values ​​of the gravity disturbance vector in each sampling point constitute a sampling sequence, and the simulated values ​​of the gravity disturbance vector in each sampling point constitute a simulation sequence. The sampling sequence and the simulation sequence are approximated to obtain the vertical sequence group corresponding to the gravity disturbance vector and the sampling segment.

[0097] It should be noted that the sequence length of the sampling segment can be set according to actual conditions, for example, 100 sampling points can be set.

[0098] Step 210 , linearly transform the sequence groups of gravity disturbance vectors in different directions to obtain transformation values ​​of gravity disturbance vectors corresponding to sampling segments in different directions; traverse all sampling segments to obtain predicted values ​​of gravity disturbance vectors.

[0099] Specifically:

[0100] For the sequence groups of gravity disturbance vectors in different directions, the simulated values ​​of gravity disturbance vectors are linearly fitted by rotation or translation to obtain the fitting parameters from the sampled values ​​to the simulated values ​​in different directions; for example, y i =a i x i +b i ,i=1,2,3, where y i is the simulation sequence, x i is the sampling sequence, i=1, 2, 3 represent north, east and vertical respectively, a i is the first parameter, b i is the second parameter;

[0101] According to the fitting parameters, the sampling values ​​of the gravity disturbance vector are subjected to rigid body transformation to obtain the transformation values ​​of the gravity disturbance vector corresponding to the sampling segments in different directions.

[0102] More specifically:

[0103] The gravity disturbance vector includes: the north component of gravity disturbance, the east component of gravity disturbance and the vertical component of gravity disturbance;

[0104] For the sequence group of the north component of gravity disturbance, the simulated value of the north component of gravity disturbance is linearly fitted by rotation or translation to obtain the north fitting parameters from the sampling value to the simulated value; according to the north fitting parameters, the sampling value of the north component of gravity disturbance is rigidly transformed to obtain the transformation value of the north component of gravity disturbance and the north component of gravity disturbance corresponding to the sampling segment.

[0105] For the sequence group of the eastward component of the gravity disturbance, the simulated values ​​of the eastward component of the gravity disturbance are linearly fitted by rotation or translation to obtain the eastward fitting parameters from the sampling value to the simulated value; according to the eastward fitting parameters, the sampling values ​​of the eastward component of the gravity disturbance are rigidly transformed to obtain the transformed values ​​of the eastward component of the gravity disturbance and the gravity disturbance corresponding to the sampling segment.

[0106] For the sequence group of vertical components of gravity disturbance, linear fitting is performed on the simulated values ​​of vertical components of gravity disturbance by rotation or translation to obtain the vertical fitting parameters from the sampling value to the simulated value; according to the vertical fitting parameters, rigid body transformation is performed on the sampled values ​​of vertical components of gravity disturbance to obtain the transformed values ​​of vertical components of gravity disturbance corresponding to the sampling segment.

[0107] It should be noted that the gravity disturbance vector includes three components, among which the north component and the east component are horizontal components, and the vertical component is the vertical component.

[0108] It should be noted that the fitting standard can adopt criteria such as minimum mean square error; there are two rigid body transformation methods, head alignment and mean alignment; head alignment is to align the sampling value with the first sampling point of the simulation value, and then rotate it according to the minimum mean square error criterion to obtain the transformation parameters; mean alignment is to calculate the mean of the simulation value in the sampling sequence interval, calculate the mean of the sampling value, make a difference, add the mean difference to the sampling value in the middle of the sampling point, and then rotate the entire series according to the minimum mean square error criterion to obtain the transformation parameters. According to the transformation parameters obtained by the rigid body transformation, the transformation value corresponding to the sampling segment is obtained, specifically: transformation value = transformation parameter * sampling value. As Figure 3 As shown, by analog value (ie Figure 3 The model value in ) and the sample value (i.e. Figure 3 The relationship between the measured values ​​in the transformation parameters is obtained, and the transformation parameters are obtained according to the relationship between the transformation parameters and the sampled values ​​(i.e. Figure 3 The measured values ​​in the prediction) are used to obtain the transformed values ​​of each component (i.e. Figure 3 predicted values ​​in ).

