A geoid height adaptive fusion system and method based on multi-source satellite altimeter data

Abstract

The present application provides a geoid height adaptive fusion system and method based on multi-source satellite altimeter data, wherein the system pre-processes multi-source altimeter data and unifies the data reference benchmark; removes the height value of the average dynamic terrain model to obtain the line geoid height data, performs intersection adjustment, and calculates the line geoid height precision of each type of satellite altimeter; calculates the initial value of the multi-source fusion weighting factor; grids the geoid height to calculate the assessment accuracy of the ocean geoid height of each type of satellite altimeter; dynamically adjusts the multi-source fusion weighting factor for iterative update; calculates the fusion weights required for the fusion of multi-source geoid heights, and generates high-resolution geoid heights through gridding. The advantages of the present application are: fully tapping the value of satellite altimeter data in different observation modes and different observation tasks, and flexibly and efficiently using multi-source altimeter data to improve the spatial resolution of the ocean geoid height.

Description

Technical Field

[0001] The present application belongs to the field of ocean remote sensing mapping, and specifically relates to a geoid height adaptive fusion system and method based on multi-source satellite altimeter data. Background Art

[0002] The geoid is a gravity equipotential surface in the earth's gravity field that represents the shape of the earth and is most closely aligned with the mean sea level. It is defined as the shape of the sea surface without any disturbance (tide, wind, wave, current, atmospheric load, etc.). At the same time, it extends from the free and static sea to the land to eventually form a closed irregular sphere. It is an ideal reference surface for geodesy to describe the topography of the earth's surface, including the ocean, that is, the starting surface for elevation. The geoid is an ideal surface and is regarded as an equipotential surface of the actual gravity field. Therefore, the distance between the geoid and the reference ellipsoid, that is, the geoid height, has a strict mathematical relationship with the gravity anomaly.

[0003] Radar altimeter is an important active microwave remote sensor, carried on ocean remote sensing satellites, which acquires ocean observation data such as sea surface height, significant wave height, wind speed, etc. by transmitting radar pulse signals to the sea surface and receiving echo signals. Gravity-related parameters such as ocean geoid and geoid height can be extracted from satellite altimeter SSH data, and the ocean gravity field can be further inverted. Satellite altimetry technology is widely used in the construction of geoid height models because it can directly provide rich sea surface height information in a large range of sea areas around the world, and has become a key technology for obtaining high-precision and high-resolution geoid height information.

[0004] Most of the existing satellite altimetry data are obtained by sub-satellite altimeter measurements, with a small measurement swath. It is difficult to provide high-resolution sea surface height information by relying solely on a single satellite altimeter. Multi-source satellite altimeter data fusion is an effective means to build a high-resolution geoid height model. Different types of satellite altimeter data have their own advantages in geoid height calculation. The coverage density of the geodetic mission (GM) data space is high, the precision of the accurate repetitive mission (ERM) data is higher and can effectively supplement the track gap, the synthetic aperture altimeter data is more accurate, and the low-resolution mode (LRM) data has better track diversity. However, the weights of the current multi-source satellite altimeter data fusion method are set based on experience, and the multi-source altimeter data fusion weights cannot be dynamically adjusted according to the evaluation accuracy, making it difficult to give full play to the value of satellite altimeter data in different observation modes and different observation missions. Summary of the invention

[0005] The purpose of this application is to overcome the defects of the prior art that it is not possible to adaptively fuse multi-source altimetry data and it is difficult to fully utilize the value of satellite altimetry data for different observation tasks and different observation modes.

[0006] In order to achieve the above object, the present application proposes a geoid height adaptive fusion system based on multi-source satellite altimeter data, characterized in that the system comprises:

[0007] Multi-source altimetry data classification preprocessing module: used to classify multi-source altimetry data according to satellite observation modes, perform preprocessing and data editing to eliminate abnormal values ​​of altimetry data, remove sea surface time-varying noise from sea surface height data according to satellite observation task classification, unify the reference benchmark of multi-source satellite altimeter data, eliminate the systematic error between multiple satellites, and obtain the sea surface height data of the survey line;

[0008] Geoid height calculation module: used to remove the height value of the average dynamic terrain model from the sea level height data of the survey line to generate the geoid height data of the survey line, perform intersection adjustment processing on the geoid height data of the survey line, obtain the geoid height data of the survey line after adjustment, and calculate the root mean square of the inconsistent values ​​from the intersection points as the geoid height accuracy of the survey line of each type of satellite altimeter;

[0009] Multi-source fusion factor adaptive iteration module: used to calculate the multi-source fusion weighting factor of each type of satellite altimeter data involved in the fusion according to the high precision of the geoid of the survey line of each type of satellite altimeter; combined with the high precision of the geoid of the survey line of each type of satellite altimeter and the evaluation accuracy obtained by the high-precision geoid evaluation module, dynamically adjust the multi-source fusion weighting factor of each type of satellite altimeter data for iterative update, for multi-source geoid high adaptive fusion;

[0010] Multi-source geoid height fusion module: used to calculate the multi-source height fusion weights required for multi-source geoid height fusion by using the distance to the grid point to be calculated and the multi-source fusion weighting factor obtained by the multi-source fusion factor adaptive iteration module, and to perform geoid height gridding processing on the geoid height data of the survey line of the multi-source satellite altimeter to obtain high-resolution geoid height grid data; and

[0011] High-precision geoid assessment module: It is used to use the ultra-high-order global geoid height model as verification data, interpolate the high-resolution geoid height grid data to the model position of the verification area to obtain the corresponding geoid height, use the geoid height value at the corresponding position to calculate the geoid height residual value, and calculate the assessment accuracy of the ocean geoid height model based on the residual value.

