A Vertical Deviation Gridding Method and System Based on Waveform Re-tracking Quality Weighting

Abstract

This invention belongs to the field of remote sensing mapping and marine surveying technology. Specifically, it relates to a vertical deviation gridding method based on waveform retracking quality weighting. The method includes: Step 1) performing waveform retracking on the 20Hz echo raw waveform data in the radar altimeter SGDR data to obtain the 1Hz accuracy, latitude, and distance measurements and their corresponding waveform retracking quality parameters, thereby obtaining the sea surface height; Step 2) calculating the residual vertical deviation ε along the trajectory. res And its corresponding longitude and latitude; Step 3) Calculate the signal cross-covariance matrix C in the LSC algorithm. ξε C ηε and C εε Step 4) Based on the waveform retracking quality parameters corresponding to the 1Hz distance measurement obtained in Step 1), calculate the noise covariance matrix C in the LSC algorithm. nn Step 5) According to C nn , and C εε C ξε and C ηε Using the LSC algorithm, the residual vertical deviation ε along the trajectory obtained in step 2) is... res Gridding is used to obtain high-precision vertical deviation grid data based on waveform quality weighting.

Description

Technical Field

[0001] This invention belongs to the field of remote sensing mapping and marine surveying technology, specifically, it relates to a vertical deviation gridding method and system based on waveform retracking quality weighting. Background Technology

[0002] A radar altimeter is an important active microwave remote sensor carried on ocean remote sensing satellites. It acquires ocean observation data such as sea surface height (SSH), significant wave height, and wind speed by transmitting radar pulse signals towards the sea surface and receiving the echo signals. Gravity-related parameters, such as the ocean geoid and vertical deviation, can be extracted from the ocean observation data obtained by the satellite altimeter to further deduce the ocean gravity field. Compared to traditional shipborne gravity measurement methods, satellite altimeters can accomplish in a few months what used to take a century, offering unparalleled advantages. Compared to gravity satellite observations, satellite altimeters can obtain shortwave ocean gravity fields globally, and are currently the only means of obtaining globally covered shortwave gravity fields. Therefore, satellite altimeters are currently the only means of obtaining high-precision global ocean shortwave gravity fields.

[0003] In ocean gravity field inversion methods based on satellite radar altimeter data, the vertical deviation method is currently the most superior method due to its ability to effectively suppress the influence of long-wavelength errors, and it is widely used by scholars both domestically and internationally. However, a prerequisite for using the vertical deviation method to invert the ocean gravity field is obtaining high-precision, high-resolution vertical deviation grid data. The vertical deviation gridding method is the key and challenging aspect of obtaining high-precision, high-resolution vertical deviation data, as it directly affects the resolution and accuracy of the vertical deviation grid data.

[0004] Currently, common approaches to calculating grid vertical deviation using satellite altimetry data fall into two categories: calculation based on intersection points and calculation based on data along the trajectory. The intersection-based method first requires calculating the intersection points of the ground trajectory using satellite latitude and longitude information, then solving for the vertical deviation at each intersection point, and finally gridding the data. This process is quite cumbersome and computationally intensive, especially for joint processing of multi-source satellite altimeter data. The least squares collocation (LSC) method based on data along the trajectory directly converts the sea surface height (SSH) from the radar altimeter along the trajectory slope into vertical deviation components on a regular grid, without needing to calculate trajectory intersection points. This method is simpler and more advantageous.

[0005] The least squares collocation method, developed in the 1960s based on the theories of Krarup and Moritz, originated from the problem of interpolating gravity anomalies using least squares predictions. Existing techniques propose using LSC to convert geoid height (the influence of sea surface topography removed from sea level height SSH) along the trajectory slope data into grid vertical deviations. In the LSC calculation process, the weights of the trajectory slope are inversely proportional to its noise variance; therefore, determining the noise covariance matrix is ​​crucial. The noise covariance matrix characterizes the accuracy of the trajectory slope used to calculate the grid vertical deviation and is a diagonal matrix composed of the noise variance of the trajectory slope. Due to the limitation of non-repetitive missions that cannot use the method of averaging multiple measurements, existing techniques treat all sea level height SSH measurements from the same satellite altimeter mission as having equal accuracy and estimate the accuracy of the trajectory vertical deviation from the sea level height SSH accuracy according to the error propagation law for use in LSC calculations. Existing technology takes into account the different attenuation characteristics of the Ka-band signal of the SARAL / AltiKa radar altimeter and the commonly used Ku-band signal, and uses an iterative method to determine the weights of the geoid gradient of the Ka-band altimeter for LSC calculation.

[0006] In actual operation of satellite altimeters, the accuracy of different measurements from the same satellite altimeter mission varies due to differences in the external observation environment. For example, in nearshore and shallow water areas, the echo waveform of the radar altimeter is interfered with by signals from land or islands within the altimeter footprint, resulting in a much lower altimeter accuracy than in open areas. However, the aforementioned LSC calculation methods are all based on the altimeter mission's accuracy, assigning the same weight to all observation points within the same altimeter mission. However, existing methods only consider the accuracy differences between different satellite altimeter missions, neglecting the data quality differences between different measurements within the same satellite altimeter mission. This severely affects the accuracy of the grid vertical deviation product and fails to reflect the true situation of radar altimeter measurements at different distances. Furthermore, the data used in existing methods for vertical deviation calculation and gravity field inversion mostly comes from non-repeating or long-period 1Hz altimeter data, making it difficult to evaluate the accuracy of individual measurements using multiple measurement averaging methods, and failing to account for the accuracy differences between different observation points within the same satellite altimeter mission. Furthermore, existing methods cannot effectively suppress high-noise outliers, which greatly increases the workload of outlier removal and reduces the utilization rate of radar altimeter measurements; it is also detrimental to improving the accuracy of grid vertical deviation. Summary of the Invention

[0007] To address the aforementioned shortcomings of existing technologies, this invention proposes a vertical deviation gridding method based on waveform retracking quality weighting. Starting from the original waveform retracking process of the radar altimeter deriving 1Hz measurements, a suitable quality parameter is extracted for each observation point to characterize the accuracy differences between different observation points in the same satellite altimetry mission. Specifically, this involves a vertical deviation gridding method based on waveform retracking quality weighting from a radar altimeter.

[0008] This invention provides a vertical deviation gridding method based on waveform retracking quality weighting, the method comprising:

[0009] Step 1) Read the SGDR data from the radar altimeter to obtain the raw 20Hz echo waveform data; perform waveform retracking on the raw 20Hz echo waveform data from the radar altimeter SGDR data to obtain the accuracy, latitude, and distance measurements at 1Hz and their corresponding waveform retracking quality parameters, thereby obtaining the sea surface height; among which, the waveform quality retracking parameters include: the number of effective values. and root mean square error σ i ;

[0010] Step 2) Based on the 1Hz longitude, latitude, and sea level obtained in Step 1), and combining the mean dynamic sea surface topography (MDT) model and the reference geoid height, calculate the residual vertical deviation ε along the trajectory. res and their corresponding longitude and latitude;

[0011] Step 3) Based on the residual vertical deviation ε along the trajectory obtained in Step 2), res Given the longitude and latitude, calculate the first signal cross-covariance matrix C in the LSC algorithm. ξε The second signal cross-covariance matrix C in the LSC algorithm ηε And the signal covariance matrix C in the LSC algorithm εε ;

[0012] Step 4) Based on the waveform retracking quality parameters corresponding to the 1Hz distance measurement obtained in Step 1), calculate the noise covariance matrix C in the LSC algorithm. nn ;

[0013] Step 5) Based on the noise covariance matrix C obtained in step 4) nn and C obtained in step 3) εε C ξε and C ηε Using the LSC algorithm, the residual vertical deviation ε along the trajectory obtained in step 2) is... res Gridding is used to obtain high-precision vertical deviation grid data based on waveform quality weighting.

