A vertical line deviation adaptive fusion system and method based on multi-source satellite altimeter data

Abstract

The present application provides a vertical line deviation adaptive fusion system and method based on multi-source satellite altimeter data, wherein the system classifies and pre-processes the multi-source altimeter data to obtain high-precision sea surface height data; performs differential processing to calculate the sea surface height slope information of the multi-source satellite altimeter; calculates the initial value of the multi-source fusion weighting factor; respectively solves the vertical line deviation component of the sea surface height slope data of the single-satellite altimeter; calculates the evaluation accuracy of the vertical line deviation grid data of each type of satellite altimeter; dynamically adjusts the multi-source fusion weighting factor of each type of satellite altimeter data for iterative update; calculates the multi-source slope fusion weight, solves the vertical line deviation component of the sea surface height slope data of the multi-source satellite altimeter, and obtains high-resolution ocean vertical line deviation grid data. The advantages of the present application are: fully tapping the value of different types of satellite altimeter data, and flexibly and efficiently using multi-source altimeter data to improve the spatial resolution of ocean vertical line deviation.

Description

Technical Field

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

[0002] The vertical deviation is defined as the angle between the gravity line at a point on the ground and the normal to the ellipsoid, usually expressed as a meridian component and a meridian component. According to the definition, the vertical deviation can be expressed as the gradient of the geoid height, and is usually calculated approximately using the slope of the sea surface height. Satellite altimetry technology is widely used in the study of vertical deviation because it can directly provide rich sea surface height information over a large area of ​​the world's oceans, and has become a key technology for obtaining high-precision, high-resolution vertical deviation information.

[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 vertical deviation can be extracted from the sea surface height data of satellite altimeter, and the ocean gravity field can be further inverted. The calculation principle of vertical deviation of satellite altimetry is: according to the time, longitude and latitude and sea surface height information in the satellite altimetry data, the slope value of the sea surface height along the survey line is calculated by using the first difference of the altimetry data, and finally the meridian component and the meridian component of the vertical deviation are solved.

[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 construct high-resolution vertical deviations. Different types of satellite altimeter data have their own advantages in solving vertical deviations. Geodetic mission (GM) data has a high spatial coverage density, and accurate repetitive mission (ERM) data has a higher accuracy and can effectively supplement track gaps. Synthetic aperture altimetry data has higher accuracy, and 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 vertical deviation assessment accuracy, making it difficult to give full play to the value of satellite altimeter data for different observation tasks and different observation modes. 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 vertical deviation 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 remove abnormal values ​​of altimetry data, and remove sea surface time-varying noise from sea surface height data according to satellite observation task classification to obtain high-precision sea surface height data;

[0008] Sea surface height slope calculation module: used to perform differential processing on the sea surface height data of multi-source satellite altimeters along the survey line, and calculate the sea surface height slope information after eliminating the gross errors;

[0009] Multi-source fusion factor adaptive iteration module: used to 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 sea surface precision of the multi-source satellite altimeter; combined with the high sea surface precision of the multi-source satellite altimeter and the evaluation accuracy obtained by the vertical deviation accuracy evaluation module, dynamically adjust the multi-source fusion weighting factor of each type of satellite altimeter data for iterative update, for multi-source vertical deviation adaptive fusion;

[0010] Multi-source vertical deviation fusion module: used to calculate the multi-source slope fusion weights required for multi-source vertical deviation fusion by using the accuracy of sea surface high slope, 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 solve the vertical deviation component of the sea surface high slope data of the multi-source satellite altimeter to obtain high-resolution ocean vertical deviation grid data; and

[0011] Vertical deviation accuracy assessment module: It is used to use the refined measured vertical deviation data as verification data, interpolate the high-resolution ocean vertical deviation grid data at the measured vertical deviation points to obtain the corresponding vertical deviation meridian component and meridian component, use the refined measured vertical deviation component values ​​at the corresponding positions to calculate the vertical deviation residual value, and calculate the assessment accuracy of the ocean vertical deviation model based on the residual.

