阅读说明：本技术 一种基于多模多频gnss接收机的潮位监测方法及系统 (Sea level monitoring method and system based on multimode multi-frequency GNSS receiver ) 是由 王笑蕾 何秀凤 车文越 宋敏峰 李翔 郭思宇 季洪壮 郑雨沙 吕如诗 于 2021-07-29 设计创作，主要内容包括：本发明公开了一种基于多模多频GNSS接收机的潮位监测方法及系统,涉及GNSS遥感、以及海洋监测技术领域,针对多模多频GNSS潮位反演中的误差特性,本发明建立了一种潮位反演的模型；并利用滑动窗口,在每窗口内输出一个潮位反演值。建立的反演模型考虑了目前已知的误差源,选取的解算策略能够自适应避免粗差,对于潮位变化较大的情况及水面较为静止的情况均具有良好的适用性。利用本发明提出的方法,基于多模多频GNSS接收机,可以实现高精度、等间隔、全自动、小成本、长期连续、有稳定框架支持的潮位监测性能,对潮位监测技术革新及技术补充具有重要意义。(The invention discloses a sea level monitoring method and a sea level monitoring system based on a multimode multi-frequency GNSS receiver, which relate to the technical field of GNSS remote sensing and ocean monitoring, and establish a sea level inversion model aiming at the error characteristics in the sea level inversion of the multimode multi-frequency GNSS; and outputting one tidal level inversion value in each window by using a sliding window. The established inversion model takes the known error source into consideration, the selected resolving strategy can self-adaptively avoid gross errors, and the method has good applicability to the conditions of large change of the sea level and static water surface. By utilizing the method provided by the invention, based on the multi-mode multi-frequency GNSS receiver, the tide level monitoring performance which is high in precision, equal in interval, full-automatic, low in cost, long-term and continuous and has a stable framework support can be realized, and the method has important significance for the innovation of the tide level monitoring technology and the technological supplement.)

1. A tide level monitoring method based on a multi-mode multi-frequency GNSS receiver is based on the GNSS receiver and realizes the tide level monitoring operation of a specified signal reflection area in a target time range, and is characterized in that when the GNSS receiver receives a reflection signal sequence from the specified signal reflection area in the target time range, the following steps are executed:

step 1, obtaining the signal-to-noise ratio of each reflection signal in the reflection signal sequence, generating the signal-to-noise ratio sequence of the reflection signal in a target area, obtaining each interval corresponding to the signal-to-noise ratio sequence according to the oscillation characteristics of the signal-to-noise ratio sequence, wherein the intervals comprise an altitude angle interval and an azimuth angle interval, and then entering step 2;

step 2, carrying out spectrum analysis on the local signal-to-noise ratio sequences in each interval corresponding to the signal-to-noise ratio sequences respectively to obtain a spectrum image corresponding to the local signal-to-noise ratio sequences, further obtaining frequencies corresponding to energy peak values of each reflection signal in the spectrum image respectively, carrying out inversion algorithm on the frequencies to obtain distance values from the antenna to the sea surface in the corresponding interval, taking the distance values as effective heights of each interval, carrying out inversion on the effective heights to obtain inversion values of the effective heights in each interval, and then entering step 3;

step 3, aiming at each interval, preprocessing the reflection signals in the frequency spectrum image corresponding to the corresponding interval by using the frequency corresponding to the energy peak value of the reflection signals in the interval obtained in the step 2 and the inversion value of the effective height, identifying invalid peak values in the reflection signals as pseudo data, deleting the invalid peak values, finishing preprocessing the reflection signals in the corresponding interval, updating each reflection signal corresponding to the corresponding interval, and then entering the step 4;

step 4, aiming at the reflection signal sequence in the reflection area of the received appointed signal in the target time range, selecting a sliding window with a preset time length in the target time range according to the effective height of each interval obtained in the step 3, obtaining the reflection signal subsequence corresponding to each sliding window, obtaining an effective height inversion fusion solution corresponding to each sliding window based on the sliding window, establishing an inversion model by taking each reflection signal in the sliding window as input and taking the effective height bit inversion fusion solution corresponding to the reflection signal subsequence in each sliding window as output, and then entering the step 5;

and 5, obtaining a tide level inversion value according to the effective height inversion fusion value obtained in the step 4, converting the tide level inversion value from a WGS84 standard into a tide level standard, obtaining a tide level standard corresponding to each reflection signal subsequence, namely obtaining a tide level standard sequence result under each sliding window in the target area, and finally obtaining a tide level standard value with equal intervals.