[0014] As one improvement to the above technical solution, step 1) specifically includes:

[0015] Read the SGDR data from the radar altimeter to obtain the raw waveform data of the 20Hz echo;

[0016] A weighted four-parameter maximum likelihood estimation algorithm was used to perform waveform retracking processing on the original 20Hz echo waveform data. Linear regression was then used to derive and obtain the longitude, latitude, and distance measurements R at 1Hz, along with their corresponding waveform retracking quality parameters. These parameters include the number of effective values. and root mean square error σ i ;

[0017] The sea level height (SSH) can be obtained using the following formula:

[0018] SSH = H - (R + ΔR) corr (33)

[0019] Where SSH is sea level, i.e., the sea level to be measured by the radar altimeter; ΔR corr denoted by atmospheric path delay and geophysical error; H represents the height of the altimeter orbit above the reference ellipsoid.

[0020] As an improvement to the above technical solution, step 2) specifically includes:

[0021] Remove the mean dynamic sea surface topography (MDT) model from the sea surface height (SSH) obtained in step 1) to obtain the geoid height (N):

[0022] N = SSH-MDT (34)

[0023] Among them, MDT stands for Dynamic Sea Surface Topography MDT Model;

[0024] Remove the reference geoid height N from the geoid height N. ref The residual geoid height ΔN is obtained:

[0025] ΔN=NN ref (35)

[0026] The residual vertical deviation along the trajectory and its corresponding longitude and latitude are respectively

[0027]

[0028]

[0029]

[0030] Where, ε res This indicates the residual vertical deviation along the trajectory; The residual vertical deviation ε along the trajectoryres latitude; λ res The residual vertical deviation ε along the trajectory res longitude; d ij The distance between two adjacent observation points i and j along the trajectory;

[0031] ΔN i The residual geoid height is given by observation point i, which is one of two adjacent observation points along the trajectory. The residual vertical deviation ε along the trajectory of observation point i, one of two adjacent observation points along the trajectory. res latitude; λ i The residual vertical deviation ε along the trajectory of observation point i, one of two adjacent observation points along the trajectory. res longitude;

[0032] ΔN j The residual geoid height of observation point j, one of two adjacent observation points along the trajectory; The residual vertical deviation ε along the trajectory of observation point j, one of two adjacent observation points along the trajectory. res latitude; λ j The residual vertical deviation ε along the trajectory of observation point j, one of two adjacent observation points along the trajectory. res Longitude.

[0033] As one improvement to the above technical solution, step 3) specifically includes:

[0034] Assume two observation points on the unit sphere are p and q, and the deviation ε along the trajectory at observation point p is... p Its corresponding azimuth angle is α p The deviation ε along the trajectory at observation point q q Its corresponding azimuth angle is α q The latitude and longitude of observation points p and q are respectively and in, Let λ be the latitude of the observation point p; p Let p be the longitude of the observation point. Let λ be the latitude of the observation point q; q Let q be the longitude of the observation point;

[0035] Determine the azimuth angle α between two points using the trigonometric functions of the sphere. pq :

[0036]

[0037] The first signal cross-covariance matrix C in the LSC algorithm ξε The second signal cross-covariance matrix C in the LSC algorithm ηεAnd the signal covariance matrix C in the LSC algorithm εε They are respectively

[0038] C ξε =C ll cosα pq cos(α q -α pq )-C mm sinα pq sin(α q -α pq (40)

[0039] C ηε =C ll sinα pq cos(α q -α pq )+C mm cosα pq sin(α q -α pq (41)

[0040] C εε =C ll cos(α p -α pq cos(α) q -α pq )+C mm sin(α p -α pq sin(α) q -α pq (42)

[0041] Among them, C ξε C is the first signal cross-covariance matrix in the LSC algorithm; ηε C is the second signal cross-covariance matrix in the LSC algorithm; εε This refers to the signal covariance matrix in the LSC algorithm.

[0042] C ll It is the covariance function of the longitudinal components; C mm It is the covariance function of the horizontal component.

[0043] As an improvement to the above technical solution, step 4) specifically includes:

[0044] Based on the number of valid values Determine the effective number of 1Hz distance measurements at observation point i.

[0045]

[0046] The effective rate ranges from 0 to 1.

[0047] Taking into account the effective number of 1Hz ranging values ​​at observation point i and root mean square error σ i 2 Then the 1Hz ranging value r of observation point i i The variance is:

[0048]

[0049] in, Let r be the 1Hz ranging value of observation point i. i The variance;

[0050] Calculate the SSH observation h at observation point i. i variance Represented as

[0051]

[0052] Similarly, the SSH observation value h at observation point j is... j variance Represented as

[0053]

[0054] in, σ is the effective rate of the 1Hz ranging value at observation point j; j 2 Let be the root mean square error of the 1Hz ranging value at observation point j;

[0055] Assuming observation points i and j are two adjacent observation points, according to the error propagation law, the variance of the residual vertical deviation along the trajectory can be obtained as follows:

[0056]

[0057] in, It is the variance of the residual vertical deviation along the trajectory of observation point i, that is, the standard deviation of the geoid slope corresponding to observation point i. and These are the standard deviations of the SSH (Short Shift) at observation point i and observation point j, respectively, representing the standard deviations of their SSH values; d ij The distance between two adjacent observation points i and j;

[0058] Following the above procedure, using the quality parameters of radar altimeter echo retracking, the variance of the residual vertical deviation along the trajectory at each observation point is estimated. Substituting this variance into the diagonal matrix, the noise covariance matrix C based on waveform quality weighting is obtained. nn ;

[0059]

[0060] As an improvement to the above technical solution, step 5) specifically includes:

[0061] Based on the noise covariance matrix C obtained in step 4), nn And the first signal cross-covariance matrix C in the LSC algorithm obtained in step 3). ξε The second signal cross-covariance matrix C in the LSC algorithm ηε And the signal covariance matrix C in the LSC algorithm εε Using the LSC algorithm, the residual vertical deviation ε along the trajectory obtained in step 2) is... res Gridding is used to obtain high-precision vertical deviation grid data based on waveform quality weighting.

[0062]

[0063] in, For high-precision vertical deviation grid data based on waveform quality weighting; ξ res The residual vertical deviation ε along the trajectory res The north-south component after gridding; η res The residual vertical deviation ε along the trajectory res The components of the gridded data;

[0064] High-precision vertical deviation grid data based on waveform quality weighting includes: residual vertical deviation ε along the trajectory. res The north-south and east-west components after gridding.

