CN115393547B - Omnidirectional filtering method and system for lunar satellite gravity anomaly data - Google Patents
Omnidirectional filtering method and system for lunar satellite gravity anomaly data Download PDFInfo
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Abstract
The invention provides an omnidirectional filtering method for lunar satellite gravity anomaly data, which comprises the following steps: dividing lunar satellite gravity anomaly grid data into a plurality of sub-areas; carrying out unidirectional denoising on data in the range of the filtering window, and sliding the filtering window until all data in the sub-area are traversed to obtain a unidirectional denoising result of each sub-area; gradually rotating the data of each sub-area according to a preset angle until denoising in all preset angle directions is completed; splicing the obtained all-directional de-noising sub-area data to obtain a primary de-noising result; and outputting a final denoising result when the denoising result meets a preset condition. According to the invention, unidirectional denoising is carried out on the data in the filtering window range according to a preset angle, and finally omnidirectional denoising data are obtained, so that the strip interference and high-frequency noise in the lunar satellite gravity abnormal data can be removed, and reliable data support is provided for the aspects of subsequent gravity data processing, inversion, geological structure interpretation and the like.
Description
Technical Field
The invention relates to the technical field of satellite gravity data processing, in particular to an omnidirectional filtering method and system for lunar satellite gravity anomaly data.
Background
The orbit perturbation during the satellite operation is obtained by utilizing the satellite gravity measurement technology, and the method can be used for constructing a moon gravity field model. The moon gravity field model is composed of a group of regularization spherical harmonic coefficients and is used for approximately describing gravity anomaly at any position on the moon, and the higher the order of the model is, the higher the accuracy of correspondingly described gravity anomaly data is. With the development of the space technology, the spherical harmonic order of the lunar gravity field model is continuously improved, and the distance between grids of the gravity anomaly data calculated by the lunar gravity field model is correspondingly reduced. At present, the lunar gravity field model with the highest precision is 1500 orders, and the corresponding lunar gravity anomaly data grid interval is 3.6km. However, in the process of improving the spherical harmonic order of the gravity field model, due to the correlation between the odd order item and the even order item, the flight orbit change, the instrument vibration and other reasons, the solved gravity anomaly data has serious stripe interference and high-frequency random noise, and the subsequent data processing and interpretation are greatly influenced, so that the lunar satellite gravity anomaly data needs to be denoised first to obtain more reliable data, and the subsequent processing and interpretation are guaranteed.
At present, various satellite gravity data denoising methods exist at home and abroad, and can be roughly divided into two categories, namely digital filtering and order truncation according to the realization principle. The digital filtering method is to apply gaussian filtering (Swenson and Wahr, 2002), sliding decorrelation (Chambers, 2006) and other methods to smooth and denoise data, and these methods are generally applied to the field of earth time-varying gravity data; the order truncation method directly discards the higher order of the error power spectrum in the gravity field model without participating in subsequent calculation. The most important problems of the methods are that the noise can not be effectively removed, namely, the stripe interference and the random noise can be removed as much as possible under the condition of not losing the effective signal amplitude. Because the interference form of the satellite gravity abnormal data strip is similar to the interference of the survey line strip before the aviation geophysical data are preprocessed, a leveling algorithm introduced into the aviation geophysical data processing is considered. Among them, beiki et al (2010) developed a unidirectional differential polynomial fitting filtering method (DPF), which is better in the effect of removing the band interference, however, the method is only suitable for the line measurement direction, the satellite gravity data has no line measurement arrangement, and the band interference is mostly distributed along multiple directions. Therefore, it is important to develop a method for eliminating multi-directional stripe errors and high-frequency noise without losing effective signals of satellite gravity abnormal data.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide an omnidirectional filtering method and system for lunar satellite gravity anomaly data.
