Method and system for inverting SLA based on airborne altimeter along-track data

Abstract

The invention discloses a method and system for inverting SLA based on airborne altimeter along-track data, and belongs to the technical field of ocean observation. The method comprises the following steps: firstly, carrying out statistical analysis on airborne altimeter along-track data; carrying out filtering processing on the along-rail data, and carrying out secondary interpolation fitting based on an airborne altimeter data interval; carrying out interpolation on an along-rail MSS according to an altimeter data sampling interval; processing the tide data along the rail in the same way to obtain the tide data; obtaining corresponding SLA data; the AVISO data and the SWOT data are processed in the same interpolation mode; and finally, realizing SLA data inversion based on the adaptive feature model. According to the method, inversion of high-resolution SLA data from airborne synthetic aperture radar altimeter aliasing signals is achieved, the precision reaches the centimeter level, the working mechanism of a new system load is verified, high-precision along-track SLA data is obtained, and a solid data and algorithm foundation is laid for subsequent satellite load development.

Description

Technical Field

[0001] The present invention belongs to the technical field of ocean observation, and in particular, relates to a method and system for inverting SLA based on along-track data of an airborne altimeter. Background Art

[0002] Sea Level Anomaly (SLA) is an important reference indicator of climate change. It is the change of instantaneous sea level relative to mean sea level (MSS). It is widely used in the study of ocean dynamic phenomena. Studying the inversion mechanism and changing law of SLA can provide a reference for human life and marine climate research.

[0003] Satellite or airborne altimeter data can infer various parameters, including sea surface height, sea surface wind speed, effective wave height, and topography of land, sea ice, and ice sheets. Satellite-borne along-track altimeter data can ensure the acquisition of high-precision along-track data due to the high stability of the satellite-borne platform and the high-precision calibration and inspection field built on it. At present, airborne along-track SLA inversion mostly tracks and processes altimeter echoes based on optimized or improved echo models to achieve along-track SLA data inversion. The tracking process is divided into two stages: altitude tracking and re-tracking. Re-tracking is mostly based on the least squares operator to obtain high-precision elevation parameters. There are upper limits on the estimation accuracy of elevation parameters and the noise suppression performance of the algorithm, which can easily cause overfitting of the elevation parameter estimation results and has limited adaptability to complex elevation changes. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for inverting SLA based on along-track data of an airborne altimeter, in view of the fact that along-track data of an airborne altimeter contains signal error terms such as airborne platform jitter and sea waves, so as to realize the inversion of high-resolution SLA data from along-track aliasing signals, so as to make up for the shortcomings of the prior art.

[0005] To achieve the above object, the present invention is achieved through the following technical solutions:

[0006] A method for inverting SLA based on along-track data of an airborne altimeter comprises the following steps:

[0007] S1: Obtain the measured along-track data of the airborne altimeter and auxiliary data AVISO and SWOT;

[0008] S2: Filtering the on-track data of the airborne altimeter: For the on-track data A1 of the airborne altimeter, firstly statistically analyze the sea wave signal and the oscillation frequency of the flight platform; firstly use the low-pass filtering method to process the A1 data to obtain the A2 data; perform spectrum analysis on the flight altitude change data during the flight, and perform high-pass filtering on the A2 data to obtain A3;

[0009] S3: Perform secondary interpolation fitting on MSS, tide, AVISO, and SWOT based on the airborne altimeter data interval: Interpolate the MSS along the track according to the altimeter data A3 sampling interval, and obtain the corresponding MSS data B1; process the tidal data along the track in the same way to obtain tidal data C1; obtain the corresponding SLA data D1 by A3-B1-C1; process the AVISO and SWOT data in the same interpolation way to obtain E1 and F1;

[0010] S4: Implement SLA data inversion based on adaptive feature model: Build a deep learning framework based on the obtained SLA data D1; D1 is used as feature data, i.e., the input variable X of the model; E1 and F1 are used as label data Y(E) and Y(F), i.e., the expected output result variables of the model; train the labeled data so that the neural network can learn the mapping relationship from input to output, and optimize the model based on the existing label data so that it can output SLA(E) and SLA(F) more accurately;

[0011] S5: Invert SLA based on the optimized adaptive feature model.