[0012] As an improvement of the above system, the processing process of the multi-source altimetry data classification preprocessing module includes:

[0013] Step A1: Classify the multi-source altimetry data according to the satellite observation mode, perform waveform re-tracking, error correction and data editing respectively to eliminate abnormal values ​​of the altimetry data;

[0014] Step A2: Classify the multi-source altimetry data according to the satellite observation mission, remove the sea surface time-varying noise from the sea surface height data, use collinear adjustment to weaken the ERM mission data, and use low-pass filtering to filter out the time-varying noise from the GM mission data;

[0015] Step A3: Select a series of high-precision satellite altimeters as reference datums, and uniformly convert the reference ellipsoids and reference frames of the multi-source satellite altimeters to the high-precision reference datums;

[0016] The calculation formula for uniformly converting the reference ellipsoid to a high-precision reference datum is:

[0017]

[0018] Among them, dh is the sea surface height difference obtained by unifying different satellite reference ellipsoids; is the latitude of the sub-satellite point of the satellite altimeter; a is the major semi-axis of the reference ellipsoid; is the first eccentricity of the ellipsoid, b is the minor semi-axis of the reference ellipsoid; is the flattening of the reference ellipsoid; da and df are the major semi-axis and flattening differences of the reference ellipsoid corresponding to different satellites, respectively;

[0019] The calculation formula for uniformly converting the reference frame to a high-precision reference datum is:

[0020]

[0021] Where, for the sub-satellite point, H is the altitude value of the satellite altimeter; H REF is the reference height value in the reference frame of the position; λ and are the longitude and latitude of the subsatellite point of the satellite altimeter; Δx, Δy and Δz are the offset parameters, and B is the overall offset factor;

[0022] Using the least squares method, the four parameters Δx, Δy, Δz and B in each satellite reference frame are calculated separately and then converted to a high-precision reference datum.

[0023] As an improvement of the above system, the processing process of the geoid height calculation module includes:

[0024] Step B1: removing the height value of the ocean average dynamic terrain model at the corresponding position from the sea surface height of the survey line of the multi-source satellite altimeter to generate the geoid height data of the survey line;

[0025] The geoid height is the difference between the sea level and the average dynamic topography of the ocean, that is:

[0026] N=SSH-MDT

[0027] Where N is the height of the geoid of the survey line; SSH is the sea level height obtained by satellite altimetry; MDT is the height value of the mean dynamic terrain model;

[0028] Step B2: Calculate the longitude and latitude of the intersection, the observation time and the height value of the geoid through the longitude and latitude of the satellite altimetry data, the observation time and the height value, and the arc segment number and the lifting and lowering identification information of the survey line, and calculate the geoid height data of the survey line after adjustment based on the least squares criterion; the geoid height data of the survey line includes: time, longitude, latitude and geoid height;

[0029] Step B3: Count the number of self-crossing points of each type of satellite altimeter respectively, and calculate the root mean square of the self-crossing point discrepancies of the survey line geoid height of each type of satellite altimeter as the survey line geoid high precision of the satellite altimeter.

[0030]

[0031] Among them, m s j Indicates the high precision of the geoid of the survey line of the j-th type of satellite altimeter data; ΔN h j It represents the geoid height discrepancy value of the survey line of the h-th self-intersection point of the j-th type satellite altimeter; n represents the number of self-intersection points of the j-th type satellite altimeter.

[0032] As an improvement of the above system, the processing process of the multi-source fusion factor adaptive iteration module includes:

[0033] Step C1: Calculate the initial value of the multi-source fusion weighting factor of each type of satellite altimeter data involved in the fusion according to the high precision of the geoid of the survey line of each type of satellite altimeter;

[0034] The initial value of the multi-source fusion weighting factor is:

[0035]

[0036] or

[0037]

[0038] Among them, W s j is the multi-source fusion weighting factor of the j-th type of satellite altimeter data; m s j represents the high precision of the geoid of the survey line of the j-th type of satellite altimeter data; l represents the number of satellite altimeter data involved in the fusion, and different tasks of the same model of satellite altimeter are divided into different categories;

[0039] Step C2: combining the high-precision geoid of each type of satellite altimeter and the evaluation accuracy of each type of satellite altimeter data obtained by the high-precision geoid evaluation module, dynamically adjusting the fusion weighting factor for iterative calculation for multi-source geoid height adaptive fusion;

[0040] The multi-source fusion weighting factor is iteratively updated as:

[0041]

[0042] or

[0043]

[0044] Among them, m g j It represents the evaluation accuracy of geoid height calculated by the j-th type of satellite altimeter data.