[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] As an improvement of the above system, the processing process of the sea surface high slope calculation module includes:

[0016] Step B1: Calculate the time difference Δt, longitude difference Δλ, and latitude difference between two adjacent points for the sea surface height data of the multi-source satellite altimeter and the sea surface height difference Δh, and calculate the spherical distance d between two adjacent points pq , point p and point g are two adjacent points:

[0017] Δt=t q -t p

[0018] Δλ=λ q -λ p

[0019]

[0020] Δh=h q -h p

[0021]

[0022] Among them, t p , t q ,λ p ,λ q , h q ,h p Represent the time, longitude, latitude and sea level of two adjacent points respectively; Re is the average radius of the earth;

[0023] Step B2: setting thresholds for the time difference and distance between two adjacent points of the multi-source satellite altimeter respectively. If the thresholds are exceeded, the points are judged as abnormal sea surface height differences caused by the sub-satellite point passing through land or islands and are removed. At the same time, the sea surface height differences that exceed the reasonable range are removed.

[0024] Step B3: Calculate the time t of the sea surface high slope using the height measurement data after eliminating gross errors dov , longitude λ dov ,latitude Slope ε dov 、azimuth az dov and precision σ dov :

[0025]

[0026] in, is the average latitude of two adjacent points, Respectively represent the high accuracy of the sea surface between two adjacent points.

[0027] As an improvement of the above system, the processing process of the multi-source fusion factor adaptive iteration module includes:

[0028] 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 sea surface precision of the multi-source satellite altimeter;

[0029] The multi-source fusion weighting factor is initially set to:

[0030]

[0031] or

[0032]

[0033] 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 sea surface high precision of the jth type of satellite altimeter; l represents the number of satellite altimeter data involved in the fusion, and different tasks of the same type of satellite altimeter are divided into different categories;

[0034] Step C2: combining the evaluation accuracy obtained by the sea surface high precision and vertical deviation accuracy evaluation module of the multi-source satellite altimeter, dynamically adjusting the multi-source fusion factor of each type of satellite altimeter data for iterative update, for adaptive fusion of multi-source satellite altimeter data;

[0035] The multi-source fusion weighting factor is iteratively updated as:

[0036]

[0037] or

[0038]

[0039] Among them, m d j It represents the evaluation accuracy of the vertical deviation calculated by the j-th type of satellite altimeter data.

[0040] As an improvement of the above system, the processing process of the multi-source vertical line deviation fusion module includes:

[0041] Step D1: Calculate the multi-source slope fusion weight required for multi-source vertical deviation fusion by using the accuracy of the sea surface high slope of the multi-source satellite altimeter, the distance to the grid point to be calculated, and the multi-source fusion weighting factor of the satellite altimeter;

[0042] 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:

[0043]

[0044] or

[0045]

[0046] Among them, d i is the distance from the ith observation point to the grid point to be determined; σ i is the sea surface high slope accuracy at the i-th observation point; W s j is the multi-source fusion weighting factor of the satellite altimeter corresponding to the i-th observation point;

[0047] Step D2: Calculate the vertical deviation component of the sea surface high slope data from the multi-source satellite altimeter to obtain a high-resolution ocean vertical deviation;

[0048] The observation equation for the average value of the sea surface high slope and vertical deviation component is:

[0049]

[0050] Among them, ε i 、v i and α i are the sea surface height slope, residual and azimuth of the i-th observation point respectively; n represents the number of observation points within a certain sea area near the grid point; and is the average value of the meridian component and the meridian component of the grid point;

[0051] According to the basic principle of least squares:

[0052]

[0053] Calculate the average values ​​of the meridian component and the zenithal component of the vertical deviation on the grid ξ and

[0054] As an improvement of the above system, the processing process of the vertical line deviation accuracy assessment module includes:

[0055] Step E1: Interpolate the high-resolution ocean vertical deviation grid data at the measured vertical deviation point to obtain the corresponding vertical deviation meridian component DOV_N k and the DOV_E component k , and then use the refined measured vertical deviation meridian component of the corresponding position and Maoyou component Calculate the residual value RES_N of the meridian component and the meridian component of the vertical deviation at the measured point k and RES_E k ;

[0056]

[0057] Step E2: Statistically analyze the residual values ​​of the meridian component and the meridian component of the vertical deviation at the measured point to calculate the evaluation accuracy of the ocean vertical deviation model.