[0012] Furthermore, in S2, the sampling filter threshold is calculated based on the following:

[0013] According to the sampling theorem, the sampling frequency must be greater than twice the maximum frequency of the signal itself in order to restore the signal; the frequency of the airborne altimeter observation data is regarded as the sampling of SSH, and the sampling frequency is 1-2hz;

[0014] Low-pass filtering, assuming the sampling frequency is 1000 Hz, the maximum frequency of the signal itself is 500 Hz, to filter out the frequency part above 400 Hz, that is, the cut-off frequency is 400 Hz, then WN = 2*400 / 1000 = 0.8;

[0015] High-pass filtering, assuming the sampling frequency is 1000 Hz, the maximum frequency of the signal itself is 500 Hz, and the frequency part below 100 Hz needs to be filtered out, that is, the cut-off frequency is 100 Hz, then WN=2*100 / 1000=0.2.

[0016] Furthermore, the adaptive feature model is composed of 1 LSTM (256) containing 256 neurons, 2 LSTM (128) containing 128 neurons, 2 fully connected layers containing 64 neurons, and 1 fully connected layer containing 1 neuron.

[0017] Furthermore, the specific process of inverting high-precision along-track SLA data using the adaptive feature model is as follows:

[0018] D1 is the feature data, that is, the input X variable of the model; E1 and F1 are the label data Y(E) , Y (F) , which is the output variable expected by the model, constructs training samples θ based on X and Y:

[0019] X train ,X test =split(X) (1)

[0020] Y train ,Y test = split(Y) (2)

[0021] θ=(X train ,Y train ) (3)

[0022] Training θ enables the neural network to learn the mapping relationship from input to output. The tanh function maps the input value to a state value ranging from -1 to 1:

[0023]

[0024] The input gate mainly includes two activation functions, σ and tanh, which are used to update the state of the memory unit. Its structure can be expressed as:

[0025] i t =σ(W i ·[h t-1 ,x t ]+b i ) (5)

[0026]

[0027] In the formula, the acceptance weight i of the information is determined by the σ function t , calculate the candidate state through the tanh function

[0028] The activation value f of the forget gate t From the current input x t and the previous hidden state h t-1 Calculated through a fully connected layer with a σ activation function:

[0029] f t =σ(W f ·[h t-1 ,x t ]+b f ) (7)

[0030] Where W and b represent the weight matrix and bias respectively; f t is a vector ranging from 0 to 1, where the value in the vector represents the cell state C t-1Whether the information in is retained; a value of 0 means discarding, and a value of 1 means retaining;

[0031] Combine the results of the forget gate and the input gate to update the cell state C t :

[0032]

[0033] In the formula, f t *C t-1 Decided C t-1 The information forgotten in Decided to The information in is added to the new memory cell C t middle;

[0034] The output gate is mainly obtained by combining short-term memory with the current input information. t and h of long-term memory output t It consists of two parts, and its expression is:

[0035] o t =σ(W o ·[h t-1 ,x t ]+b o ) (9)

[0036] h t =o t *tanh(C t ) (10)

[0037] In the formula, through the hidden layer o t , and get the final hidden state h t The value of the expected output SLA (E) 、SLA (F) .

[0038] A system for inverting SLA based on along-track data of an airborne altimeter, comprising:

[0039] Data acquisition module to obtain the measured along-track data of the airborne altimeter and auxiliary data AVISO and SWOT;

[0040] The data filtering and processing module performs filtering on the along-track data of the airborne altimeter;

[0041] The data secondary interpolation fitting module performs secondary interpolation fitting on MSS, tide, AVISO and SWOT based on the airborne altimeter data interval;

[0042] Model optimization module, used to optimize the adaptive feature model;

[0043] Inversion module, which realizes SLA data inversion based on adaptive feature model.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The present invention proposes a method based on the combination of bandpass filtering and adaptive feature AI analysis to achieve the inversion of high-resolution SLA data from the aliased signal of the airborne synthetic aperture radar altimeter with an accuracy of centimeters. This verifies the working mechanism of the new system payload and obtains high-precision along-track SLA data, laying a solid data and algorithm foundation for the development of subsequent satellite payloads. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a schematic diagram of the UAV crossing from A (cold vortex) to B (warm vortex) on April 12 in Example 1.