[0045] As an improvement of the above system, the processing process of the multi-source geoid height fusion module includes:

[0046] Step D1: multi-source height fusion weight calculation: used to calculate the multi-source height fusion weight required for multi-source geoid height fusion using the distance to the grid point to be calculated and the multi-source fusion weight factor of the satellite altimeter;

[0047] If there are multiple altimetry data within a certain sea area near the grid point, the weight P of the i-th observation point of the multi-source satellite altimetry data is i for:

[0048]

[0049] Among them, d i is the distance from the ith observation point to the grid point to be determined; W s j is the multi-source fusion weighting factor of the satellite altimeter corresponding to the i-th observation point;

[0050] Step D2: Gridding the geoid height data of the survey lines of the multi-source satellite altimeter to obtain high-resolution geoid height grid data; the geoid height grid data includes longitude, latitude and geoid height.

[0051] As an improvement of the above system, the processing process of the ocean gravity field verification and evaluation module includes:

[0052] Step E1: Interpolate the high-resolution geoid height grid data to the model location of the verification area to obtain the corresponding geoid height Geoid k , and then according to the model geoid height value at the corresponding location Calculate the residual value RES of the geoid heightk ;

[0053]

[0054] Step E2: Count the residual values ​​of the ocean geoid height and calculate the accuracy of the residual values ​​of the ocean geoid height;

[0055] As an improvement to the above system, the accuracy of the residual value of the ocean geoid height is the root mean square:

[0056]

[0057] Among them, RMS j is the root mean square of the geoid height inverted from the j-th type satellite altimeter data; N is the number of measured points for evaluating the j-th type satellite altimeter.

[0058] The present application also provides a geoid height adaptive fusion method based on multi-source satellite altimeter data, which is implemented based on the above system and includes:

[0059] Step 1) The multi-source altimetry data classification preprocessing module classifies the multi-source altimetry data according to the satellite observation mode, performs preprocessing and data editing respectively to eliminate the abnormal values ​​of the altimetry data, removes the sea surface time-varying noise from the sea surface height data according to the satellite observation task classification, and unifies the reference benchmark of the multi-source satellite altimeter data to eliminate the systematic error between multiple satellites, and obtains the sea surface height data of the survey line;

[0060] Step 2) geoid height calculation module removes the height value of the ocean average dynamic terrain model from the survey line sea level high data, generates the survey line geoid height data, carries out intersection adjustment processing to the survey line geoid height data, obtains the survey line geoid height data after adjustment, and calculates the root mean square of the inconsistent value from the intersection as the survey line geoid high precision of every type of satellite altimeter respectively;

[0061] Step 3) calculating the initial value of the multi-source fusion weighting factor of each type of satellite altimeter data involved in the fusion according to the high precision of the geoid of the survey line of each type of satellite altimeter;

[0062] Step 4) using the distance to the grid point to be calculated and the multi-source fusion weighting factor obtained by the multi-source fusion factor adaptive iteration module, the multi-source height fusion weight required for the multi-source geoid height fusion is calculated, and the geoid height data of the survey line of the single-satellite altimeter are respectively processed into a grid of geoid heights to obtain the geoid height grid data of the single-satellite altimeter;

[0063] Step 5) The geoid high-precision assessment module evaluates the accuracy of the geoid high grid data of the single satellite altimeter respectively; combining the high-precision geoid of the survey line of each type of satellite altimeter and the assessment accuracy obtained by the geoid high-precision assessment module, dynamically adjusts the multi-source fusion weighting factor of each type of satellite altimeter data for iterative update, for adaptive fusion of multi-source altimeter data;

[0064] Step 6) Utilize the distance to the grid point to be determined and the multi-source fusion weighting factor obtained by the multi-source fusion factor adaptive iteration module to calculate the multi-source height fusion weight required for the multi-source geoid height fusion, perform geoid height gridding on the geoid height data of the survey line of the multi-source satellite altimeter, and obtain a high-resolution geoid height model.

[0065] Compared with the prior art, the advantages of this application are:

[0066] 1. This application has for the first time established a high-resolution geoid height calculation system that adaptively integrates multi-source satellite altimetry data from different observation modes and different observation tasks, and can construct a higher-resolution ocean geoid height model.