In order to achieve the purpose, the invention provides the following scheme:
an omnidirectional filtering method for lunar satellite gravity anomaly data comprises the following steps:
step 1: acquiring lunar satellite gravity anomaly grid data to be processed;
and 2, step: dividing the lunar satellite gravity anomaly grid data into a plurality of sub-areas;
and 3, step 3: creating a filtering window, carrying out unidirectional denoising on data in the range of the filtering window, sliding the filtering window until all data in the sub-area are traversed, and obtaining a unidirectional denoising result of each sub-area;
and 4, step 4: performing coordinate rotation on the data of each sub-area according to a preset angle, rescreening the gridding, and returning to the step 3 until the denoising in all preset angle directions is completed, so as to obtain the data of each sub-area subjected to omnidirectional denoising;
and 5: splicing all sub-area data subjected to omnidirectional denoising to obtain a first denoising result;
step 6: and judging whether the first denoising result meets an error condition, if not, taking the first denoising result as lunar satellite gravity abnormal grid data to be processed, and returning to the step 2 until outputting the omnidirectional denoising data meeting the error condition.
Preferably, the performing unidirectional denoising on the data within the filtering window range includes:
establishing a filtering window; the filtering window comprises a longitudinal one-dimensional window and a square two-dimensional window, and the longitudinal one-dimensional window is positioned in the center of the square two-dimensional window;
respectively carrying out polynomial fitting on the data in the longitudinal one-dimensional window and the data in the square two-dimensional window to obtain data fitting results;
determining an error value according to the data fitting result;
and obtaining unidirectional denoising data in a corresponding filtering window according to the error value.
Preferably, the data in the longitudinal one-dimensional window and the data in the square two-dimensional window are respectively subjected to polynomial fitting to obtain data fitting results, and the data fitting results include:
the formula is adopted:
obtaining a data fitting result; wherein, f (x) 1D Fitting the data for a longitudinal one-dimensional window, f (x, y) 2D Is the data fitting result of a square two-dimensional window, x and y are the coordinate positions of data in a grid respectively, i and j are polynomial times, n is the maximum time, i + j is less than or equal to n, a i Is a univariate polynomial x i Coefficient of term fit, b i,j Is a binary polynomial x i y j The term fitting coefficient.
Preferably, the determining an error value according to the data fitting result includes:
the formula is adopted:
e(x,y)＝a 0 -b 0,0
determining an error value; wherein e (x, y) is the error of the (x, y) position, a 0 Fitting constant term coefficients to longitudinal one-dimensional filter window data, b 0,0 Constant term coefficients are fitted to the square two-dimensional filter window data.
The invention also provides an omnidirectional filtering system for lunar satellite gravity anomaly data, which comprises:
the data acquisition module is used for acquiring lunar satellite gravity anomaly grid data to be processed;
the data subdivision module is used for subdividing the lunar satellite gravity anomaly grid data into a plurality of sub-areas;
the unidirectional denoising module is used for creating a filtering window, performing unidirectional denoising on the data in the range of the filtering window, sliding the filtering window until all the data in the sub-area are traversed, and obtaining a unidirectional denoising result of each sub-area;
the omnidirectional denoising module is used for performing coordinate rotation on the data of each sub-area according to a preset angle, rescreening the gridding, returning to the unidirectional denoising module until the denoising in all the preset angle directions is completed, and obtaining the data of each sub-area subjected to omnidirectional denoising;
the data splicing module is used for splicing the data of each sub-area subjected to omnidirectional denoising to obtain a first denoising result;
and the inspection iteration module is used for judging whether the first denoising result meets an error condition, if not, taking the first denoising result as lunar satellite gravity abnormal grid data to be processed, and returning the lunar satellite gravity abnormal grid data to the data subdivision module for iteration again until omnidirectional denoising data meeting the error condition are output.
Preferably, the unidirectional denoising module includes:
the filtering window construction unit is used for establishing a filtering window; the filtering window comprises a longitudinal one-dimensional window and a square two-dimensional window, and the longitudinal one-dimensional window is positioned in the center of the square two-dimensional window;
the polynomial fitting unit is used for respectively performing polynomial fitting on the data in the longitudinal one-dimensional window and the data in the square two-dimensional window to obtain a data fitting result;
an error value determining unit for determining an error value according to the data fitting result;
and the denoising unit is used for obtaining unidirectional denoising data in the corresponding filtering window according to the error value.