[0065] This invention provides a vertical deviation gridding system based on waveform retracking quality weighting, the system comprising: a data preprocessing module, a residual vertical deviation extraction module, and a vertical deviation gridding module;

[0066] The data preprocessing module is used to read the SGDR data from the radar altimeter to obtain the raw 20Hz echo waveform data; perform waveform retracking on the raw 20Hz echo waveform data from the radar altimeter SGDR data to obtain the accuracy, latitude, and distance measurements at 1Hz and their corresponding waveform retracking quality parameters, thereby obtaining the sea surface height; wherein, the waveform quality retracking parameters include: the number of effective values. and root mean square error σ i ;

[0067] The residual vertical deviation extraction module is used to calculate the residual vertical deviation ε along the trajectory based on the obtained 1Hz longitude, latitude, and sea surface height, combined with the dynamic sea surface topography MDT model and the reference geoid height. res and their corresponding longitude and latitude;

[0068] The vertical deviation gridding module is used to calculate the residual vertical deviation ε along the trajectory. res Using the latitude and longitude information, calculate the first signal cross-covariance matrix C in the LSC algorithm. ξε The second signal cross-covariance matrix C in the LSC algorithm ηε And the signal covariance matrix C in the LSC algorithm εε Based on the obtained waveform retracking quality parameters at sea level, the noise covariance matrix C, weighted by waveform quality, is calculated. nn Using the LSC algorithm, the vertical deviation along the track obtained by the residual vertical deviation extraction module is gridded to obtain high-precision vertical deviation grid data based on waveform quality weighting.

[0069] As one of the improvements to the above technical solution, the specific implementation process of the data preprocessing module is as follows:

[0070] Read the SGDR data from the radar altimeter to obtain the raw waveform data of the 20Hz echo;

[0071] A weighted four-parameter maximum likelihood estimation algorithm was used to perform waveform retracking processing on the original 20Hz echo waveform data. Linear regression was then used to derive and obtain the longitude, latitude, and distance measurements R at 1Hz, along with their corresponding waveform retracking quality parameters. These parameters include the number of effective values. and root mean square error σ i ;

[0072] The sea level height (SSH) can be obtained using the following formula:

[0073] SSH = H - (R + ΔR) corr (49)

[0074] Where SSH is sea level, i.e., the sea level to be measured by the radar altimeter; ΔR corr For atmospheric path delay and geophysical error; H is the height of the altimeter orbit above the reference ellipsoid.

[0075] As one of the improvements to the above technical solution, the specific implementation process of the residual vertical deviation extraction module is as follows:

[0076] Remove the dynamic sea surface topography (MDT) model from the obtained sea surface height (SSH) to obtain the geoid height (N):

[0077] N = SSH-MDT (50)

[0078] Among them, MDT stands for Dynamic Sea Surface Topography MDT Model;

[0079] Remove the reference geoid height N from the geoid height N. ref The residual geoid height ΔN is obtained:

[0080] ΔN=NN ref (51)

[0081] The residual vertical deviation along the trajectory and its corresponding longitude and latitude are respectively

[0082]

[0083]

[0084]

[0085] Where, ε res This indicates the residual vertical deviation along the trajectory; The residual vertical deviation ε along the trajectory res latitude; λ res The residual vertical deviation ε along the trajectory res longitude; d ij The distance between two adjacent observation points i and j along the trajectory

[0086] ΔN i The residual geoid height is given by observation point i, which is one of two adjacent observation points along the trajectory. The residual vertical deviation ε along the trajectory of observation point i, one of two adjacent observation points along the trajectory. res latitude; λ i The residual vertical deviation ε along the trajectory of observation point i, one of two adjacent observation points along the trajectory. res longitude;

[0087] ΔN j The residual geoid height of observation point j, one of two adjacent observation points along the trajectory; The residual vertical deviation ε along the trajectory of observation point j, one of two adjacent observation points along the trajectory. res latitude; λ j The residual vertical deviation ε along the trajectory of observation point j, one of two adjacent observation points along the trajectory. res Longitude.

[0088] As one of the improvements to the above technical solution, the specific implementation process of the vertical deviation gridding module is as follows:

[0089] Assume two observation points on the unit sphere are p and q, and the deviation ε along the trajectory at observation point p is... p Its corresponding azimuth angle is α p The deviation ε along the trajectory at observation point q qIts corresponding azimuth angle is α q The latitude and longitude of observation points p and q are respectively and in, Let λ be the latitude of the observation point p; p Let p be the longitude of the observation point. Let λ be the latitude of the observation point q; q Let q be the longitude of the observation point;

[0090] Determine the azimuth angle α between two points using the trigonometric functions of the sphere. pq :

[0091]

[0092] The first signal cross-covariance matrix C in the LSC algorithm ξε The second signal cross-covariance matrix C in the LSC algorithm ηε And the signal covariance matrix C in the LSC algorithm εε They are respectively

[0093] C ξε =C ll cosα pq cos(α q -α pq )-C mm sinα pq sin(α q -α pq (56)

[0094] C ηε =C ll sinα pq cos(α q -α pq )+C mm cosα pq sin(α q -α pq (57)

[0095] C εε =C ll cos(α p -α pq cos(α) q -α pq )+C mm sin(α p -α pq sin(α) q -α pq (58)

[0096] Among them, C ξεC is the first signal cross-covariance matrix in the LSC algorithm; ηε C is the second signal cross-covariance matrix in the LSC algorithm; εε This refers to the signal covariance matrix in the LSC algorithm.

[0097] C ll It is the covariance function of the horizontal component; C mm It is the covariance function of the longitudinal component;

[0098] Based on the number of valid values Determine the effective number of 1Hz distance measurements at observation point i.

[0099]

[0100] The effective rate ranges from 0 to 1.

[0101] Assuming observation points i and j are two adjacent observation points, and considering the effective number of the 1Hz observations at observation point i... and root mean square error Then the 1Hz ranging value r of observation point i i The variance is:

[0102]

[0103] in, Let r be the 1Hz ranging value of observation point i. i The variance;

[0104] Calculate the SSH observation h at observation point i. i variance Represented as

[0105]

[0106] Then the SSH observation value h at observation point j j variance Represented as

[0107]

[0108] in, σ is the effective number rate of the 1Hz observations at observation point j; j 2 Let be the root mean square error of the 1Hz observation at observation point j;

[0109] According to the law of error propagation, the variance of the residual perpendicular deviation along the trajectory can be obtained as follows:

[0110]

[0111] in, It is the variance of the residual vertical deviation along the trajectory of observation point i, that is, the standard deviation of the geoid slope corresponding to observation point i. and These are the standard deviations of the SSH (Short Shift) at observation point i and observation point j, respectively, representing the standard deviations of their SSH values; d ij The distance between two adjacent observation points i and j;

[0112] Following the above procedure, using the quality parameters of radar altimeter echo retracking, the variance of the residual vertical deviation along the trajectory at each observation point is estimated. Substituting this variance into the diagonal matrix, the noise covariance matrix C based on waveform quality weighting is obtained. nn ;

[0113]

[0114] Based on the noise covariance matrix C obtained in step 4), nn And the first signal cross-covariance matrix C in the LSC algorithm obtained in step 3). ξε The second signal cross-covariance matrix C in the LSC algorithm ηε And the signal covariance matrix C in the LSC algorithm εε Using the LSC algorithm, the residual vertical deviation ε along the trajectory obtained in step 2) is... res Gridding is used to obtain high-precision vertical deviation grid data based on waveform quality weighting.

[0115]

[0116] in, For high-precision vertical deviation grid data based on waveform quality weighting; ξ res The residual vertical deviation ε along the trajectory res The north-south component after gridding; η res The residual vertical deviation ε along the trajectory res The components of the gridded data;

[0117] High-precision vertical deviation grid data based on waveform quality weighting includes: residual vertical deviation ε along the trajectory. res The north-south and east-west components after gridding.

[0118] The advantages of this invention compared to the prior art are:

[0119] 1. Traditional DOV estimation methods only consider the accuracy differences of different satellite altimeter data, ignoring the accuracy differences between different measurements of the same altimeter mission. The method of this invention considers the accuracy differences of the measurement values ​​of each observation point of the same radar altimeter, and can better reflect the true situation of radar altimeter measurements at different distances.