[0067] 2. The high-resolution geoid height calculation method of adaptively fusing multi-source satellite altimetry data proposed in this application can fully tap the value of satellite altimetry data in different observation modes and different observation tasks, and fully, flexibly and efficiently utilize multi-source altimetry data to improve the spatial resolution of ocean geoid height. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 The figure shows the structure of the geoid height adaptive fusion system based on multi-source satellite altimeter data;

[0069] Figure 2 Shown is a flow chart of the geoid height adaptive fusion method based on multi-source satellite altimeter data. DETAILED DESCRIPTION

[0070] The technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0071] The adaptive fusion system and method of geoid height based on multi-source satellite altimeter data proposed in this application are not only used for radar altimeters, but also suitable for laser altimeters. By adaptively fusing satellite altimetry data of different observation modes (LRM, synthetic aperture mode, wide swath interferometry, dual-satellite follow-up altimetry, and GNSS-R altimetry, etc.) and different observation tasks (ERM, GM), the multi-source altimetry data can be fully, flexibly and efficiently utilized to improve the spatial resolution of the ocean geoid height.

[0072] Example 1

[0073] like Figure 1 As shown, the present application proposes a geoid height adaptive fusion system based on multi-source satellite altimeter data, including a multi-source altimetry data classification preprocessing module, a geoid height calculation module, a multi-source fusion factor adaptive iteration module, a multi-source geoid height fusion module and a geoid height verification and evaluation module.

[0074] The design of each module is as follows:

[0075] 1. Multi-source altimetry data classification preprocessing module: used to classify multi-source altimetry data according to satellite observation mode, perform preprocessing and data editing to eliminate abnormal values ​​of altimetry data, remove sea surface time-varying noise from sea surface height data according to satellite observation task classification, unify the reference benchmark of multi-source satellite altimeter data, eliminate the systematic error between multiple satellites, and obtain high-precision sea surface height data (time, longitude, latitude, altitude) of the survey line;

[0076] The processing process of the multi-source altimetry data classification preprocessing module includes:

[0077] Step 1: Classify and process by observation mode: classify multi-source altimetry data by satellite observation mode, perform waveform re-tracking, error correction and data editing respectively to eliminate abnormal values ​​of altimetry data;

[0078] Step 2: Classify and process according to observation tasks; classify multi-source altimetry data according to satellite observation tasks, remove sea surface time-varying noise from sea surface height data, use collinear adjustment to weaken ERM mission data, and use low-pass filtering to filter out time-varying noise from GM mission data;

[0079] Step 3: Unify the reference datum; select a series of high-precision satellite altimeters (such as Jason-3) as the reference datum, and convert the reference ellipsoid and reference frame of the multi-source satellite altimeters to the high-precision reference datum.

[0080] The unified calculation formula of the reference ellipsoid is:

[0081]

[0082] Where dh is the sea surface height difference obtained by unifying different satellite reference ellipsoids, a is the major semi-axis of the reference ellipsoid, and b is the minor semi-axis of the reference ellipsoid. is the first eccentricity of the ellipsoid, is the flattening of the reference ellipsoid, da and df are the major semi-axis and flattening differences of the reference ellipsoids corresponding to different satellites, respectively. is the latitude of the sub-satellite point of the satellite altimeter.

[0083] The unified computational model of the reference frame is:

[0084]

[0085] For the sub-satellite point, H is the altitude value of the satellite altimeter, H REF is the reference height value in the reference frame of the position, λ and is the longitude and latitude of the subsatellite point of the satellite altimeter, Δx, Δy and Δz are the offset parameters, and B is the overall offset factor. Using the least squares method, the four parameters Δx, Δy, Δz and B in each satellite reference frame model are calculated separately and then converted to a high-precision reference benchmark.

[0086] 2. Geoid height calculation module: used to remove the average dynamic terrain model from the sea level height of the survey line of the multi-source satellite altimeter to generate the survey line geoid height data, perform intersection adjustment on the survey line geoid height data of the multi-source satellite altimeter, obtain the adjusted survey line geoid height data (time, longitude, latitude, altitude), and calculate the root mean square of the intersection point discrepancy value as the survey line geoid high precision of each type of satellite altimeter;

[0087] The processing of the geoid height calculation module includes:

[0088] Step 1: Calculate the geoid height of the survey line: Used to remove the ocean average dynamic terrain model at the corresponding position from the survey line sea surface high of the multi-source satellite altimeter to generate the survey line geoid height data;

[0089] The geoid height is the difference between the sea level and the average dynamic topography of the ocean,

[0090] N=SSH-MDT

[0091] Where N is the height of the geoid of the survey line; SSH is the sea level height obtained by satellite altimetry; MDT is the height value of the mean dynamic terrain model.

[0092] Step 2: Intersection adjustment: The longitude and latitude, observation time, height value of the intersection point, and the arc segment number and lift identification information of the satellite altimetry data are used to accurately calculate the longitude and latitude, observation time, and geoid height value of the intersection point. The geoid height data (time, longitude, latitude, and geoid height) of the survey line after adjustment are obtained based on the least squares criterion adjustment solution.

[0093] Step 3: Calculate the high precision of the geoid; count the number of self-crossing points of each type of satellite altimeter respectively, and calculate the root mean square of the discrepancy between the self-crossing points of the geoid height of the survey line of each type of satellite altimeter as the high precision of the geoid of the survey line of the satellite altimeter.