[0047] Figure 2 This is a schematic diagram of the transit SWOT track on April 22 in Example 1.

[0048] Figure 3 It is a mapping diagram from input to output.

[0049] Figure 4 It is a processing flow chart of the adaptive feature analysis model.

[0050] Figure 5 This is the basic architecture diagram of the adaptive feature analysis model.

[0051] Figure 6 This is a schematic diagram showing that the changing trend of the SLA curve is opposite to the trend from cold vortex to warm vortex.

[0052] Figure 7 This is a schematic diagram showing that the change trend of the SLA signal is the same as that of the change from a cold vortex to a warm vortex.

[0053] Figure 8 It is a schematic diagram of the SLA results along the track obtained by inversion calculation of SLA.

[0054] Fig. 9 This is the result of SLA inversion performed by the airborne altimeter in the part that overlaps with the SWOT trajectory off the coast of Sanya. DETAILED DESCRIPTION

[0055] The technical solution of the present invention is further described below in conjunction with the embodiments and drawings.

[0056] Example 1

[0057] A method for inverting SLA based on along-track data of an airborne altimeter comprises the following steps:

[0058] 1. Dataset

[0059] 1) Airborne altimeter measured along-track data

[0060] In April 2023, the working mechanism verification and system mechanism verification of the new system payload were carried out in the northern waters of the South China Sea, and the relevant airborne altimeter along-track observation data were accumulated simultaneously, such as Figure 1 shown.

[0061] 2) Auxiliary data

[0062] The AVISO data E1 at 0.25 degrees along the track and the SWOT transit data F1 during the test are as follows: Figure 2 shown.

[0063] 2. Data processing logic

[0064] 1) Filtering of the onboard altimeter along-track data

[0065] For the on-track data A1 of the airborne altimeter, firstly, statistical analysis is performed on the sea wave signals (mainly wind waves and swells) and the oscillation frequency of the flight platform; the on-track data is first processed by low-pass filtering to obtain A2 data; the flight altitude change data during the flight is subjected to spectrum analysis, and the A2 data is subjected to high-pass filtering to obtain A3;

[0066] 2) Perform secondary interpolation fitting on MSS, tide, AVISO, and SWOT based on the airborne altimeter data interval

[0067] According to the sampling interval of altimeter data A3, the MSS along the track is interpolated and the corresponding MSS data B1 is obtained; the tidal data along the track is processed in the same way to obtain tidal data C1; A3-B1-C1 is used to obtain the corresponding SLA data D1; the AVISO and SWOT data are processed in the same interpolation way to obtain E1 and F1;

[0068] 3) Realize SLA data inversion based on adaptive feature AI analysis

[0069] Based on the obtained SLA data D1, a deep learning framework is built; D1 is used as feature data, i.e., the input variable X of the model; E1 and F1 are used as label data Y(E) and Y(F), i.e., the expected output variables of the model. The labeled data is trained so that the neural network can learn the mapping relationship from input to output, and optimize the model based on the existing label data so that it can output SLA(E) and SLA(F) more accurately.

[0070] Data processing flow chart, such as Figure 4 As shown, the following steps are included:

[0071] 1) Filter the on-track observation data of the airborne altimeter to remove the influence of sea waves (wind waves, swells) and flight platform vibration to obtain SSH1;

[0072] 2) Perform frequency analysis on the flight altitude change signal of the data SSH1, and remove the flight altitude change factor from SSH1 by high-pass filtering to obtain SSH2;

[0073] 3) Based on the sampling interval of SSH2, perform secondary interpolation fitting on the MSS, tide, and AVISO data along the track; subtract the MSS and tide data from SSH2 to obtain the SLA data;

[0074] 4) Input SLA data and AVISO into the adaptive feature analysis model to output high-precision SLA along-track data.