[0058] As an improvement to the above system, the ocean vertical deviation model is evaluated with the root mean square accuracy:

[0059]

[0060] Where RMS-Nw is the root mean square of the meridian component of the vertical deviation calculated from the j-th type of satellite altimeter data; RMS_E j Calculate the root mean square of the vertical deviation of the j-th type of satellite altimeter data; N represents the number of verification points for evaluating the j-th type of satellite altimeter; RES_N k and RES_E k It is the residual value of the meridian component and the meridian component of the vertical deviation at the measured point.

[0061] The present application also provides a vertical line deviation adaptive fusion method based on multi-source satellite altimeter data, which is implemented based on the above system and includes:

[0062] 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, and removes the sea surface time-varying noise from the sea surface height data according to the satellite observation task classification to obtain high-precision sea surface height data;

[0063] Step 2) The sea surface height slope calculation module performs differential processing on the sea surface height data of the multi-source satellite altimeters, and calculates the sea surface height slope information after eliminating the gross errors;

[0064] Step 3) the multi-source fusion factor adaptive iteration module calculates 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 sea surface precision of the multi-source satellite altimeter;

[0065] Step 4) using the accuracy of the sea surface high slope, 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 slope fusion weight required for the multi-source vertical line deviation fusion is calculated, and the vertical line deviation component of the sea surface high slope data of the single-satellite altimeter is solved to obtain the ocean vertical line deviation grid data of the single-satellite altimeter;

[0066] Step 5) The vertical deviation accuracy assessment module performs accuracy assessment on the ocean vertical deviation grid data of the single-satellite altimeter respectively; then, combining the sea surface high precision of the multi-source satellite altimeter and the assessment accuracy obtained by the vertical deviation accuracy assessment module, dynamically adjusts the multi-source fusion weighting factor of each type of satellite altimeter data for iterative update, and calculates the adaptive update value of the multi-source fusion weighting factor;

[0067] Step 6) Using the accuracy of the sea surface high slope, 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 slope fusion weight required for the multi-source vertical deviation fusion is calculated, and the vertical deviation component of the sea surface high slope data of the multi-source satellite altimeter is solved to obtain high-resolution ocean vertical deviation grid data.

[0068] Compared with the prior art, the advantages of this application are:

[0069] 1. This application has established for the first time a high-resolution vertical deviation solution system that adaptively integrates multi-source satellite altimetry data, which can construct a higher-resolution ocean vertical deviation model.

[0070] 2. The high-resolution vertical deviation solution method of adaptively fused multi-source satellite altimetry data proposed in this application can fully tap the value of satellite altimetry data of different observation modes and different observation tasks, and fully, flexibly and efficiently utilize multi-source altimetry data to improve the spatial resolution of ocean vertical deviations. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 The figure shows the structure diagram of the vertical deviation adaptive fusion system based on multi-source satellite radar altimeter;

[0072] Figure 2 Shown is a flow chart of the vertical deviation adaptive fusion method based on multi-source satellite radar altimeters. DETAILED DESCRIPTION

[0073] The technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0074] The adaptive fusion system and method of vertical deviation 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 tasks (ERM, GM) and different observation modes (LRM, synthetic aperture mode, wide swath interferometry, dual-satellite follow-up altimetry and GNSS-R altimetry, etc.), multi-source altimetry data can be fully, flexibly and efficiently utilized to improve the spatial resolution of vertical deviation.

[0075] Example 1

[0076] like Figure 1 As shown, the present application proposes a vertical line deviation adaptive fusion system based on multi-source satellite altimeter data, including a multi-source altimetry data classification preprocessing module, a sea surface high slope calculation module, a multi-source fusion factor adaptive iteration module, a multi-source vertical line deviation fusion module and a vertical line deviation accuracy evaluation module.