[0094]

[0095] Among them, m sj Indicates the high precision of the geoid of the survey line of the j-th type of satellite altimeter data; ΔN h j It represents the geoid height discrepancy value of the survey line of the h-th self-intersection point of the j-th type satellite altimeter; n represents the number of self-intersection points of the j-th type satellite altimeter.

[0096] 3. Multi-source fusion factor adaptive iteration module: used to calculate the multi-source fusion weighting factor of each type of satellite altimeter data involved in the fusion according to the high precision of the geoid of the survey line of each type of satellite altimeter; combined with the high precision of the geoid of the survey line of each type of satellite altimeter and the evaluation accuracy obtained by the high-precision geoid evaluation module, dynamically adjust the multi-source fusion weighting factor of each type of satellite altimeter data for iterative update, for multi-source geoid high adaptive fusion;

[0097] The processing process of the multi-source fusion factor adaptive iteration module includes:

[0098] Step 1: Calculate the initial value of the multi-source fusion weighting factor; Calculate the initial value of the multi-source fusion weighting factor of each type of satellite altimeter data involved in the fusion according to the high precision of the geoid of the survey line of each type of satellite altimeter;

[0099] The initial value of the multi-source fusion weighting factor is:

[0100]

[0101] or

[0102]

[0103] Among them, W s j is the multi-source fusion weighting factor of the j-th type of satellite altimeter data; m s j represents the high precision of the geoid of the survey line of the jth type of satellite altimeter data; l represents the number of satellite altimeter data involved in the fusion (different tasks of the same model of satellite altimeter are divided into different categories, such as ERS-1 / GM and ERS-1 / ERM are two types of altimeter data involved in the fusion).

[0104] Step 2: Adaptively update the multi-source fusion weighting factor; combine the high-precision geoid of each type of satellite altimeter and the evaluation accuracy of each type of satellite altimeter data obtained by the high-precision geoid evaluation module, dynamically adjust the fusion weighting factor for iterative calculation, and use it for multi-source geoid high-precision adaptive fusion;

[0105] The multi-source fusion weighting factor is iteratively updated as:

[0106]

[0107] or

[0108]

[0109] Among them, W s j is the multi-source fusion weighting factor of the j-th type of satellite altimeter data; m s j represents the high precision of the geoid of the jth satellite altimeter; m g j represents the evaluation accuracy of geoid height calculated by the j-th type of satellite altimeter data; l represents the number of satellite altimeter data involved in the fusion (different tasks of the same model of satellite altimeter are divided into different categories, such as ERS-1 / GM and ERS-1 / ERM are two types of altimeter data involved in the fusion).

[0110] 4. Multi-source geoid height fusion module: It is used to calculate the multi-source height fusion weights required for multi-source geoid height fusion by using the accuracy of geoid height, the distance to the grid point to be calculated, and the multi-source fusion weighting factor obtained by the multi-source fusion factor adaptive iteration module, and to grid the geoid height data of the survey line of the multi-source satellite altimeter to obtain high-resolution geoid height grid data (longitude, latitude, geoid height);

[0111] The processing of the multi-source geoid height fusion module includes:

[0112] Step 1: Calculation of multi-source height fusion weights: Use the distance to the grid point to be calculated and the multi-source fusion weighting factor of the satellite altimeter to calculate the multi-source height fusion weights required for multi-source geoid height fusion;

[0113] If there are multiple altimetry data within a certain sea area near the grid point, the weight P of the i-th observation point of the multi-source satellite altimetry data is i for

[0114]

[0115] Where, d i is the distance from the ith observation point to the grid point to be determined; W s j is the multi-source fusion weighting factor of the satellite altimeter corresponding to the i-th observation point.

[0116] Step 2: Gridding of geoid height: Gridding the geoid height data of the survey lines of the multi-source satellite altimeter to obtain high-resolution geoid height grid data (longitude, latitude, geoid height).

[0117] 5. High-precision geoid assessment module: It is used to use the internationally authoritative ultra-high-order global geoid height model as verification data, interpolate the high-resolution geoid height grid data to the model position of the verification area to obtain the corresponding geoid height, use the model geoid height value at the corresponding position to calculate the geoid height residual value, and calculate the assessment accuracy of the ocean geoid height model based on the residual.

[0118] The processing of the ocean gravity field verification and evaluation module includes:

[0119] Step 1: Calculate the geoid height residual; interpolate the high-resolution geoid height grid data to the model location of the verification area to obtain the corresponding geoid height Geoid k , and then use the model geoid height value at the corresponding location Calculate the residual value of the geoid height;

[0120]

[0121] Step 2: Geoid high-precision assessment; statistics are performed on the residual values ​​of the ocean geoid height to calculate the accuracy of the residual values ​​of the ocean geoid height; there are many ways to calculate the accuracy, such as root mean square.

[0122]

[0123] Among them, RMS j Calculate the root mean square of the geoid height for the j-th type of satellite altimeter data; N represents the number of verification points for evaluating the j-th type of satellite altimeter.