[0075] 3. Compare and analyze the inverted SLA(E) and SLA(F) with AVISO and SWOT, and calculate the RMSE value.

[0076] The data was compared and analyzed with the 0.25 degree AVISO along the track using the adaptive feature analysis method to obtain the corresponding SLA (E) data, with an RMSE of 0.44 cm;

[0077] The data was also compared and analyzed with the SWOT data along the track using the adaptive feature analysis method to obtain the corresponding SLA (F) data, with an RMSE of 1.12 cm.

[0078] in,

[0079] 1. Analyze and process the on-track data of the airborne altimeter based on filtering

[0080] The frequency of sea waves (wind waves, swells) and aircraft platform vibration in the South China Sea sea trial area was analyzed, and the statistical period of sea waves in April was 5s-9s, 0.1-0.2HZ;

[0081] The oscillation frequency of the aircraft platform is above 20HZ;

[0082] By using the principle of low-pass filtering, it is necessary to filter out signal data above 0.2HZ, and the low-pass filter threshold is 2*0.2hz / 1=0.4hz;

[0083] There are signals of ascent and descent during the flight. After frequency analysis, the frequency of the aircraft along the track on April 12 was 0.08 Hz, so the high-pass filter threshold of the aircraft was 0.16 Hz (2*0.08 Hz / 1=0.16 Hz).

[0084] Table 1 Processing table of airborne altimeter along-track data

[0085]

[0086]

[0087] The sampling filter threshold is calculated based on the following:

[0088] According to the sampling theorem, the sampling frequency must be greater than twice the maximum frequency of the signal itself in order to restore the signal.

[0089] Therefore, the frequency of the airborne altimeter observation data can be regarded as the sampling of SSH, and the sampling frequency is 1-2 Hz. In this paper, 1 Hz is used as the basis.

[0090] Low-pass filtering, assuming the sampling frequency is 1000 Hz, the maximum frequency of the signal itself is 500 Hz, to filter out the frequency part above 400 Hz, that is, the cut-off frequency is 400 Hz, then WN = 2*400 / 1000 = 0.8;

[0091] High-pass filtering, assuming the sampling frequency is 1000 Hz, the maximum frequency of the signal itself is 500 Hz, to filter out the frequency part below 100 Hz, that is, the cut-off frequency is 100 Hz, then WN = 2*100 / 1000 = 0.2.

[0092] 2. Invert high-precision along-track SLA data based on adaptive feature model analysis

[0093] The adaptive feature analysis model is as follows Figure 5 As shown in the figure, the model consists of 1 LSTM(256) containing 256 neurons, 2 LSTM(128) containing 128 neurons, 2 fully connected layers containing 64 neurons, and 1 fully connected layer containing 1 neuron.

[0094] Table 2 Parameters of the adaptive feature analysis model

[0095]

[0096] Based on AVISO data, SWOT data and filtered SLA data, high-precision along-track SLA data are inverted through an adaptive feature model with centimeter-level accuracy. Analysis of the inverted data shows that airborne observations are highly consistent with satellite observations, and airborne altimeter observations can obtain more small-scale change signals.

[0097] D1 is the feature data, that is, the input X variable of the model; E1 and F1 are the label data Y (E) , Y (F) , which is the output variable expected by the model, constructs training samples θ based on X and Y:

[0098] Xtrain ,X test =split(X) (1)

[0099] Y train ,Y test = split(Y) (2)

[0100] θ=(X train ,Y train ) (3)

[0101] Training θ enables the neural network to learn the mapping relationship from input to output. The tanh function maps the input value to a state value ranging from -1 to 1:

[0102]

[0103] The input gate mainly includes two activation functions, σ and tanh, which are used to update the state of the memory unit. Its structure can be expressed as:

[0104] i t =σ(W i ·[h t-1 ,x t ]+b i ) (5)

[0105]

[0106] In the formula, the acceptance weight i of the information is determined by the σ function t , calculate the candidate state through the tanh function The activation value f of the forget gate t From the current input x t and the previous hidden state h t-1 Calculated through a fully connected layer with a σ activation function:

[0107] f t =σ(W f ·[h t-1 ,x t ]+b f ) (7)

[0108] Where W and b represent the weight matrix and bias respectively. t is a vector ranging from 0 to 1, where the value in the vector represents the cell state C t-1 Whether the information in the packet is retained. A value of 0 means discarding, and a value of 1 means retaining.