[0109] like Figure 4 The method architecture shown in FIG. 1 is a method for calculating the simulated value δg of the gravity disturbance vector according to satellite data (ie, carrier navigation data). n1* , according to the satellite data (that is, the carrier navigation data) and the gravity data (that is, the gravity measurement data), the sampling value of the gravity disturbance vector δg is obtained n1 , then sample, and perform fitting and rigid transformation on the simulated value and the sampled value to obtain the gravity disturbance in each direction and

[0110] After obtaining the transformed value of the gravity disturbance vector (that is, obtaining a set of transformation parameters from the sampled value to the model value with the number of observations as a threshold), there are four variations (any one of which can be chosen, where a is the basic variation and b, c, and d are improved variations) to traverse all sampling segments and predict and restore the actual gravity disturbance results through the transformation parameters:

[0111] a. The first variant: take the new gravity perturbation result as the true value. If head alignment is used, the last sampling point is used as the initial value of the next set of sampling points and translated. Then smooth according to the set sampling sequence and perform the next stage of linear transformation until the end. If mean alignment is used, the next stage result is not affected by the previous stage. The advantage is that the error does not accumulate, but the disadvantage is that it will lead to discontinuous results. Figure 5 As shown in (a) to (d).

[0112] b. The second variation (improved for real-time performance) introduces a sliding window concept. Previously, the calculation of the next sampling sequence was performed after the sampling sequence was completed. Now, a sliding window is used, starting with 1-100. Then, sampling point 2 is used as the initial value, and processing is continued from 2-101, 3-102, and so on. This method continuously corrects the data of the first sampling point, which is equivalent to correcting the first number with the next 99 numbers. The sliding window can also be set to different lengths, such as 1, 10, 20, and so on.

[0113] c. The third variant: (order reduction) Considering the computational efficiency of the computing equipment, the computational complexity of the ultra-high-order model is huge. If all 2190 orders are calculated, it will not be possible to achieve real-time solution. Therefore, order reduction is used to calculate a limited number of orders. According to the sliding speed of the sliding window and the computational efficiency of the computer, the order is reduced accordingly to ensure computational efficiency and speed. Figure 5 (e) shown.

[0114] d. The fourth variation uses an iterative approach, similar to the real-time variant. After calculating sampling points 1-100, sampling point 50 is used as the true value and the initial value for the second phase, 50-151. After the calculation, all values ​​51-100 are overwritten. The difference between the two sets of data is determined to be less than a set threshold X. If so, the calculation is continued. Otherwise, the calculation is repeated using 49, sliding forward until it is less than the threshold. If so, the operation is repeated in the middle of the second set, and so on until it is less than the threshold. This method ensures that the transformed data is less than a certain threshold. This method preserves high-frequency information as much as possible while ensuring that the low-frequency information fits the model, thus enriching the details of the gravity measurement.

[0115] The test equipment mainly includes a vector gravimeter, a satellite antenna, a carrier, an uninterruptible power supply UPS, and a satellite signal receiver. Steps 202, 204, and 206 are completed through the above equipment. The measurement of the repeated survey line is carried out through the carrier-mounted test. After returning, the data is post-processed to complete steps 208 and 210.

[0116] The above gravity vector extraction method solves the problems of insufficient accuracy and unclear details of the extraction results of the gravity field spherical harmonic model, and time-varying errors and measurement system differences in the measured data. This application extracts the gravity vector by introducing an external reference value, and adopts a strategic fitting method that integrates the measured value and the model value. Compared with the traditional gravity vector extraction method, it retains the high-frequency characteristics of the measured gravity vector information, and at the same time combines the advantages of the low-frequency trend stability of the global ultra-high-order gravity field spherical harmonic model to control the error of the gravity vector within a certain range; since the focus of the gravity vector information is the gravity disturbance information in the horizontal direction, and the gravity disturbance in the horizontal direction is actually very small, the range of the system difference will not be very large, and the overall trend of the model is relatively reliable; in addition, a variety of variations are proposed to improve the efficiency of the calculation while ensuring accuracy, and ensure the real-time performance of the gravity vector calculation.