[0120] 2. Since the weights in the LSC method are inversely proportional to their own noise level, the weights of high-noise measurements are naturally very low. Therefore, traditional methods need to remove outliers before gridding the vertical deviation. However, the method of this invention can effectively suppress high-noise outliers, greatly saving the workload of removing outliers and improving the utilization rate of radar altimeter measurements.

[0121] 3. For the vertical deviation gridding of a single satellite altimeter, the traditional method assigns the same weight to all measurements, similar to the "unweighted least squares method". However, the method of this invention assigns different weights to each measurement based on the waveform retracking quality parameter, similar to the "weighted least squares method". The different contributions of each measurement are used more finely for vertical deviation gridding, which is beneficial to improving the accuracy of grid vertical deviation. Attached Figure Description

[0122] Figure 1 This is a schematic diagram of the vertical deviation gridding system based on waveform re-tracking quality weighting of the present invention;

[0123] Figure 2 This is a schematic diagram of the vertical deviation between point q and point p on the sphere in step 3) of the vertical deviation gridding method based on waveform re-tracking quality weighting of the present invention.

[0124] Figure 3 This is a flowchart of the vertical deviation gridding method based on waveform re-tracking quality weighting of the present invention;

[0125] Figure 4(a) is a histogram of the standard deviation of the residual vertical deviation along the track using the traditional LSC method;

[0126] Figure 4(b) is a histogram of the standard deviation of the residual vertical deviation along the track using the method of the present invention (i.e., the weighted LSC method based on waveform retracking quality). Detailed Implementation

[0127] The present invention will now be further described in conjunction with the accompanying drawings and examples.

[0128] This invention provides a vertical deviation gridding method based on waveform retracking quality weighting. This method overcomes the shortcomings of the current least squares configuration method gridding. According to the waveform retracking quality parameters, each measurement value is assigned a different weight. In the vertical deviation gridding process, each satellite altimeter measurement value is processed more finely, which can effectively suppress outliers, better reflect the true situation of different radar altimeter measurements, and generate higher precision grid vertical deviation.

[0129] like Figure 3 As shown, the method includes:

[0130] Step 1) Read the SGDR data from the radar altimeter to obtain the raw 20Hz echo waveform data; perform waveform retracking on the raw 20Hz echo waveform data from the radar altimeter SGDR data to obtain the accuracy, latitude, and distance measurements at 1Hz and their corresponding waveform retracking quality parameters, thereby obtaining the sea surface height; among which, the waveform quality retracking parameters include: the number of effective values. and root mean square error σ i ;

[0131] Specifically, the SGDR (Sensor Geophysical Data Records) data from the radar altimeter is read to obtain the raw waveform data of the 20Hz echo; wherein, the raw waveform data of the 20Hz echo includes: the 20Hz target echo waveform;

[0132] Using a weighted four-parameter maximum likelihood estimation algorithm, waveform retracking processing was performed on the original 20Hz echo waveform data. The longitude, latitude, and distance measurements R at 1Hz were then derived through linear regression.

[0133] The height H of the altimeter satellite orbit above the reference ellipsoid was obtained using a precise orbit determination method.

[0134] The sea level height (SSH) can be obtained using the following formula:

[0135] SSH = H - (R + ΔR) corr (65)

[0136] Where SSH is sea level, i.e., the sea level to be measured by the radar altimeter; ΔR corr This is due to atmospheric path delay and geophysical errors;

[0137] Among them, the geophysical error is the error obtained by correcting for distance measurement error terms such as dry tropospheric delay, wet tropospheric delay, ionospheric delay, and sea state deviation, as well as geophysical error terms such as ocean tides, solid earth tides, polar tides, and dynamic atmospheric pressure.

[0138] In the linear regression process of radar altimeter waveform retracking, two quality parameters are established to characterize the quality of each 1Hz distance measurement value based on the discreteness of the 20Hz data used to derive the 1Hz distance measurement value. These two quality parameters are: one is the number of effective values. This means deriving the number of valid 20Hz measurements for each 1Hz distance measurement; the other is the root mean square error σ. i That is, the root mean square of the difference between the effective measurement value at 20Hz and the derived distance measurement value at 1Hz.

[0139] The root mean square error reflects the noise level of each 1Hz measurement derived from the original 20Hz echo waveform data. The larger the value, the worse the data accuracy; the smaller the value, the better the accuracy of the derived 1Hz measurement.

[0140] Step 2) Based on the 1Hz longitude, latitude, and sea level obtained in Step 1), and combining the dynamic sea surface topography MDT model and the reference geoid height, calculate the residual vertical deviation ε along the trajectory. res and their corresponding longitude and latitude;

[0141] Specifically, the dynamic sea surface topography (MDT) model is removed from the sea surface height SSH obtained in step 1) to obtain the geoid height N:

[0142] N = SSH-MDT (66)

[0143] Among them, MDT is the Dynamic Sea Surface Topography MDT Model, which is a known model;

[0144] Remove the reference geoid height N from the geoid height N. ref The residual geoid height ΔN is obtained:

[0145] ΔN=NN ref (67)

[0146] The residual vertical deviation along the trajectory and its corresponding longitude and latitude are respectively

[0147]

[0148]

[0149]

[0150] Where, ε res This indicates the residual vertical deviation along the trajectory; The residual vertical deviation ε along the trajectory res latitude; λ res The residual vertical deviation ε along the trajectory res longitude; d ij The distance between two adjacent observation points i and j along the trajectory

[0151] ΔN i The residual geoid height is given by observation point i, which is one of two adjacent observation points along the trajectory. The residual vertical deviation ε along the trajectory of observation point i, one of two adjacent observation points along the trajectory. res latitude; λ i The residual vertical deviation ε along the trajectory of observation point i, one of two adjacent observation points along the trajectory. reslongitude;

[0152] ΔN j The residual geoid height of observation point j, one of two adjacent observation points along the trajectory; The residual vertical deviation ε along the trajectory of observation point j, one of two adjacent observation points along the trajectory. res latitude; λ j The residual vertical deviation ε along the trajectory of observation point j, one of two adjacent observation points along the trajectory. res Longitude.

[0153] Step 3) Based on the residual vertical deviation ε along the trajectory obtained in Step 2), res Given the longitude and latitude, calculate the covariance matrix C in the LSC algorithm. εε C ξε and C ηε ;

[0154] Specifically, such as Figure 2 As shown, assume two points on the unit sphere are p and q, and the deviation ε along the trajectory at point p is... p Its corresponding azimuth angle is α p The deviation ε along the trajectory at point q q Its corresponding azimuth angle is α q The latitude and longitude of points p and q are respectively and in, Let λ be the latitude of point p; p Let p be the longitude; Let λ be the latitude of point q; q Let q be the longitude; and All are obtained using the above formula (5); λ q and λ q All are obtained using the above formula (6); ε p and ε q All are obtained using the above formula (4); α p The sum is α q Known value;

[0155] Determine the azimuth angle α between two points using the trigonometric functions of the sphere. pq :

[0156]

[0157] The first signal cross-covariance matrix C in the LSC algorithm ξε The second signal cross-covariance matrix C in the LSC algorithm ηε And the signal covariance matrix C in the LSC algorithm εε They are respectively

[0158] C ξε =C ll cosα pq cos(α q -α pq )-C mm sinα pq sin(α q -α pq (72)

[0159] C ηε =C ll sinα pq cos(α q -α pq )+C mm cosα pq sin(α q -α pq (73)

[0160] C εε =C ll cos(α p -α pq cos(α) q -α pq )+C mm sin(α p -α pq sin(α) q -α pq (74)

[0161] Among them, C ξε C is the first signal cross-covariance matrix in the LSC algorithm; ηε C is the second signal cross-covariance matrix in the LSC algorithm; εε This refers to the signal covariance matrix in the LSC algorithm.