[0077] The design of each module is as follows:

[0078] 1. 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 remove abnormal values ​​of altimetry data, and remove sea surface time-varying noise from sea surface height data according to satellite observation task classification to obtain high-precision sea surface height data (time, longitude, latitude, sea surface height);

[0079] The processing process of the multi-source altimetry data classification preprocessing module includes:

[0080] 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;

[0081] Step 2: Classify and process by observation mission; classify multi-source altimetry data by satellite observation mission, 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.

[0082] 2. Sea surface height and slope calculation module: used to perform differential processing on the sea surface height data (time, longitude, latitude, sea surface height) of multi-source satellite altimeters along the survey line, and calculate the sea surface height slope information (time, longitude, latitude, slope, azimuth, accuracy) after eliminating gross errors;

[0083] The processing of the sea surface high slope calculation module includes:

[0084] Step 1: Differential processing: Calculate the time difference Δt, longitude difference Δλ, latitude difference Δλ between two adjacent points for the sea surface height data (time, longitude, latitude, sea surface height) of the multi-source satellite altimeter. and the sea surface height difference Δh, and calculate the spherical distance d between two adjacent points (point p and point g) pq ;

[0085] Δt=t q -t p

[0086] Δλ=λ q -λ p

[0087]

[0088] Δh=hx-h p

[0089]

[0090] In the formula, t p , t q ,λp ,λ q , h q ,h p They respectively represent the time, longitude, latitude and sea level height of two adjacent points; Re is the average radius of the earth.

[0091] Step 2: Eliminate gross errors; set thresholds for the time difference and distance between two adjacent points of the multi-source satellite altimeter. If the thresholds are exceeded, they are considered to be abnormal values ​​of sea surface height differences caused by the sub-satellite point passing through land and islands, etc., and are eliminated. At the same time, sea surface height differences that exceed the reasonable range are eliminated.

[0092] Step 3: Calculate the sea surface high slope; use the altimetry data after eliminating gross errors to calculate the time, longitude, latitude, slope, azimuth, and accuracy of the sea surface high slope;

[0093]

[0094]

[0095] In the formula, is the average latitude of two adjacent points, Respectively represent the high accuracy of the sea surface at two adjacent points. ( This can be a fixed value assigned by external evaluation or calculated based on altimeter data);

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

[0099] The multi-source fusion weighting factor is initially set to:

[0100]

[0101] or

[0102]

[0103] Among them, W sj is the multi-source fusion weighting factor of the j-th type of satellite altimeter data; m s j represents the sea surface high accuracy of the jth type of satellite altimeter, which is usually determined by an external reliable prior value; l represents the number of satellite altimetry data involved in the fusion (different missions 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 altimetry data involved in the fusion).

[0104] Step 2: Adaptively update the multi-source fusion factor; combine the sea surface high precision and vertical deviation accuracy evaluation module of the multi-source satellite altimeter to obtain the evaluation accuracy, dynamically adjust the multi-source fusion factor of each type of satellite altimeter data for iterative update, and use it for adaptive fusion of multi-source satellite altimeter data;

[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 accuracy of the sea surface data of the jth type of satellite altimeter, which is usually determined by an external reliable prior value; m d j represents the evaluation accuracy of the vertical deviation 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 vertical deviation fusion module: It is used to calculate the multi-source slope fusion weights required for multi-source vertical deviation fusion by using the accuracy of sea surface high slope, 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 solve the vertical deviation component of the sea surface high slope data of the multi-source satellite altimeter to obtain high-resolution ocean vertical deviation grid data (longitude, latitude, meridian component, and meridian component);

[0111] The processing of the multi-source vertical deviation fusion module includes:

[0112] Step 1: Calculation of multi-source slope fusion weights: Calculate the multi-source slope fusion weights required for multi-source vertical deviation fusion using the accuracy of the sea surface high slope of the multi-source satellite altimeter, the distance to the grid point to be calculated, and the multi-source fusion weighting factor of the satellite altimeter;

[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] or

[0116]

[0117] Where, d i is the distance from the ith observation point to the grid point to be determined; σ i is the sea surface high slope accuracy at the i-th observation point; W s j is the multi-source fusion weighting factor of the satellite altimeter corresponding to the i-th observation point.