[0124] Example 2

[0125] like Figure 2 As shown, the present application also proposes a geoid height adaptive fusion method based on multi-source satellite altimeter data, which is implemented based on the above system and includes:

[0126] Step 1) The multi-source altimetry data classification preprocessing module classifies the multi-source altimetry data according to the satellite observation mode, performs preprocessing and data editing respectively to eliminate the abnormal values ​​of the altimetry data, removes the sea surface time-varying noise from the sea surface height data according to the satellite observation task classification, and unifies the reference benchmark of the multi-source satellite altimeter data to eliminate the systematic error between multiple satellites, and obtains high-precision sea surface height data (time, longitude, latitude, altitude) of the survey line;

[0127] Step 2) the geoid height calculation module removes the ocean average dynamic terrain model from the survey line sea level high of the multi-source satellite altimeter, generates the survey line geoid height data, performs intersection adjustment processing on the survey line geoid height data of the multi-source satellite altimeter, obtains the survey line geoid height data (time, longitude, latitude, altitude) after adjustment, and calculates the root mean square of the intersection point discrepancy value as the survey line geoid high precision of each type of satellite altimeter;

[0128] Step 3) calculating the initial value of the multi-source fusion weighting factor of each type of satellite altimeter data involved in the fusion according to the high precision of the geoid of the survey line of the multi-source satellite altimeter;

[0129] Step 4) Calculate the multi-source height fusion weights required for multi-source geoid height fusion by using the distance to the grid point to be determined and the multi-source fusion weighting factor obtained by the multi-source fusion factor adaptive iteration module, and perform geoid height gridding processing on the geoid height data of the survey line of the single-satellite altimeter to obtain the geoid height grid data (longitude, latitude, geoid height) of the single-satellite altimeter;

[0130] Step 5) The geoid high-precision assessment module evaluates the accuracy of the geoid high grid data of the single satellite altimeter respectively; combining the high-precision geoid of the survey line of each type of satellite altimeter and the assessment accuracy obtained by the geoid high-precision assessment module, dynamically adjusts the multi-source fusion weighting factor of each type of satellite altimeter data for iterative update, for adaptive fusion of multi-source altimeter data;

[0131] Step 6) Utilize the distance to the grid point to be determined and the multi-source fusion weighting factor obtained by the multi-source fusion factor adaptive iteration module to calculate the multi-source height fusion weight required for the multi-source geoid height fusion, perform geoid height gridding on the geoid height data of the survey line of the multi-source satellite altimeter, and obtain a high-resolution geoid height model (longitude, latitude, geoid height).

[0132] The present application may also provide a computer device, comprising: at least one processor, a memory, at least one network interface and a user interface. The various components in the device are coupled together through a bus system. It is understood that the bus system is used to achieve connection and communication between these components. In addition to the data bus, the bus system also includes a power bus, a control bus and a status signal bus.

[0133] The user interface may include a display, a keyboard or a pointing device, such as a mouse, a trackball, a touch pad or a touch screen.

[0134] It is understood that the memory in the embodiments disclosed in the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus random access memory (DRRAM). The memories described herein are intended to include, but are not limited to, these and any other suitable types of memories.

[0135] In some embodiments, the memory stores the following elements, executable modules or data structures, or a subset thereof, or an extended set thereof: an operating system and applications.

[0136] The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application includes various application programs, such as a media player (Media Player), a browser (Browser), etc., which are used to implement various application services. The program for implementing the method of the embodiment of the present disclosure can be included in the application.

[0137] In the above embodiment, the processor may also call a program or instruction stored in the memory, specifically, a program or instruction stored in an application program, and is used to:

[0138] Execute the steps of the above method.

[0139] The above method can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The above-disclosed methods, steps and logic block diagrams can be implemented or executed. The general processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the above-disclosed method can be directly embodied as a hardware decoding processor to execute, or the hardware and software modules in the decoding processor are combined to execute. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0140] It is understood that the embodiments described in the present application can be implemented by hardware, software, firmware, middleware, microcode or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in the present application or a combination thereof.

[0141] For software implementation, the technology of the present application can be implemented by executing the functional modules (such as procedures, functions, etc.) of the present application. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

[0142] The present application may also provide a non-volatile storage medium for storing a computer program. When the computer program is executed by a processor, each step in the above method embodiment can be implemented.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application and are not intended to limit it. Although the present application is described in detail with reference to the embodiments, a person skilled in the art should understand that any modification or equivalent replacement of the technical solution of the present application does not depart from the spirit and scope of the technical solution of the present application and should be included in the scope of the claims of the present application.