Preferably, the polynomial fitting unit includes:
a data fitting subunit for employing the formula:
obtaining a data fitting result; wherein, f (x) 1D Fitting the data for a longitudinal one-dimensional window, f (x, y) 2D Is the data fitting result of a square two-dimensional window, x and y are the coordinate positions of data in a grid respectively, i and j are polynomial times, n is the maximum time, i + j is less than or equal to n, a i Is a univariate polynomial x i Coefficient of term fit, b i,j Is a binary polynomial x i y j The term fitting coefficient.
Preferably, the error value determination unit includes:
an error value determining subunit configured to use the formula:
e(x,y)＝a 0 -b 0,0
determining an error value; wherein e (x, y) is the error of the (x, y) position, a 0 Fitting constant term coefficients to longitudinal one-dimensional filter window data, b 0,0 Constant term coefficients are fitted to the square two-dimensional filter window data.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
compared with the prior art, the omni-directional filtering method and system for the lunar satellite gravity anomaly data have the advantages that the data in the filtering window range are subjected to unidirectional de-noising according to the preset angle, the omni-directional de-noising data are finally obtained, strip interference and high-frequency noise in the lunar satellite gravity anomaly data can be removed, and reliable data support is provided for the aspects of subsequent gravity data processing, inversion, geological structure explanation and the like.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of an omnidirectional filtering method for lunar satellite gravity anomaly data according to the present invention;
FIG. 2 is a schematic diagram of the unidirectional denoising principle provided in the present invention; wherein dx and dy are grid spacing in x and y directions of data respectively, and r is a filtering half-window parameter.
FIG. 3 is a Bruger gravity anomaly map of an original moon Ramkeregeion satellite provided by the present invention;
FIG. 4 is a Bru mkerregregion satellite Booth anomaly map of the moon after the omni-directional filtering and denoising provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The terms "first," "second," "third," and "fourth," etc. in the description and claims of this application and in the accompanying drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, the inclusion of a list of steps, processes, methods, etc. is not limited to only those steps recited, but may alternatively include additional steps not recited, or may alternatively include additional steps inherent to such processes, methods, articles, or devices.
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, the present invention is described in detail with reference to the accompanying drawings and the detailed description thereof.
Referring to fig. 1, an omnidirectional filtering method for lunar satellite gravity anomaly data includes:
step 1: acquiring lunar satellite gravity anomaly grid data to be processed;
step 2: dividing the lunar satellite gravity anomaly grid data into a plurality of sub-areas;
further, in step 2, the grid number of the sub-area is determined according to the actual memory condition of the computer and the total grid number of the input data; secondly, in order to enable the subsequent sub-regions to be smoothly spliced, a certain overlapping area needs to be ensured among the sub-regions; furthermore, the sub-regions are numbered in the order of their position in the x-direction and the y-direction in the mesh data, and since the total data mesh number is not necessarily an integer multiple of the mesh number of the sub-regions, the sizes of the sub-regions at the data edge may not coincide with those of the other sub-regions.
And step 3: creating a filtering window, carrying out unidirectional denoising on data in the range of the filtering window, sliding the filtering window until all data in the sub-area are traversed, and obtaining a unidirectional denoising result of each sub-area;
step 3 of the present invention includes:
establishing a filtering window; the filtering window comprises a longitudinal one-dimensional window and a square two-dimensional window, and the longitudinal one-dimensional window is positioned in the center of the square two-dimensional window;
in practical application, selecting a proper width r of a filtering half window, establishing a longitudinal one-dimensional window with the length of 2 · r +1 and a square two-dimensional window with the side length of 2 · r +1, enabling the one-dimensional window to be positioned at the center of the two-dimensional window (as shown in figure 2), respectively carrying out polynomial fitting on data in the two windows, subtracting a data fitting constant term coefficient of the square two-dimensional filtering window at a corresponding position from a data fitting constant term coefficient of the longitudinal one-dimensional filtering window to determine the error size, and subtracting the error from input data to obtain denoised data in the one-dimensional filtering window. The size of the width r of the filtering half window is directly related to the final denoising effect, and the result is excessively smooth and more effective signals are lost due to the excessively large half window; if the half-window is too small, the banding interference cannot be eliminated. Therefore, the most appropriate filtering half-window is selected by referring to factors such as data grid spacing, stripe interference width and the like and adopting a trial and error method.