[0162] C ll It is the covariance function of the horizontal component; C mm It is the covariance function of the longitudinal components; C ll and C mm All are isotropic and can be obtained using the outlier order variance model 4 of the Tscherning and Rapp models, and are known values.

[0163] Step 4) Based on the 1Hz distance measurement obtained in Step 1), the waveform re-tracking quality parameters are as follows: and σ i Calculate the noise covariance matrix C in the LSC algorithm. nn ;

[0164] Specifically, the noise covariance matrix Cnn The noise level characterizing the residual vertical deviation along the trajectory, used to calculate the grid vertical deviation, is a diagonal matrix composed of the variances of the residual vertical deviation along the trajectory. In the LSC calculation process, the weights of the trajectory slope are inversely proportional to its variance; therefore, determining the noise covariance matrix is ​​crucial.

[0165] Based on the number of effective values ​​of the 1Hz distance measurement Determine the effective rate of 1Hz distance measurement values

[0166]

[0167] Among them, the 1Hz distance measurement value is used as the 1Hz observation value of the observation point; the effective number range is 0 to 1; the number of effective values The closer the ratio is to 20, the larger it is, indicating better accuracy of the 1Hz observation. When the number of effective values... When the value is less than 8, the 20Hz data is generally considered to be of poor quality and cannot be used to derive 1Hz observations.

[0168] Assuming observation points i and j are two adjacent observation points, and considering the effective number of the 1Hz observations at observation point i... and root mean square error σ i 2 Then the 1Hz ranging value r of observation point i i The variance is:

[0169]

[0170] in, Let r be the 1Hz ranging value of observation point i. i The variance;

[0171] When estimating the accuracy of the vertical deviation along the trajectory, the influence of radial track error can be ignored. The accuracy of each SSH observation can be directly represented by the accuracy of each altimeter distance measurement. Therefore, each SSH observation h... i variance That is, the SSH observation value h at observation point i i variance Represented as

[0172]

[0173] Then the SSH observation value h at observation point j j variance Represented as

[0174]

[0175] in, σ is the effective number rate of the 1Hz observations at observation point j; j 2 Let be the root mean square error of the 1Hz observation at observation point j;

[0176] Then by each SSH observation h i The accuracy of the geoid height is used to replace the accuracy of the corresponding geoid height. According to the law of error propagation, the variance of the residual vertical deviation along the trajectory can be obtained as follows:

[0177]

[0178] In the formula, It is the variance of the residual vertical deviation along the trajectory of observation point i, that is, the standard deviation of the geoid slope corresponding to observation point i. and These are the standard deviations of the SSH (Short Shift) at observation point i and observation point j, respectively, representing the standard deviations of their SSH values; d ij The distance between two adjacent observation points i and j;

[0179] Following the above procedure, using the quality parameters of radar altimeter echo retracking, the variance of the residual vertical deviation along the trajectory at each observation point is estimated. Substituting this variance into the diagonal matrix, the noise covariance matrix C based on waveform quality weighting is obtained. nn ;

[0180]

[0181] The difference between the weighted LSC method based on waveform retracking quality and the traditional LSC method lies in the different noise covariance matrix used.

[0182] In the traditional LSC method, sea surface height measurements from the same radar altimeter are weighted with equal precision, and the variance of the residual vertical deviation along the trajectory with equal precision is obtained through the error propagation formula. Its traditional noise covariance matrix C nn 1 for

[0183]

[0184] The weighted LSC method based on waveform retracking quality obtains quality parameters for different sea surface height measurements from the discreteness of the data during waveform retracking, and calculates the noise variance of each residual vertical deviation along the trajectory used for gridding from the quality parameters. Its noise covariance matrix is

[0185]

[0186] The waveform retracking quality-weighted vertical deviation gridding method considers the accuracy differences at each measurement point. Since the data quality varies at each measurement point, the accuracy of the geoid slope for each residual trajectory will inevitably differ. Figure 4(a) and 4(b) As can be seen, the standard deviation of the residual vertical deviation along the track based on waveform retracking quality has a large distribution range, with a minimum of 1.140 μrad and a maximum of 77.878 μrad, but it is mainly concentrated in the range of 2 to 4 μrad, with a mean of 2.848 μrad, which is close to the mean of 2.244 μrad used in the traditional LSC method.

[0187] Step 5) Based on the noise covariance matrix C obtained in step 4) nn and the covariance matrix C obtained in step 3). εε C ξε and C ηε Using the LSC algorithm, the residual vertical deviation ε along the trajectory obtained in step 2) is... res Gridding is used to obtain high-precision vertical deviation grid data based on waveform quality weighting.

[0188] Specifically, based on the noise covariance matrix C obtained in step 4), nn And the first signal cross-covariance matrix C in the LSC algorithm obtained in step 3). ξε The second signal cross-covariance matrix C in the LSC algorithm ηε And the signal covariance matrix C in the LSC algorithm εε Using the LSC algorithm, the residual vertical deviation ε along the trajectory obtained in step 2) is... res Gridding is used to obtain high-precision vertical deviation grid data based on waveform quality weighting.

[0189]

[0190] in, For high-precision vertical deviation grid data based on waveform quality weighting; ξ res The residual vertical deviation ε along the trajectory res The north-south component after gridding; η res The residual vertical deviation ε along the trajectory res The components of the gridded data;

[0191] High-precision vertical deviation grid data based on waveform quality weighting includes: residual vertical deviation ε along the trajectory. res The north-south and east-west components after gridding.

[0192] To enable clearer and more accurate analysis, this embodiment presents data results using both the traditional LSC algorithm and the LSC algorithm of this invention, as detailed below:

[0193] Table 1. Comparison of vertical deviation components obtained by the two LSC gridding methods with those of the EGM2008 model (unit: μrad)

[0194]

[0195] As shown in Table 1, compared with the accepted gravity field model EGM2008, the root mean square differences between the north-south and east-west components of the vertical deviation obtained using the weighted LSC based on waveform retracking quality of this invention are 0.918 and 1.299 μrad, respectively, representing reductions of 48% and 49% compared to the results of the traditional method. The results indicate that the new method based on waveform retracking quality significantly improves the accuracy of the vertical deviation components in a 1'×1' grid. Therefore, the weighted LSC method based on waveform retracking quality exhibits superior performance in vertical deviation gridding.

[0196] like Figure 1 As shown, the present invention also provides a vertical deviation gridding system based on waveform retracking quality weighting, the system comprising: a data preprocessing module, a residual vertical deviation extraction module, and a vertical deviation gridding module;

[0197] The data preprocessing module is used to perform waveform retracking on the 20Hz echo in the radar altimeter SGDR data to obtain the radar altimeter's 1Hz accuracy, latitude, SSH distance measurement values ​​and their corresponding waveform retracking quality parameters.

[0198] The residual vertical deviation extraction module is used to effectively calculate the short-wave gravity field. First, it employs a removal-recovery method to remove the reference geoid model corresponding to the long-wave gravity field information from the sea level height. Furthermore, since the vertical deviation is the gradient along the geoid height, it is also necessary to remove the influence of removing the average dynamic sea surface topography from the sea level height to extract the residual vertical deviation information along the track.