[0117] It should be understood that although Figure 2 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0118] This application also provides a gravity vector extraction device, such as Figure 6 As shown, in one embodiment, it includes: an acquisition module 602, a sampling module 604, a simulation module 606, a fitting module 608 and a transformation module 610, wherein:

[0119] An acquisition module 602 is configured to acquire carrier navigation data and gravity measurement data;

[0120] The sampling module 604 is used to obtain gravity vector information based on the carrier navigation data and gravity measurement data, substitute it into the solved normal gravity field, and obtain the sampling value of the gravity disturbance vector of the current sampling point in the navigation system;

[0121] The simulation module 606 is used to obtain a simulated value of the gravity disturbance vector of the current sampling point using a very high-order global gravity field spherical harmonic model based on the carrier navigation data;

[0122] The fitting module 608 is configured to sequentially sample and segment the navigation trajectory to obtain multiple sampling segments, each of which includes multiple sampling points; approximate the sampled value and the simulated value of the gravity disturbance vector at each sampling point in any sampling segment to obtain a sequence group of gravity disturbance vectors corresponding to the sampling segment in different directions;

[0123] The transformation module 610 is used to perform linear transformation on the sequence groups of the gravity disturbance vector in different directions to obtain the transformation values ​​of the gravity disturbance vector corresponding to the sampling segments in different directions; and traverse all sampling segments to obtain the predicted value of the gravity disturbance vector.

[0124] The specific definitions of the gravity vector extraction device can be found in the definitions of the gravity vector extraction method above and will not be repeated here. Each module in the above-described device may be implemented in whole or in part via software, hardware, or a combination thereof. Each of the above-described modules may be embedded in or independent of a processor in a computer device in hardware form, or may be stored in a memory in the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0125] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a gravity vector extraction method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0126] Those skilled in the art will understand that Figure 7The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0127] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the method in the above embodiment when executing the computer program.

[0128] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method in the above embodiment are implemented.

[0129] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0130] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0131] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A gravity vector extraction method, characterized in that: include: Acquire carrier navigation data and gravity measurement data; According to the carrier navigation data and gravity measurement data, the gravity vector information is obtained, and substituted into the solved normal gravity field to obtain the sampling value of the gravity disturbance vector of the current sampling point in the navigation system; According to the carrier navigation data, the ultra-high-order global gravity field spherical harmonic model is used to obtain the simulated value of the gravity disturbance vector at the current sampling point; Sequential sampling and segmentation are performed according to the navigation trajectory to obtain multiple sampling segments, each sampling segment including multiple sampling points; Approximating the sampled value and the simulated value of the gravity disturbance vector in each sampling point in any sampling segment to obtain a sequence group of the gravity disturbance vector corresponding to the sampling segment in different directions; The sequence groups of gravity disturbance vectors in different directions are linearly transformed to obtain transformation values ​​of the gravity disturbance vectors corresponding to the sampling segments in different directions; all sampling segments are traversed to obtain predicted values ​​of the gravity disturbance vectors.

2. The gravity vector extraction method according to claim 1, characterized in that: According to the carrier navigation data and gravity measurement data, the gravity vector information is obtained and substituted into the solved normal gravity field to obtain the sampling value of the gravity disturbance vector of the current sampling point in the navigation system, including: Where, δg n1 is the sampling value of the gravity disturbance vector, is the satellite's acceleration, is the direction cosine matrix, f b is the measured value of the specific force under the load system, is the projection of the Earth's rotational angular velocity in the local geographic coordinate system, is the projection of the angular velocity of the local geographic coordinate system relative to the earth coordinate system in the local geographic coordinate system, is the satellite's velocity, γ n1 is the normal gravity vector.

3. The gravity vector extraction method according to claim 2, characterized in that: According to the carrier navigation data, the ultra-high-order global gravity field spherical harmonic model is used to obtain the simulated value of the gravity disturbance vector at the current sampling point, including: Where, δg n1* is the simulated value of the gravity perturbation vector, G is the universal gravitational constant, M is the mass of the earth, r is the radial length from the current point to the center of the reference ellipsoid, n is the order of the spherical harmonic model, m is the degree of the spherical harmonic model, a is the length of the major semi-axis of the reference ellipsoid, is the coefficient of the nth order and mth order perturbation gravity spherical harmonic model, λ is the longitude, is the coefficient of the nth order and mth order perturbation gravity spherical harmonic model, is the nth order and mth degree fully regularized Legendre function, and θ is the pole distance.