[0109] Combine the results of the forget gate and the input gate to update the cell state C t :

[0110]

[0111] In the formula, f t *C t-1 Decided C t-1 The information forgotten in Decided to The information in is added to the new memory cell C t middle.

[0112] The output gate is mainly obtained by combining short-term memory with the current input information. t and h of long-term memory output t It consists of two parts, such as Figure 3 As shown, its expression is:

[0113] o t =σ(W o ·[h t-1 ,x t ]+b o ) (9)

[0114] h t =o t *tanh(C t ) (10)

[0115] In the formula, through the hidden layer o t , and get the final hidden state h t The value of the expected output SLA (E) 、SLA (F) .

[0116] The data processing results are as follows:

[0117] 1) When the sea surface height anomaly (SLA) is obtained by subtracting the mean sea level (MSS) from the sea surface height (SSH) parsed from the airborne altimeter signal (where the sea surface height data has been low-pass filtered to remove the waves and flight platform oscillation signals), the change trend of the SLA curve is opposite to the trend from cold vortex to warm vortex, such as Figure 6 shown.

[0118] 2) After high-pass filtering the SSH in "result step 1)" to remove the aircraft platform height change signal it contains, SLA is calculated again. At this time, the change trend of SLA signal is the same as the change trend from cold vortex to warm vortex, but the accuracy difference is huge, SLA (meter level), AVISO (centimeter level), such as Figure 7 shown.

[0119] 3) Relying on the adaptive feature AI method proposed in the present invention, the SLA calculated in "result step 2)" is inverted to obtain the along-track SLA data, such as Figure 8 shown.

[0120] The SLA inversion results along the aircraft track on April 12, with 0.25 degree AVISO, have an RMSE of 0.44cm, achieving centimeter-level high-precision data inversion.

[0121] Based on the same processing logic, the SLA inversion is performed on the part of the airborne altimeter that overlaps with the SWOT trajectory in the waters off Sanya on April 22, as shown in the following figure: Fig. 9 shown.

[0122] On April 22, the SLA inversion of the transit SWOT data and the airborne altimeter data had an RMSE of 1.12 cm, and the airborne altimeter data could obtain more small-scale change signals.

[0123] The above results illustrate that the method and system provided by the present invention can achieve high-precision inversion of high-resolution SLA data from the aliased signal of an airborne synthetic aperture radar altimeter.

[0124] Finally, although this specification is described according to implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A method for inverting SLA based on along-track data of an airborne altimeter, characterized in that: The method comprises the following steps: S1: Obtain the measured along-track data of the airborne altimeter and auxiliary data AVISO and SWOT; S2: Filtering the on-track data of the airborne altimeter: For the on-track data A1 of the airborne altimeter, firstly statistically analyze the sea wave signal and the oscillation frequency of the flight platform; firstly use the low-pass filtering method to process the A1 data to obtain the A2 data; perform spectrum analysis on the flight altitude change data during the flight, and perform high-pass filtering on the A2 data to obtain A3; S3: Perform secondary interpolation fitting on MSS, tide, AVISO, and SWOT based on the airborne altimeter data interval: Interpolate the MSS along the track according to the altimeter data A3 sampling interval, and obtain the corresponding MSS data B1; process the tidal data along the track in the same way to obtain tidal data C1; obtain the corresponding SLA data D1 by A3-B1-C1; process the AVISO and SWOT data in the same interpolation way to obtain E1 and F1; S4: Implement SLA data inversion based on adaptive feature model: Build a deep learning framework based on the obtained SLA data D1; D1 is used as feature data, i.e., the input variable X of the model; E1 and F1 are used as label data Y(E) and Y(F), i.e., the expected output result variables of the model; train the labeled data so that the neural network can learn the mapping relationship from input to output, and optimize the model based on the existing label data so that it can output SLA(E) and SLA(F) more accurately; S5: Invert SLA based on the optimized adaptive feature model.