Particularly, because the one-dimensional window is in the center of the two-dimensional window, in order to make the sub-region edge data normally denoise, a range of a filtering half-window r needs to be expanded on the sub-region, and the expansion adopts a two-dimensional boundary value replication method, namely, the gravity abnormal value of the edge is directly replicated in the expansion region.
Further, respectively carrying out polynomial fitting on the data in the longitudinal one-dimensional window and the data in the square two-dimensional window to obtain a data fitting result; the calculation formula is as follows:
wherein, f (x) 1D Fitting the data for a longitudinal one-dimensional window, f (x, y) 2D Is the data fitting result of a square two-dimensional window, x and y are the coordinate positions of data in a grid respectively, i and j are polynomial times, n is the maximum time, i + j is less than or equal to n, a i Is a univariate polynomial x i Coefficient of term fit, b i,j Is a binary polynomial x i y j The term fitting coefficient.
Determining an error value according to the data fitting result; the error value is calculated by the formula:
e(x,y)＝a 0 -b 0,0
wherein e (x, y) is the error of the (x, y) position, a 0 Fitting constant term coefficients to the longitudinal one-dimensional filtered window data, b 0,0 Constant term coefficients are fitted to the square two-dimensional filter window data.
And obtaining unidirectional denoising data in a corresponding filtering window according to the error value.
And 4, step 4: performing coordinate rotation on the data of each sub-area according to a preset angle, rescreening the gridding, and returning to the step 3 until the denoising in all preset angle directions is completed, so as to obtain the data of each sub-area subjected to omnidirectional denoising;
furthermore, in step 4, a plurality of preset angles (such as 0 °, 30 °, 60 °, 90 °, -30 °, -60 °) can be selected for the sub-region data according to the lunar satellite gravity anomaly data band interference characteristics, so that the sub-region omnidirectional filtering denoising is realized.
And 5: splicing all sub-area data subjected to omnidirectional denoising to obtain a first denoising result;
further, in step 5, the present invention needs to reset the sub-regions according to the corresponding positions before the partitioning, and the data of the corresponding positions of the overlapping portions are averaged.
Step 6: and judging whether the first denoising result meets an error condition, if not, taking the first denoising result as lunar satellite gravity abnormal grid data to be processed, and returning to the step 2 until outputting the omnidirectional denoising data meeting the error condition. It should be noted that the error condition may be iteration times manually set in advance according to experience, or may be a root mean square error tolerance, that is, it satisfies that either the iteration times limit or the root mean square error limit can be checked, and a final omnidirectional filtering denoising result is output.
The following further describes the denoising process of the present invention with reference to specific embodiments:
the method comprises the following steps: and reading lunar satellite gravity anomaly grid data to be processed in a research area (see figure 3, the total grid number of the data is 296 multiplied by 108). Further, the bog gravity anomaly data in this embodiment is calculated by the moon GRGM1200B model.
Step two: dividing the grid data into 6 × 3 sub-areas, wherein the grid number of the sub-areas except the edge sub-areas is 70 × 70, and the overlapped part is 25 grids;
step three: selecting a filtering half window as 8, carrying out unidirectional denoising on data in a filtering window range, and sliding two denoising windows in sub-regions to further obtain unidirectional denoising results of all data in each sub-region;
step four: gradually rotating the data of each subarea according to specific angles of 0 degrees, 30 degrees, 60 degrees, 90 degrees, 30 degrees and 60 degrees, re-gridding, and repeating the unidirectional denoising step of the subareas to perform unidirectional denoising on the data until all the omnidirectional denoising at the specific angles is completed;
step five: splicing the data of each sub-area subjected to omnidirectional denoising to obtain a first denoising result of the whole area;
step six: and (3) checking the effect of the first omnidirectional filtering denoising result, and finally outputting an omnidirectional filtering denoising result (see fig. 4) when the root mean square error meets the condition after 3 iterations.
According to the specific embodiment provided by the invention, the invention discloses the following beneficial effects:
1) The invention realizes an omnidirectional denoising method suitable for lunar satellite gravity anomaly data containing stripe interference and random noise.
2) The invention utilizes the subdivision scheme of the sliding subarea, reduces the occupation amount of the computer memory in the denoising process, and greatly improves the denoising speed.