[0199] The vertical deviation gridding module is used to calculate the noise standard deviation of each residual vertical deviation along the track based on the obtained waveform retracking quality parameters.

[0200] The noise variance of each residual vertical deviation along the trajectory is used to construct a diagonal matrix, which is then substituted into the noise covariance matrix in the LSC algorithm. The vertical deviation along the track obtained by the residual vertical deviation extraction module is then gridded using LSC, ultimately generating high-precision vertical deviation grid data based on waveform quality weighting.

[0201] Specifically, the system includes:

[0202] The data preprocessing module is used to read the SGDR data of the radar altimeter to obtain the 20Hz echo raw waveform data; perform waveform retracking on the 20Hz echo raw waveform data in the radar altimeter SGDR data, and calculate the accuracy, latitude, sea surface height and corresponding waveform retracking quality parameters at 1Hz.

[0203] Specifically, the SGDR data of the radar altimeter is read to obtain the raw waveform data of the 20Hz echo;

[0204] A weighted four-parameter maximum likelihood estimation algorithm was used to perform waveform retracking processing on the original 20Hz echo waveform data. Linear regression was then used to derive and obtain the longitude, latitude, and distance measurements R at 1Hz, along with their corresponding waveform retracking quality parameters. These parameters include the number of effective values. and root mean square error σ i ;

[0205] The sea level height (SSH) can be obtained using the following formula:

[0206] SSH = H - (R + ΔR) corr (83)

[0207] Where SSH is sea level, i.e., the sea level to be measured by the radar altimeter; ΔR corr denoted by atmospheric path delay and geophysical error; H represents the height of the altimeter orbit above the reference ellipsoid.

[0208] The residual vertical deviation extraction module is used to calculate the residual vertical deviation ε along the trajectory based on the obtained 1Hz longitude, latitude, and sea surface height, combined with the dynamic sea surface topography MDT model and the reference geoid height. res and their corresponding longitude and latitude;

[0209] Specifically, the dynamic sea surface topography (MDT) model is removed from the obtained sea surface height (SSH) to obtain the geoid height (N):

[0210] N = SSH-MDT (84)

[0211] Among them, MDT stands for Dynamic Sea Surface Topography MDT Model;

[0212] Remove the reference geoid height N from the geoid height N. ref The residual geoid height ΔN is obtained:

[0213] ΔN=NN ref (85)

[0214] The residual vertical deviation along the trajectory and its corresponding longitude and latitude are respectively

[0215]

[0216]

[0217]

[0218] Where, ε res This indicates the residual vertical deviation along the trajectory; The residual vertical deviation ε along the trajectory res latitude; λ res The residual vertical deviation ε along the trajectory res longitude; d ij The distance between two adjacent observation points i and j along the trajectory

[0219] ΔN i The residual geoid height is given by observation point i, which is one of two adjacent observation points along the trajectory. The residual vertical deviation ε along the trajectory of observation point i, one of two adjacent observation points along the trajectory. res latitude; λ i The residual vertical deviation ε along the trajectory of observation point i, one of two adjacent observation points along the trajectory. res longitude;

[0220] ΔN j The residual geoid height of observation point j, one of two adjacent observation points along the trajectory; The residual vertical deviation ε along the trajectory of observation point j, one of two adjacent observation points along the trajectory. res latitude; λ j The residual vertical deviation ε along the trajectory of observation point j, one of two adjacent observation points along the trajectory. res Longitude.

[0221] The vertical deviation gridding module is used to calculate the residual vertical deviation ε along the trajectory. res Using the latitude and longitude information, calculate the first signal cross-covariance matrix C in the LSC algorithm. ξε The second signal cross-covariance matrix C in the LSC algorithm ηε And the signal covariance matrix C in the LSC algorithm εε Based on the obtained waveform retracking quality parameters at sea level, the noise covariance matrix C, weighted by waveform quality, is calculated. nn Using the LSC algorithm, the vertical deviation along the track obtained by the residual vertical deviation extraction module is gridded to obtain high-precision vertical deviation grid data based on waveform quality weighting.

[0222] Specifically, assuming two observation points on the unit sphere are p and q, the deviation ε along the trajectory at observation point p is... p Its corresponding azimuth angle is α p The deviation ε along the trajectory at observation point q q Its corresponding azimuth angle is α q The latitude and longitude of observation points p and q are respectively and in, Let λ be the latitude of the observation point p; p Let p be the longitude of the observation point. Let λ be the latitude of the observation point q; q Let q be the longitude of the observation point;

[0223] Determine the azimuth angle α between two points using the trigonometric functions of the sphere. pq :

[0224]

[0225] The first signal cross-covariance matrix C in the LSC algorithm ξε The second signal cross-covariance matrix C in the LSC algorithm ηε And the signal covariance matrix C in the LSC algorithm εε They are respectively

[0226] C ξε =C ll cosα pq cos(α q -α pq )-C mm sinα pq sin(α q -α pq (90)

[0227] C ηε =C ll sinα pq cos(α q -α pq )+C mm cosα pq sin(α q -α pq (91)

[0228] C εε =C ll cos(α p -α pq cos(α) q -α pq )+C mm sin(α p -αpq sin(α) q -α pq (92)

[0229] Among them, C ξε C is the first signal cross-covariance matrix in the LSC algorithm; ηε C is the second signal cross-covariance matrix in the LSC algorithm; εε This refers to the signal covariance matrix in the LSC algorithm.

[0230] C ll It is the covariance function of the horizontal component; C mm It is the covariance function of the longitudinal component;

[0231] Based on the number of valid values Determine the effective number of 1Hz distance measurements at observation point i.

[0232]

[0233] The effective rate ranges from 0 to 1.

[0234] Assuming observation points i and j are two adjacent observation points, and considering the effective number of the 1Hz observations at observation point i... and root mean square error σ i 2 Then the 1Hz ranging value r of observation point i i The variance is:

[0235]

[0236] in, Let r be the 1Hz ranging value of observation point i. i The variance;

[0237] Calculate the SSH observation h at observation point i. i variance Represented as

[0238]

[0239] Then the SSH observation value h at observation point j j variance Represented as

[0240]

[0241] in, σ is the effective number rate of the 1Hz observations at observation point j; j 2 Let be the root mean square error of the 1Hz observation at observation point j;

[0242] According to the law of error propagation, the variance of the residual perpendicular deviation along the trajectory can be obtained as follows:

[0243]

[0244] in, It is the variance of the residual vertical deviation along the trajectory of observation point i, that is, the standard deviation of the geoid slope corresponding to observation point i. and These are the standard deviations of the SSH (Short Shift) at observation point i and observation point j, respectively, representing the standard deviations of their SSH values; d ij The distance between two adjacent observation points i and j;

[0245] Following the above procedure, using the quality parameters of radar altimeter echo retracking, the variance of the residual vertical deviation along the trajectory at each observation point is estimated. Substituting this variance into the diagonal matrix, the noise covariance matrix C based on waveform quality weighting is obtained. nn ;

[0246]

[0247] Based on the noise covariance matrix C obtained in step 4), nn And the first signal cross-covariance matrix C in the LSC algorithm obtained in step 3). ξε The second signal cross-covariance matrix C in the LSC algorithm ηε And the signal covariance matrix C in the LSC algorithm εε Using the LSC algorithm, the residual vertical deviation ε along the trajectory obtained in step 2) is... res Gridding is used to obtain high-precision vertical deviation grid data based on waveform quality weighting.