4. The gravity vector extraction method according to any one of claims 1 to 3, characterized in that: Approximating the sampled value and the simulated value of the gravity disturbance vector in each sampling point in any sampling segment to obtain a sequence group of gravity disturbance vectors corresponding to the sampling segment in different directions, including: The gravity disturbance vector includes: the north component of gravity disturbance, the east component of gravity disturbance and the vertical component of gravity disturbance; For any sampling segment of the north component of the gravity disturbance, the sampled values ​​of the gravity disturbance vector at each sampling point constitute a sampling sequence, and the simulated values ​​of the gravity disturbance vector at each sampling point constitute a simulation sequence. The sampling sequence and the simulation sequence are approximated to obtain the north sequence group corresponding to the gravity disturbance vector and the sampling segment; For any sampling segment of the eastward component of the gravity disturbance, the sampled values ​​of the gravity disturbance vector at each sampling point constitute a sampling sequence, and the simulated values ​​of the gravity disturbance vector at each sampling point constitute a simulation sequence. The sampling sequence and the simulation sequence are approximated to obtain the eastward sequence group corresponding to the gravity disturbance vector and the sampling segment; For any sampling segment of the vertical component of the gravity disturbance, the sampling values ​​of the gravity disturbance vector in each sampling point constitute a sampling sequence, and the simulated values ​​of the gravity disturbance vector in each sampling point constitute a simulation sequence. The sampling sequence and the simulation sequence are approximated to obtain the vertical sequence group corresponding to the gravity disturbance vector and the sampling segment.

5. The gravity vector extraction method according to any one of claims 1 to 3, characterized in that: Performing linear transformation on the sequence groups of gravity disturbance vectors in different directions to obtain transformation values ​​of the gravity disturbance vectors corresponding to the sampling segments in different directions, including: For the sequence groups of gravity disturbance vectors in different directions, the simulated values ​​of the gravity disturbance vectors are linearly fitted by rotation or translation to obtain the fitting parameters from the sampled values ​​to the simulated values ​​in different directions; According to the fitting parameters, a rigid body transformation is performed on the sampling values ​​of the gravity disturbance vector to obtain transformation values ​​of the gravity disturbance vector corresponding to the sampling segments in different directions.

6. The gravity vector extraction method according to any one of claims 1 to 3, characterized in that: Acquire carrier navigation data and gravity measurement data, including: Obtain satellite data results and solve them to obtain satellite position information; perform differential calculations on the satellite position information to obtain satellite velocity and acceleration; perform combined inertial navigation and satellite navigation based on the satellite velocity and acceleration to obtain carrier navigation data; The gravimeter is used to perform measurement and data processing to obtain gravity measurement data.

7. Gravity vector extraction device, characterized in that, include: An acquisition module, used to acquire carrier navigation data and gravity measurement data; The sampling module is used to obtain the gravity vector information based on the carrier navigation data and gravity measurement data, substitute it into the solved normal gravity field, and obtain the sampling value of the gravity disturbance vector of the current sampling point in the navigation system; The simulation module is used to obtain the simulated value of the gravity disturbance vector of the current sampling point based on the carrier navigation data using the ultra-high-order global gravity field spherical harmonic model; A fitting module is used to sequentially sample and segment the navigation trajectory to obtain multiple sampling segments, each of which includes multiple sampling points; Approximating the sampled value and the simulated value of the gravity disturbance vector in each sampling point in any sampling segment to obtain a sequence group of the gravity disturbance vector corresponding to the sampling segment in different directions; The transformation module is used to linearly transform the sequence groups of the gravity disturbance vector in different directions to obtain the transformation values ​​of the gravity disturbance vector corresponding to the sampling segments in different directions; and traverse all sampling segments to obtain the predicted value of the gravity disturbance vector.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, 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 6 are implemented.

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