[0118] Step 2: Calculation of vertical deviation components: used to calculate the vertical deviation components of the sea surface high slope data of the multi-source satellite altimeter to obtain high-resolution ocean vertical deviation (longitude, latitude, meridian component, and meridian component);

[0119] If there are n observation data within a certain sea area near the grid point, the observation equation for the average value of the sea surface high slope and vertical deviation component is:

[0120]

[0121] In the formula, ε i 、v i and α i are the sea surface height slope, residual and azimuth of the i-th observation point respectively; and It is the average value of the meridian component and the meridian component at this grid point.

[0122] According to the basic principle of least squares:

[0123]

[0124] Calculate the average value of the meridian component and the meridian component of the vertical deviation on the grid and

[0125] 5. Vertical deviation accuracy assessment module: It is used to use the refined measured vertical deviation data as verification data, interpolate the high-resolution ocean vertical deviation grid data at the measured vertical deviation points to obtain the corresponding vertical deviation meridian component and meridian component, use the refined measured vertical deviation component values ​​at the corresponding positions to calculate the vertical deviation residual value, and calculate the assessment accuracy of the ocean vertical deviation model based on the residual.

[0126] The processing of the vertical line deviation accuracy assessment module includes:

[0127] Step 1: Calculate the vertical deviation residual: Interpolate the high-resolution ocean vertical deviation grid data at the measured vertical deviation point to obtain the corresponding vertical deviation meridian component DOV_N k and the DOV_E component k , and then use the refined measured vertical deviation meridian component of the corresponding position and Maoyou component Calculate the residual values ​​of the meridian component and the meridian component of the vertical deviation at the measured point;

[0128]

[0129] Step 2: Assess the accuracy of vertical deviation; perform statistics on the residual values ​​of the meridian component and the meridian component of the vertical deviation at the measured point to calculate the assessment accuracy of the ocean vertical deviation model; there are many ways to calculate the accuracy, such as root mean square.

[0130]

[0131] Among them, RMS_N j Calculate the root mean square of the meridian component of the vertical deviation for the jth type of satellite altimeter data; RMS_E j Calculate the root mean square of the truncated and unitary components of the vertical deviation 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.

[0132] Example 2

[0133] like Figure 2 As shown, the present application also proposes a vertical deviation adaptive fusion method based on a multi-source satellite radar altimeter, which is implemented based on the above system and includes:

[0134] 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 to remove abnormal values ​​of the altimetry data, and removes the sea surface time-varying noise from the sea surface height data according to the satellite observation task classification to obtain high-precision sea surface height data (time, longitude, latitude, sea surface height);

[0135] Step 2) The sea surface height slope calculation module performs differential processing on the sea surface height data (time, longitude, latitude, sea surface height) of the multi-source satellite altimeter, and calculates the sea surface height slope information (time, longitude, latitude, slope, azimuth, accuracy) after eliminating gross errors;

[0136] Step 3) the multi-source fusion factor adaptive iteration module calculates 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 sea surface precision of the multi-source satellite altimeter;

[0137] Step 4) Calculate the multi-source slope fusion weights required for multi-source vertical deviation fusion by using the accuracy of the sea surface high slope, 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 solve the vertical deviation component of the sea surface high slope data of the single-satellite altimeter to obtain the ocean vertical deviation grid data (longitude, latitude, meridian component, and meridian component) of the single-satellite altimeter;

[0138] Step 5) The vertical deviation accuracy assessment module performs accuracy assessment on the ocean vertical deviation grid data of the single-satellite altimeter respectively; then, combining the sea surface high precision of the multi-source satellite altimeter and the assessment accuracy obtained by the vertical deviation accuracy assessment module, dynamically adjusts the multi-source fusion weighting factor of each type of satellite altimeter data for iterative update, and calculates the adaptive update value of the multi-source fusion weighting factor;