[0248]

[0249] in, For high-precision vertical deviation grid data based on waveform quality weighting; ξ res The residual vertical deviation ε along the trajectory res The north-south component after gridding; η res The residual vertical deviation ε along the trajectory res The components of the gridded data;

[0250] High-precision vertical deviation grid data based on waveform quality weighting includes: residual vertical deviation ε along the trajectory. res The north-south and east-west components after gridding.

[0251] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of the present invention do not depart from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A waveform re-tracking quality weighted vertical deflection gridding method, characterized by, The method comprises: Step 1) reading SGDR data of the radar altimeter to obtain 20Hz echo original waveform data; performing waveform re-tracking on the 20Hz echo original waveform data in the SGDR data of the radar altimeter to obtain 1Hz precision, latitude, distance measurement values and corresponding waveform re-tracking quality parameters, and then obtaining sea surface height; wherein the waveform quality re-tracking parameters include: number of effective values and root mean square error σ i ; Step 2) Calculate the residual vertical deflection e along the trajectory from the longitude, latitude and sea surface height at 1 Hz obtained in step 1), combined with the Mean Dynamic Topography MDT model and the reference Geoid height res and its corresponding longitude, latitude; Step 3) Based on the residual vertical deviation ε along the trajectory obtained in Step 2), res Given the longitude and latitude, calculate the first signal cross-covariance matrix C in the LSC algorithm. ξε The second signal cross-covariance matrix C in the LSC algorithm ηε And the signal covariance matrix C in the LSC algorithm εε ; Step 4) Calculate the noise covariance matrix C in the LSC algorithm based on the waveform retrace quality parameters corresponding to the 1 Hz range measurement values obtained in step 1) nn ; Step 5) grid the along-track residual vertical deviation ε nn obtained in step 2) using the LSC algorithm to obtain high-precision vertical deviation grid data based on waveform quality weighting; and εε C ξε and C ηε Step 3) obtain C res using the LSC algorithm to obtain high-precision vertical deviation grid data based on waveform quality weighting. The step 3) specifically comprises: Assume two observation points on the unit sphere are p and q, the along-track vertical deviation at observation point p is ε p , and the corresponding azimuth is α p , the along-track vertical deviation at observation point q is ε q , and the corresponding azimuth is α q , the latitude and longitude of observation points p and q are respectively and wherein, the latitude of observation point p; λ p the longitude of observation point p; the latitude of observation point q; λ q the longitude of observation point q; According to the spherical trigonometric function relationship, the azimuth angle a between two points is determined pq : the first signal cross-covariance matrix C in the LSC algorithm ξε the second signal cross-covariance matrix C in the LSC algorithm ηε the signal covariance matrix C in the LSC algorithm εε respectively C ξε = C lq cosα pq cos(α q -α pq )-C mm sinα pq sin(α q -α pq ) (2) C ηε = C ll sinα pq cos(α q -α pq )+C mm cosα pq sin(α q -α pq ) (3) C εε = C ll cos(α p -α pq )cos(α q -α pq )+C mm sin(α p -α pq )sin(α q -α pq ) (4) where C ξε is the first signal cross-covariance matrix in the LSC algorithm; C ηε is the second signal cross-covariance matrix in the LSC algorithm; C εε is the signal covariance matrix in the LSC algorithm; C ll is the covariance function of the longitudinal components; C mm is the covariance function of the transversal components; The step 4) specifically comprises: According to the number of significant values Determining the significant rate of 1 Hz range measurements for observation point i The effective numerical rate ranges from 0 to 1; The effective number of 1 Hz ranging values of observation point i is comprehensively considered and the root mean square error σ i 2 The 1 Hz ranging value r i of observation point i is obtained as follows: wherein, r is the 1 Hz ranging value for observation point i i the variance of r; The SSH observation value h of the observation point i is calculated i the variance of is expressed as By the same token, the SSH observation value h of the observation point j is expressed as j the variance of the observation value h of the observation point j is expressed as wherein, is the effective number of bits for the 1 Hz ranging value of the observation point j; σ j 2 is the root mean square error for the 1 Hz ranging value of the observation point j; Supposing that the observation point i and the observation point j are two adjacent observation points, according to the error propagation law, the variance of the trajectory residual vertical deviation is wherein, is the variance of the along-track residual vertical deflection at observation point i, i.e. the standard deviation of the geoid slope at observation point i; and is the standard deviation of the SSH at observation point i and the standard deviation of the SSH at observation point j, respectively, in two adjacent observation points; ij is the distance between two adjacent observation points i and j; According to the above flow, the variance of the residual vertical deviation along the track of each observation point is estimated by using the quality parameters of the radar altimeter echo re-tracking, and substituted into the diagonal matrix to obtain the noise covariance matrix C weighted based on the waveform quality nn ; The step 5) specifically comprises: the noise covariance matrix C obtained according to step 4) nn , the first signal cross-covariance matrix C in the LSC algorithm obtained according to step 3) ξε , the second signal cross-covariance matrix C in the LSC algorithm ηε , and the signal covariance matrix C in the LSC algorithm εε , the along-track residual vertical deflection ε obtained according to step 2) is gridded based on waveform quality weighting by using the LSC algorithm to obtain high-precision vertical deflection grid data res : wherein, is the high-precision grid data of vertical deflection based on waveform quality weighting; ξ res is the residual vertical deflection along the track ε res is the gridded north-south component; η res is the residual vertical deflection along the track ε res is the gridded east-west component; High-precision vertical deflection grid data based on waveform quality weighting includes: residual vertical deflection ε along the track res Grided north-south component and east-west component.

2. The waveform-based retrace quality weighting griding method of claim 1, wherein, The step 1) specifically comprises: Reading the SGDR data of the radar altimeter to obtain 20Hz echo original waveform data; The weighted four-parameter maximum likelihood estimation algorithm is used to perform waveform retracking on the 20Hz echo raw waveform data, and the longitude, latitude and distance measurement value R at 1Hz and the corresponding waveform retracking quality parameters are derived by linear regression; wherein the waveform quality retracking parameters include: the number of effective values and the root mean square error σ i ; According to the following formula, the sea surface height, i.e. more accurate sea surface height SSH value, is obtained: SSH = H - (R + AR corr ) (11) where SSH is the sea surface height, i.e. the height of the sea surface to be measured by the radar altimeter; AR corr is the atmospheric path delay and geophysical errors; and H is the height of the altimetry satellite orbit to the reference ellipsoid.

3. The waveform-based retrace quality weighting griding method of claim 1, wherein, The step 2) specifically comprises: Removing the mean dynamic sea surface terrain MDT model from the sea surface height SSH obtained in the step 1) to obtain the geoid height N: N = SSH-MDT (12) The MDT is a dynamic sea surface terrain MDT model; removing the reference geoid height N from the geoid height N ref to obtain the residual geoid height ΔN: ΔN = N - N ref (13) Then the trajectory residual vertical deviation and the corresponding longitude and latitude are Where, ε res This indicates the residual vertical deviation along the trajectory; The residual vertical deviation ε along the trajectory res latitude; λ res The residual vertical deviation ε along the trajectory res longitude; d ij The distance between two adjacent observation points i and j along the trajectory; ΔN i the residual geoid height for observation point i of the two observation points adjacent along the trajectory; the along trajectory residual vertical deflection ε res the latitude of the observation point i of the two observation points adjacent along the trajectory; λ i the along trajectory residual vertical deflection ε res the longitude of the observation point i of the two observation points adjacent along the trajectory; ΔN j the residual geodetic height of the observation point j among the two observation points adjacent along the track; the along-track residual vertical deflection ε res the latitude of the observation point j among the two observation points adjacent along the track; λ j the along-track residual vertical deflection ε res the longitude of the observation point j among the two observation points adjacent along the track.