3) The invention adopts an iterative algorithm, tests the omnidirectional denoising effect of each iteration, and effectively improves the denoising effect.
The invention also provides an omnidirectional filtering system for lunar satellite gravity anomaly data, which comprises:
the data acquisition module is used for acquiring lunar satellite gravity anomaly grid data to be processed;
the data subdivision module is used for subdividing the lunar satellite gravity anomaly grid data into a plurality of sub-areas;
the unidirectional denoising module is used for carrying out unidirectional denoising on the data in the range of the filtering window, sliding the filtering window until all the data in the sub-area are traversed, and obtaining a unidirectional denoising result of each sub-area;
the omnidirectional denoising module is used for gradually rotating the data of each sub-area according to a preset angle, returning to the unidirectional denoising module until unidirectional denoising of all preset angles is finished, and obtaining the data of each sub-area subjected to omnidirectional denoising;
the data splicing module is used for splicing all sub-area data subjected to omnidirectional denoising to obtain a first denoising result;
and the inspection iteration module is used for judging whether the first denoising result meets an error condition or not, taking the first denoising result as lunar satellite gravity abnormal grid data to be processed if the first denoising result does not meet the error condition, and returning to the data subdivision module until outputting omnidirectional denoising data meeting the error condition.
Preferably, the unidirectional denoising module includes:
the filtering window construction unit is used for establishing a filtering window; the filtering window comprises a longitudinal one-dimensional window and a square two-dimensional window, and the longitudinal one-dimensional window is positioned in the center of the square two-dimensional window;
the polynomial fitting unit is used for respectively performing polynomial fitting on the data in the longitudinal one-dimensional window and the data in the square two-dimensional window to obtain a data fitting result;
an error value determining unit for determining an error value according to the data fitting result;
and the denoising unit is used for obtaining unidirectional denoising data in the corresponding filtering window according to the error value.
Preferably, the polynomial fitting unit includes:
a data fitting subunit for employing the formula:
obtaining a data fitting result; wherein, f (x) 1D Fitting the data for a longitudinal one-dimensional window, f (x, y) 2D Is the data fitting result of a square two-dimensional window, x and y are the coordinate positions of data in a grid respectively, i and j are polynomial times, n is the maximum time, i + j is less than or equal to n, a i Is a univariate polynomial x i Coefficient of term fit, b i,j Is a binary polynomial x i y j The term fitting coefficient.
Preferably, the error value determination unit includes:
an error value determining subunit configured to use the formula:
e(x,y)＝a 0 -b 0,0
determining an error value; wherein e (x, y) is the error of the (x, y) position, a 0 Fitting constant term coefficients to longitudinal one-dimensional filter window data, b 0,0 Constant term coefficients are fitted to the square two-dimensional filter window data.
According to the method, unidirectional denoising is carried out on the data in the filtering window range according to the preset angle, and the omnidirectional denoising data is finally obtained, so that the strip interference and high-frequency noise in the lunar satellite gravity anomaly data can be removed, and reliable data support is provided for the aspects of subsequent gravity data processing, inversion, geological structure explanation and the like.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The method disclosed by the embodiment corresponds to the device disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the device part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (6)
1. An omnidirectional filtering method for lunar satellite gravity anomaly data is characterized by comprising the following steps:
step 1: acquiring lunar satellite gravity anomaly grid data to be processed;
step 2: dividing the lunar satellite gravity anomaly grid data into a plurality of sub-areas;
and 3, step 3: creating a filtering window, carrying out unidirectional denoising on data in the range of the filtering window, sliding the filtering window until all data in the sub-area are traversed, and obtaining a unidirectional denoising result of each sub-area;
the unidirectional denoising is performed on the data in the filtering window range, and the unidirectional denoising comprises the following steps:
establishing a filtering window; the filtering window comprises a longitudinal one-dimensional window and a square two-dimensional window, and the longitudinal one-dimensional window is positioned in the center of the square two-dimensional window;
respectively carrying out polynomial fitting on the data in the longitudinal one-dimensional window and the data in the square two-dimensional window to obtain data fitting results;
determining an error value according to the data fitting result;
obtaining unidirectional denoising data in a corresponding filtering window according to the error value;
and 4, step 4: performing coordinate rotation on the data of each sub-area according to a preset angle, rescreening the gridding, and returning to the step 3 until the denoising in all preset angle directions is completed, so as to obtain the data of each sub-area subjected to omnidirectional denoising;
and 5: splicing all sub-area data subjected to omnidirectional denoising to obtain a first denoising result;
step 6: and judging whether the first denoising result meets an error condition, if not, taking the first denoising result as lunar satellite gravity abnormal grid data to be processed, and returning to the step 2 until outputting the omnidirectional denoising data meeting the error condition.