Claims

1. A geoid height adaptive fusion system based on multi-source satellite altimeter data, characterized in that: The system comprises: Multi-source altimetry data classification preprocessing module: used to classify multi-source altimetry data according to satellite observation modes, perform preprocessing and data editing to eliminate abnormal values ​​of altimetry data, remove sea surface time-varying noise from sea surface height data according to satellite observation task classification, unify the reference benchmark of multi-source satellite altimeter data, eliminate the systematic error between multiple satellites, and obtain the sea surface height data of the survey line; Geoid height calculation module: used to remove the height value of the average dynamic terrain model from the sea level height data of the survey line to generate the geoid height data of the survey line, perform intersection adjustment processing on the geoid height data of the survey line, obtain the geoid height data of the survey line after adjustment, and calculate the root mean square of the inconsistent values ​​from the intersection points as the geoid height accuracy of the survey line of each type of satellite altimeter; Multi-source fusion factor adaptive iteration module: used to calculate the multi-source fusion weighting factor of each type of satellite altimeter data involved in the fusion according to the high precision of the geoid of the survey line of each type of satellite altimeter; combined with the high precision of the geoid of the survey line of each type of satellite altimeter and the evaluation accuracy obtained by the high-precision geoid evaluation module, dynamically adjust the multi-source fusion weighting factor of each type of satellite altimeter data for iterative update, for multi-source geoid high adaptive fusion; Multi-source geoid height fusion module: used to calculate the multi-source height fusion weights required for multi-source geoid height fusion by using the distance to the grid point to be calculated and the multi-source fusion weighting factor obtained by the multi-source fusion factor adaptive iteration module, and to perform geoid height gridding processing on the geoid height data of the survey line of the multi-source satellite altimeter to obtain high-resolution geoid height grid data; and High-precision geoid assessment module: It is used to use the ultra-high-order global geoid height model as verification data, interpolate the high-resolution geoid height grid data to the model position of the verification area to obtain the corresponding geoid height, use the geoid height value at the corresponding position to calculate the geoid height residual value, and calculate the assessment accuracy of the ocean geoid height model based on the residual value.

2. The geoid height adaptive fusion system based on multi-source satellite altimeter data according to claim 1, characterized in that: The processing process of the multi-source altimetry data classification preprocessing module includes: Step A1: Classify the multi-source altimetry data according to the satellite observation mode, perform waveform re-tracking, error correction and data editing respectively to eliminate abnormal values ​​of the altimetry data; Step A2: Classify the multi-source altimetry data according to the satellite observation mission, remove the sea surface time-varying noise from the sea surface height data, use collinear adjustment to weaken the ERM mission data, and use low-pass filtering to filter out the time-varying noise from the GM mission data; Step A3: Select a series of high-precision satellite altimeters as reference datums, and uniformly convert the reference ellipsoids and reference frames of the multi-source satellite altimeters to the high-precision reference datums; The calculation formula for uniformly converting the reference ellipsoid to a high-precision reference datum is: Among them, dh is the sea surface height difference obtained by unifying different satellite reference ellipsoids; is the latitude of the sub-satellite point of the satellite altimeter; a is the major semi-axis of the reference ellipsoid; is the first eccentricity of the ellipsoid, b is the minor semi-axis of the reference ellipsoid; is the flattening of the reference ellipsoid; da and df are the major semi-axis and flattening differences of the reference ellipsoid corresponding to different satellites, respectively; The calculation formula for uniformly converting the reference frame to a high-precision reference datum is: Where, for the sub-satellite point, H is the altitude value of the satellite altimeter; H REF is the reference height value in the reference frame of the position; λ and are the longitude and latitude of the subsatellite point of the satellite altimeter; Δx, Δy and Δz are the offset parameters, and B is the overall offset factor; Using the least squares method, the four parameters Δx, Δy, Δz and B in each satellite reference frame are calculated separately and then converted to a high-precision reference datum.

3. The geoid height adaptive fusion system based on multi-source satellite altimeter data according to claim 1, characterized in that: The processing process of the geoid height calculation module includes: Step B1: removing the height value of the ocean average dynamic terrain model at the corresponding position from the sea surface height of the survey line of the multi-source satellite altimeter to generate the geoid height data of the survey line; The geoid height is the difference between the sea level and the average dynamic topography of the ocean, that is: N=SSH-MDT Where N is the height of the geoid of the survey line; SSH is the sea level height obtained by satellite altimetry; MDT is the height value of the mean dynamic terrain model; Step B2: Calculate the longitude and latitude of the intersection, the observation time and the height value of the geoid through the longitude and latitude of the satellite altimetry data, the observation time and the height value, and the arc segment number and the lifting and lowering identification information of the survey line, and calculate the geoid height data of the survey line after adjustment based on the least squares criterion; the geoid height data of the survey line includes: time, longitude, latitude and geoid height; Step B3: Count the number of self-intersection points of each type of satellite altimeter respectively, and calculate the root mean square of the self-intersection point discrepancy value of the survey line geoid height of each type of satellite altimeter as the survey line geoid high precision of the satellite altimeter; Among them, m s j Indicates the high precision of the geoid of the survey line of the j-th type of satellite altimeter data; ΔN h j It represents the geoid height discrepancy value of the survey line of the h-th self-intersection point of the j-th type satellite altimeter; n represents the number of self-intersection points of the j-th type satellite altimeter.