[0139] Step 6) Utilize the accuracy of the sea surface high slope, 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 slope fusion weight required for the multi-source vertical deviation fusion, and solve the vertical deviation component of the sea surface high slope data of the multi-source satellite altimeter to obtain high-resolution ocean vertical deviation grid data (longitude, latitude, meridian component, and meridian component).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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:

[0146] Execute the steps of the above method.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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 vertical deviation 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 remove abnormal values ​​of altimetry data, and remove sea surface time-varying noise from sea surface height data according to satellite observation task classification to obtain high-precision sea surface height data; Sea surface height slope calculation module: used to perform differential processing on the sea surface height data of multi-source satellite altimeters along the survey line, and calculate the sea surface height slope information after eliminating the gross errors; Multi-source fusion factor adaptive iteration module: used to 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 sea surface precision of the multi-source satellite altimeter; combined with the high sea surface precision of the multi-source satellite altimeter and the evaluation accuracy obtained by the vertical deviation accuracy evaluation module, dynamically adjust the multi-source fusion weighting factor of each type of satellite altimeter data for iterative update, for multi-source vertical deviation adaptive fusion; Multi-source vertical deviation fusion module: used to calculate the multi-source slope fusion weights required for multi-source vertical deviation fusion by using the accuracy of sea surface high slope, 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 solve the vertical deviation component of the sea surface high slope data of the multi-source satellite altimeter to obtain high-resolution ocean vertical deviation grid data; and Vertical deviation accuracy assessment module: It is used to use the refined measured vertical deviation data as verification data, interpolate the high-resolution ocean vertical deviation grid data at the measured vertical deviation points to obtain the corresponding vertical deviation meridian component and meridian component, use the refined measured vertical deviation component values ​​at the corresponding positions to calculate the vertical deviation residual value, and calculate the assessment accuracy of the ocean vertical deviation model based on the residual.

2. The vertical deviation adaptive fusion system based on multi-source satellite altimeter data according to claim 1 is 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.

3. The vertical deviation adaptive fusion system based on multi-source satellite altimeter data according to claim 1 is characterized in that: The processing process of the sea surface high slope calculation module includes: Step B1: Calculate the time difference Δt, longitude difference Δλ, and latitude difference between two adjacent points for the sea surface height data of the multi-source satellite altimeter and the sea surface height difference Δh, and calculate the spherical distance d between two adjacent points pq , point p and point q are two adjacent points: Δt=t q -t p Dλ=λ q -l p Δh=h q -h p Among them, t p , t q ,λ p ,λ q , h q ,h p Represent the time, longitude, latitude and sea level of two adjacent points respectively; Re is the average radius of the earth; Step B2: setting thresholds for the time difference and distance between two adjacent points of the multi-source satellite altimeter respectively. If the thresholds are exceeded, the points are judged as abnormal sea surface height differences caused by the sub-satellite point passing through land or islands and are removed. At the same time, the sea surface height differences that exceed the reasonable range are removed. Step B3: Calculate the time t of the sea surface high slope using the height measurement data after eliminating gross errors dov , longitude λ dov ,latitude Slope ε dov 、azimuth az dov and precision σ dov : in, is the average latitude of two adjacent points, Respectively represent the high accuracy of the sea surface between two adjacent points.

4. The vertical deviation adaptive fusion system based on multi-source satellite altimeter data according to claim 1 is 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 sea surface precision of the multi-source satellite altimeter; The multi-source fusion weighting factor is initially set to: or 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 sea surface high precision of the jth type of satellite altimeter; l represents the number of satellite altimeter data involved in the fusion, and different tasks of the same type of satellite altimeter are divided into different categories; Step C2: combining the evaluation accuracy obtained by the sea surface high precision and vertical deviation accuracy evaluation module of the multi-source satellite altimeter, dynamically adjusting the multi-source fusion factor of each type of satellite altimeter data for iterative update, for adaptive fusion of multi-source satellite altimeter data; The multi-source fusion weighting factor is iteratively updated as: or Among them, m d j It represents the evaluation accuracy of the vertical deviation calculated by the j-th type of satellite altimeter data.