2. The method for inverting SLA based on along-track data of an airborne altimeter according to claim 1, characterized in that: In S2, the sampling filter threshold is calculated based on the following: According to the sampling theorem, the sampling frequency must be greater than twice the maximum frequency of the signal itself in order to restore the signal; the frequency of the airborne altimeter observation data is regarded as the sampling of SSH, and the sampling frequency is 1-2hz; Low-pass filtering, assuming the sampling frequency is 1000 Hz, the maximum frequency of the signal itself is 500 Hz, to filter out the frequency part above 400 Hz, that is, the cut-off frequency is 400 Hz, then WN = 2*400 / 1000 = 0.8; High-pass filtering, assuming the sampling frequency is 1000 Hz, the maximum frequency of the signal itself is 500 Hz, and the frequency part below 100 Hz needs to be filtered out, that is, the cut-off frequency is 100 Hz, then WN=2*100 / 1000=0.

2.

3. The method for inverting SLA based on along-track data of an airborne altimeter according to claim 1, characterized in that: The adaptive feature model consists of 1 LSTM containing 256 neurons, 2 LSTM containing 128 neurons, 2 fully connected layers containing 64 neurons, and 1 fully connected layer containing 1 neuron.

4. The method for inverting SLA based on along-track data of an airborne altimeter according to claim 1, characterized in that: The specific process of inverting high-precision along-track SLA data using the adaptive feature model is as follows: D1 is the feature data, that is, the input X variable of the model; E1 and F1 are the label data Y (E) , Y (F) , which is the output variable expected by the model, constructs training samples θ based on X and Y: X train ,X test =split(X) (1) AND train ,AND test =split(Y) (2) θ=(X train ,Y train ) (3) Training θ enables the neural network to learn the mapping relationship from input to output. The tanh function maps the input value to a state value ranging from -1 to 1: The input gate includes two activation functions, σ and tanh, which are used to update the state of the memory unit. Its structure is expressed as: i t =σ(W i ·[h t-1 ,x t ]+b i ) (5) In the formula, the acceptance weight i of the information is determined by the σ function t , calculate the candidate state through the tanh function The activation value f of the forget gate t From the current input x t and the previous hidden state h t-1 Calculated through a fully connected layer with a σ activation function: f t =σ(W f ·[h t-1 ,x t ]+b f ) (7) Where W and b represent the weight matrix and bias respectively; f t is a vector ranging from 0 to 1, where the value in the vector represents the cell state C t-1 Whether the information in is retained; a value of 0 means discarding, and a value of 1 means retaining; Combine the results of the forget gate and the input gate to update the cell state C t : In the formula, f t *C t-1 Decided C t-1 The information forgotten in Decided to The information in is added to the new memory cell C t middle; The output gate is mainly obtained by combining short-term memory with the current input information. t and h of long-term memory output t It consists of two parts, and its expression is: the t =σ(W o ·[h t-1 ,x t ]+b o ) (9) h t =o t *tanh(C t ) (10) In the formula, through the hidden layer o t , and get the final hidden state h t The value of SLA is the expected output result. (E) 、SLA (F) .

5. A system for inverting SLA based on along-track data of an airborne altimeter, characterized in that: The system includes: Data acquisition module to obtain the measured along-track data of the airborne altimeter and auxiliary data AVISO and SWOT; The data filtering and processing module performs filtering on the along-track data of the airborne altimeter; The data secondary interpolation fitting module performs secondary interpolation fitting on MSS, tide, AVISO and SWOT based on the airborne altimeter data interval; Model optimization module, used to optimize the adaptive feature model; Inversion module, which realizes SLA data inversion based on adaptive feature model.

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