4. The geoid height adaptive fusion system based on multi-source satellite altimeter data according to claim 1, characterized in that: The processing process of the multi-source fusion factor adaptive iteration module includes: Step C1: Calculate the initial value of the multi-source fusion weighting factor of each type of satellite altimeter data involved in the fusion according to the high precision of the geoid of the survey line of each type of satellite altimeter; The initial value of the multi-source fusion weighting factor is: or in, is the multi-source fusion weighting factor of the j-th type of satellite altimeter data; m s j represents the high precision of the geoid of the survey line of the j-th type of satellite altimeter data; l represents the number of satellite altimeter data involved in the fusion, and different tasks of the same model of satellite altimeter are divided into different categories; Step C2: combining the high-precision geoid of each type of satellite altimeter and the evaluation accuracy of each type of satellite altimeter data obtained by the high-precision geoid evaluation module, dynamically adjusting the fusion weighting factor for iterative calculation for multi-source geoid height adaptive fusion; The multi-source fusion weighting factor is iteratively updated as: or Among them, m g j It represents the evaluation accuracy of geoid height calculated by the j-th type of satellite altimeter data.

5. The geoid height adaptive fusion system based on multi-source satellite altimeter data according to claim 1, characterized in that: The processing process of the multi-source geoid height fusion module includes: Step D1: multi-source height fusion weight calculation: used to calculate the multi-source height fusion weight required for multi-source geoid height fusion using the distance to the grid point to be calculated and the multi-source fusion weight factor of the satellite altimeter; If there are multiple altimetry data within the set sea area near the grid point, the weight of the i-th observation point of the multi-source satellite altimetry data is: Among them, d i is the distance from the ith observation point to the grid point to be determined; is the multi-source fusion weighting factor of the satellite altimeter corresponding to the i-th observation point; Step D2: Gridding the geoid height data of the survey lines of the multi-source satellite altimeter to obtain high-resolution geoid height grid data; the geoid height grid data includes longitude, latitude and geoid height.

6. The geoid height adaptive fusion system based on multi-source satellite altimeter data according to claim 1, characterized in that: The processing process of the geoid high-precision assessment module includes: Step E1: Interpolate the high-resolution geoid height grid data to the model location of the verification area to obtain the corresponding geoid height k , and then according to the model geoid height value at the corresponding location Calculate the residual value RES of the geoid height k ; Step E2: Count the residual values ​​of the ocean geoid height and calculate the accuracy of the residual values ​​of the ocean geoid height.

7. The geoid height adaptive fusion system based on multi-source satellite altimeter data according to claim 6, characterized in that: The accuracy of the residual value of the ocean geoid height is the root mean square: Among them, RMS j Calculate the root mean square of the geoid height for the j-th type of satellite altimeter data; N represents the number of verification points for evaluating the j-th type of satellite altimeter.

8. A method for adaptively fusing geoid height based on multi-source satellite altimeter data, implemented based on the system of any one of claims 1 to 7, the method comprising: Step 1) The multi-source altimetry data classification preprocessing module classifies the multi-source altimetry data according to the satellite observation mode, performs preprocessing and data editing respectively to eliminate the abnormal values ​​of the altimetry data, removes the sea surface time-varying noise from the sea surface height data according to the satellite observation task classification, and unifies the reference benchmark of the multi-source satellite altimeter data to eliminate the systematic error between multiple satellites, and obtains the sea surface height data of the survey line; Step 2) geoid height calculation module removes the height value of the ocean average dynamic terrain model from the survey line sea level high data, generates the survey line geoid height data, carries out intersection adjustment processing to the survey line geoid height data, obtains the survey line geoid height data after adjustment, and calculates the root mean square of the inconsistent value from the intersection as the survey line geoid high precision of every type of satellite altimeter respectively; Step 3) calculating the initial value of the multi-source fusion weighting factor of each type of satellite altimeter data involved in the fusion according to the high precision of the geoid of the survey line of each type of satellite altimeter; Step 4) using the distance to the grid point to be calculated and the multi-source fusion weighting factor obtained by the multi-source fusion factor adaptive iteration module, the multi-source height fusion weight required for the multi-source geoid height fusion is calculated, and the geoid height data of the survey line of the single-satellite altimeter are respectively processed into a grid of geoid heights to obtain the geoid height grid data of the single-satellite altimeter; Step 5) The geoid high-precision assessment module evaluates the accuracy of the geoid high grid data of the single satellite altimeter respectively; combining the high-precision geoid of the survey line of each type of satellite altimeter and the assessment accuracy obtained by the geoid high-precision assessment module, dynamically adjusts the multi-source fusion weighting factor of each type of satellite altimeter data for iterative update, for adaptive fusion of multi-source altimeter data; Step 6) Utilize the distance to the grid point to be determined and the multi-source fusion weighting factor obtained by the multi-source fusion factor adaptive iteration module to calculate the multi-source height fusion weight required for the multi-source geoid height fusion, perform geoid height gridding on the geoid height data of the survey line of the multi-source satellite altimeter, and obtain a high-resolution geoid height model.

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