4. A waveform re-tracking quality weighted vertical deflection gridding system, characterized by, The system comprises a data preprocessing module, a residual vertical deviation extraction module and a vertical deviation gridding module. The data preprocessing module is configured to read SGDR data of the radar altimeter to obtain 20Hz echo original waveform data; perform waveform retracking on the 20Hz echo original waveform data in the SGDR data of the radar altimeter to obtain 1Hz precision, latitude and distance measurement values and corresponding waveform retracking quality parameters, and then obtain sea surface height; wherein the waveform quality retracking parameters include: number of effective values and root mean square error σ i . The residual vertical deviation extraction module is configured to calculate, according to the obtained longitude, latitude and sea surface height at 1 Hz, in combination with a dynamic sea surface topography MDT model and a reference geoid height, a residual vertical deviation ε along a track res and a corresponding longitude and latitude thereof; The vertical deviation gridding module is used to calculate the residual vertical deviation ε along the trajectory. res Using the latitude and longitude information, calculate the first signal cross-covariance matrix C in the LSC algorithm. ξε The second signal cross-covariance matrix C in the LSC algorithm ηε And the signal covariance matrix C in the LSC algorithm εε Based on the obtained waveform retracking quality parameters at sea level, the noise covariance matrix C, weighted by waveform quality, is calculated. nn Using the LSC algorithm, the track-side vertical deviation obtained by the residual vertical deviation extraction module is gridded to obtain high-precision vertical deviation grid data based on waveform quality weighting. The specific implementation process of the vertical deviation gridding module is: Assume two observation points on the unit sphere are p and q, the along-track vertical deviation at observation point p is ε p , and the corresponding azimuth is α p , the along-track vertical deviation at observation point q is ε q , and the corresponding azimuth is α q , the latitude and longitude of observation points p and q are respectively and wherein, is the latitude of observation point p; λ p is the longitude of observation point p; is the latitude of observation point q; λ q is the longitude of observation point q; According to the spherical trigonometric function relationship, the azimuth angle a between two points is determined pq : the first signal cross-covariance matrix C in the LSC algorithm ξε the second signal cross-covariance matrix C in the LSC algorithm ηε and the signal covariance matrix C in the LSC algorithm εε respectively C ξε = C ll cosα pq cos(α q -α pq )-C mm sinα pq sin(α q -α pq ) (18) C ηε = C ll sinα pq cos(α q -α pq )+C mm cosα pq sin(α q -α pq ) (19) C εε = C ll cos(α p -α pq )cos(α q -α pq )+C mm sin(α p -α pq )sin(α q -α pq ) (20) where C ξε is the first signal cross-covariance matrix in the LSC algorithm; C ηε is the second signal cross-covariance matrix in the LSC algorithm; C εε is the signal covariance matrix in the LSC algorithm; C ll is the covariance function of the longitudinal components; C mm is the covariance function of the transversal components; According to the number of significant values Determining the significant rate of 1 Hz range measurements for observation point i The effective numerical rate ranges from 0 to 1; Assuming that observation point i and observation point j are two adjacent observation points, the effective number of 1 Hz observation values of observation point i is comprehensively considered and the root mean square error σ i 2 , the variance of the 1 Hz ranging value r i of observation point i is: wherein, r is the 1 Hz ranging value for observation point i i the variance of r; The SSH observation value h of the observation point i is calculated i the variance of is expressed as the SSH observation value h of the observation point j j the variance of the observation value h is expressed as wherein, σj is the standard deviation of the 1 Hz observations at observation point j; and j 2 σj is the root mean square error of the 1 Hz observations at observation point j; and According to the error propagation law, the variance of the trajectory residual vertical deviation is wherein, is the variance of the along-track residual vertical deflection at observation point i, i.e. the standard deviation of the geoid slope at observation point i; and is the standard deviation of the SSH at observation point i and the standard deviation of the SSH at observation point j, respectively, in two adjacent observation points;d ij is the distance between two adjacent observation points i and j; According to the above flow, the variance of the residual vertical deviation along the track of each observation point is estimated by using the quality parameters of the radar altimeter echo re-tracking, and substituted into the diagonal matrix to obtain the noise covariance matrix C weighted based on the waveform quality nn ; the noise covariance matrix C obtained according to step 4) nn and the first signal cross-covariance matrix C in the LSC algorithm obtained according to step 3) ξε the second signal cross-covariance matrix C in the LSC algorithm ηε and the signal covariance matrix C in the LSC algorithm εε using the LSC algorithm, gridding the along-track residual vertical deflections ε obtained according to step 2) res to obtain high-precision vertical deflection grid data based on waveform quality weighting: wherein, is the high-precision grid data of vertical deflection based on waveform quality weighting; ξ res is the residual vertical deflection along the track ε res is the gridded north-south component; η res is the residual vertical deflection along the track ε res is the gridded east-west component; High-precision vertical deflection grid data based on waveform quality weighting includes: residual vertical deflection ε along the track res Grided north-south component and east-west component.

5. The waveform-based retrace quality-weighted vertical deflection gridding system of claim 4, wherein, The specific implementation process of the data preprocessing module is: Reading the SGDR data of the radar altimeter to obtain 20Hz echo original waveform data; The weighted four-parameter maximum likelihood estimation algorithm is used to perform waveform retracking on the 20Hz echo raw waveform data, and the longitude, latitude and distance measurement value R at 1Hz and the corresponding waveform retracking quality parameters are derived by linear regression; wherein the waveform quality retracking parameters include: the number of effective values and the root mean square error σ i ; According to the following formula, the sea surface height, i.e. more accurate sea surface height SSH value, is obtained: SSH = H - (R + AR corr ) (27) where SSH is the sea surface height, i.e. the height of the sea surface to be measured by the radar altimeter; AR corr is the atmospheric path delay and geophysical errors; H is the height of the altimetry satellite orbit to the reference ellipsoid.

6. The waveform-based retrace quality-weighted vertical deflection gridding system of claim 4, wherein, The specific implementation process of the residual vertical deviation extraction module is: Removing the dynamic sea surface terrain MDT model from the obtained sea surface height SSH to obtain the geoid height N: N = SSH-MDT (28) The MDT is a dynamic sea surface terrain MDT model; removing the reference geoid height N from the geoid height N ref to obtain the residual geoid height ΔN: ΔN = N - N ref (29) Then the trajectory residual vertical deviation and the corresponding longitude and latitude are where ε res represents the vertical deflection along the trajectory; is the latitude of the vertical deflection along the trajectory ε res represents the longitude of the vertical deflection along the trajectory ε res is the latitude of the vertical deflection along the trajectory ε res represents the longitude of the vertical deflection along the trajectory ε ij is the distance between two adjacent observation points i and j along the trajectory ΔN i the residual geoid height for observation point i of the two observation points adjacent along the trajectory; the along trajectory residual vertical deflection ε res the latitude of the observation point i of the two observation points adjacent along the trajectory; i the along trajectory residual vertical deflection ε res the longitude of the observation point i of the two observation points adjacent along the trajectory; ΔN j the residual geodetic height of the observation point j among the two observation points adjacent along the track; the along-track residual vertical deflection ε of the observation point j among the two observation points adjacent along the track res the latitude of the observation point j among the two observation points adjacent along the track; λ j the along-track residual vertical deflection ε of the observation point j among the two observation points adjacent along the track res the longitude of the observation point j among the two observation points adjacent along the track.

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