5. The vertical deviation adaptive fusion system based on multi-source satellite altimeter data according to claim 1 is characterized in that: The processing process of the multi-source vertical line deviation fusion module includes: Step D1: Calculate the multi-source slope fusion weight required for multi-source vertical deviation fusion by using the accuracy of the sea surface high slope of the multi-source satellite altimeter, the distance to the grid point to be calculated, and the multi-source fusion weighting factor of the satellite altimeter; 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: or Among them, d i is the distance from the ith observation point to the grid point to be determined; σ i is the sea surface high slope accuracy at the i-th observation point; W s j is the multi-source fusion weighting factor of the satellite altimeter corresponding to the i-th observation point; Step D2: Calculate the vertical deviation component of the sea surface high slope data from the multi-source satellite altimeter to obtain a high-resolution ocean vertical deviation; The observation equation for the average value of the sea surface high slope and vertical deviation component is: Among them, ε i , ν i and α i are the sea surface height slope, residual and azimuth of the i-th observation point respectively; n represents the number of observation points within a certain sea area near the grid point; and is the average value of the meridian component and the meridian component of the grid point; According to the basic principle of least squares: Calculate the average value of the meridian component and the meridian component of the vertical deviation on the grid and 6. The vertical deviation adaptive fusion system based on multi-source satellite altimeter data according to claim 1 is characterized in that: The processing process of the vertical line deviation accuracy assessment module includes: Step E1: Interpolate the high-resolution ocean vertical deviation grid data at the measured vertical deviation point to obtain the corresponding vertical deviation meridian component DOV_N k and the DOV_E component k , and then use the refined measured vertical deviation meridian component of the corresponding position and Maoyou component Calculate the residual value RES_N of the meridian component and the meridian component of the vertical deviation at the measured point k and RES_E k ; Step E2: Statistically analyze the residual values ​​of the meridian component and the meridian component of the vertical deviation at the measured point to calculate the evaluation accuracy of the ocean vertical deviation model.

7. The vertical deviation adaptive fusion system based on multi-source satellite altimeter data according to claim 6 is characterized in that: The estimated accuracy of the ocean vertical deviation model is the root mean square: Among them, RMS_N j Calculate the root mean square of the meridian component of the vertical deviation for the jth type of satellite altimeter data; RMS_E j Calculate the root mean square of the vertical deviation of the j-th type of satellite altimeter data; N represents the number of verification points for evaluating the j-th type of satellite altimeter; RES_N k and RES_E k It is the residual value of the meridian component and the meridian component of the vertical deviation at the measured point.

8. A method for adaptively fusion of vertical deviation 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, and removes the sea surface time-varying noise from the sea surface height data according to the satellite observation task classification to obtain high-precision sea surface height data; Step 2) The sea surface height slope calculation module performs differential processing on the sea surface height data of the multi-source satellite altimeters, and calculates the sea surface height slope information after eliminating the gross errors; Step 3) the multi-source fusion factor adaptive iteration module calculates 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 sea surface precision of the multi-source satellite altimeter; Step 4) using the accuracy of the sea surface high slope, 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 slope fusion weight required for the multi-source vertical line deviation fusion is calculated, and the vertical line deviation component of the sea surface high slope data of the single-satellite altimeter is solved to obtain the ocean vertical line deviation grid data of the single-satellite altimeter; Step 5) The vertical deviation accuracy assessment module performs accuracy assessment on the ocean vertical deviation grid data of the single-satellite altimeter respectively; then, combining the sea surface high precision of the multi-source satellite altimeter and the assessment accuracy obtained by the vertical deviation accuracy assessment module, dynamically adjusts the multi-source fusion weighting factor of each type of satellite altimeter data for iterative update, and calculates the adaptive update value of the multi-source fusion weighting factor; Step 6) Using the accuracy of the sea surface high slope, 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 slope fusion weight required for the multi-source vertical deviation fusion is calculated, and the vertical deviation component of the sea surface high slope data of the multi-source satellite altimeter is solved to obtain high-resolution ocean vertical deviation grid data.

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