2. The method as claimed in claim 1, wherein the performing polynomial fitting on the data in the longitudinal one-dimensional window and the square two-dimensional window to obtain a data fitting result comprises:
the formula is adopted:
get the numberAccording to the fitting result; wherein, f (x) 1D Fitting the data for a longitudinal one-dimensional window, f (x, y) 2D Is the data fitting result of a square two-dimensional window, x and y are the coordinate positions of the data in the grid respectively, i and j are polynomial times, n is the maximum time, and a i Is a univariate polynomial x i Coefficient of term fit, b i,j Is a binary polynomial x i y j The term fitting coefficient.
3. The method as claimed in claim 2, wherein the determining an error value according to the data fitting result comprises:
the formula is adopted:
determining an error value; wherein, is the error size of (x, y) position, a 0 Fitting constant term coefficients to longitudinal one-dimensional filter window data, b 0,0 Constant term coefficients are fitted to the square two-dimensional filter window data.
4. An omnidirectional filtering system for lunar satellite gravity anomaly data, comprising:
the data acquisition module is used for acquiring lunar satellite gravity anomaly grid data to be processed;
the data subdivision module is used for subdividing the lunar satellite gravity anomaly grid data into a plurality of sub-areas;
the unidirectional denoising module is used for creating a filtering window, performing unidirectional denoising on the data in the range of the filtering window, sliding the filtering window until all the data in the sub-area are traversed, and obtaining a unidirectional denoising result of each sub-area;
wherein, the unidirectional denoising module comprises:
the filtering window construction unit is used for establishing a filtering window; the filtering window comprises a longitudinal one-dimensional window and a square two-dimensional window, and the longitudinal one-dimensional window is positioned in the center of the square two-dimensional window;
the polynomial fitting unit is used for respectively performing polynomial fitting on the data in the longitudinal one-dimensional window and the data in the square two-dimensional window to obtain data fitting results;
an error value determination unit for determining an error value according to the data fitting result;
the denoising unit is used for obtaining unidirectional denoising data in a corresponding filtering window according to the error value;
the omnidirectional denoising module is used for performing coordinate rotation on the data of each sub-area according to a preset angle, rescreening the gridding, returning to the unidirectional denoising module until the denoising in all the preset angle directions is completed, and obtaining the data of each sub-area subjected to omnidirectional denoising;
the data splicing module is used for splicing the data of each sub-area subjected to omnidirectional denoising to obtain a first denoising result;
and the inspection iteration module is used for judging whether the first denoising result meets an error condition or not, taking the first denoising result as lunar satellite gravity abnormal grid data to be processed if the first denoising result does not meet the error condition, and returning to the data subdivision module until outputting omnidirectional denoising data meeting the error condition.
5. The system of claim 4, wherein the polynomial fitting unit comprises:
a data fitting subunit for employing the formula:
obtaining a data fitting result; wherein, f (x) 1D Fitting the data for a longitudinal one-dimensional window, f (x, y) 2D Is the data fitting result of a square two-dimensional window, x and y are respectively the coordinate positions of the data in the grid, i and j are polynomial times, n is the maximum time, and a i Is a univariate polynomial x i Coefficient of term fit, b i,j Is a binary polynomial x i y j The term fitting coefficient.
6. The system of claim 5, wherein the error value determining unit comprises:
an error value determining subunit configured to use the formula:
determining an error value; wherein, is the error magnitude of (x, y) position, a 0 Fitting constant term coefficients to longitudinal one-dimensional filter window data, b 0,0 Constant term coefficients are fitted to the square two-dimensional filter window data.
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| Application Number | Priority Date | Filing Date | Title |
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