2. The method as claimed in claim 1, wherein in step 2, the local snr sequence is subjected to spectrum analysis by a Lomb-score spectrum analysis method to obtain a frequency corresponding to the local snr sequence, and further according to the following formula:

h＝λf/2

and obtaining the effective height of the corresponding interval of each local signal-to-noise ratio sequence, wherein h is the effective height containing errors, f is the frequency of the local signal-to-noise ratio sequence, and lambda is the frequency wavelength.

3. The method as claimed in claim 2, wherein the preprocessing of the reflected signals in the spectrum image in step 3 comprises the following steps:

step 3-1, calculating a ratio of peak energy to background energy in the signal-to-noise sequence spectrum image, when the ratio is larger than a preset threshold value, considering the peak value to be significant, keeping an inversion point corresponding to the peak value, when the ratio is smaller than the preset threshold value, considering the peak value to be insignificant, and deleting the peak value;

and 3-2, inverting the spectrum peak value, screening the spectrum peak value inversion result, identifying pseudo data which are not in a preset tide level interval, and deleting the spectrum peak value corresponding to the pseudo data.

4. The method as claimed in claim 1, wherein the step 4 of building an inversion model comprises the steps of:

step 4-1, dividing the effective height inversion values in each sliding window in the specified signal reflection area in different time windows by using the sliding windows to obtain corresponding reflection signal subsequences;

step 4-2, based on the sliding windows, combining the effective height inversion values and the inversion errors corresponding to the sliding windows, and establishing an inversion model asWherein L is an effective height inversion sequence containing errors, H is a fusion solution of the effective height inversion values,for sea surface dynamic variation error, Δ_TThe atmospheric refraction error is, and delta is other inversion errors;

and 4-3, performing least square solution on the inversion model to obtain effective height inversion fusion solutions corresponding to all the sliding windows.

5. A system for monitoring tide levels based on a multi-mode multi-frequency GNSS receiver is characterized by comprising:

one or more processors;

a memory storing instructions that are executable, which when executed by the one or more processors, cause the one or more processors to:

when the GNSS receiver receives the reflected signal from the signal reflection area, the following steps are executed:

step 1, obtaining the signal-to-noise ratio of each reflection signal in the reflection signal sequence, generating the signal-to-noise ratio sequence of the reflection signal in a target area, obtaining each interval corresponding to the signal-to-noise ratio sequence according to the oscillation characteristics of the signal-to-noise ratio sequence, wherein the intervals comprise an altitude angle interval and an azimuth angle interval, and then entering step 2;

step 2, carrying out spectrum analysis on the local signal-to-noise ratio sequences in each interval corresponding to the signal-to-noise ratio sequences respectively to obtain a spectrum image corresponding to the local signal-to-noise ratio sequences, further obtaining frequencies corresponding to energy peak values of each reflection signal in the spectrum image respectively, carrying out inversion algorithm on the frequencies to obtain distance values from the antenna to the sea surface in the corresponding interval, taking the distance values as effective heights of each interval, carrying out inversion on the effective heights to obtain inversion values of the effective heights in each interval, and then entering step 3;

step 3, aiming at each interval, preprocessing the reflection signals in the frequency spectrum image corresponding to the corresponding interval by using the frequency corresponding to the energy peak value of the reflection signals in the interval obtained in the step 2 and the inversion value of the effective height, identifying invalid peak values in the reflection signals as pseudo data, deleting the invalid peak values, finishing preprocessing the reflection signals in the corresponding interval, updating each reflection signal corresponding to the corresponding interval, and then entering the step 4;

step 4, aiming at the reflection signal sequence in the reflection area of the received appointed signal in the target time range, selecting a sliding window with a preset time length in the target time range according to the effective height of each interval obtained in the step 3, obtaining the reflection signal subsequence corresponding to each sliding window, obtaining an effective height inversion fusion solution corresponding to each sliding window based on the sliding window, establishing an inversion model by taking each reflection signal in the sliding window as input and taking the effective height bit inversion fusion solution corresponding to the reflection signal subsequence in each sliding window as output, and then entering the step 5;

and 5, obtaining a tide level inversion value according to the effective height inversion fusion value obtained in the step 4, converting the tide level inversion value from a WGS84 standard into a tide level standard, obtaining a tide level standard corresponding to each reflection signal subsequence, namely obtaining a tide level standard sequence result under each sliding window in the target area, and finally obtaining a tide level standard value with equal intervals.

6. A computer-readable medium storing software, the software comprising instructions executable by one or more computers which, when executed by the one or more computers, perform the operations of the tidal level monitoring method of any of claims 1-4.

Technical Field

The invention relates to the technical field of GNSS remote sensing and ocean monitoring, in particular to a tide level monitoring method and a tide level monitoring system based on a multimode multi-frequency GNSS receiver.

Background

The water level in the ocean is called the tidal level, which, under the influence of tides and the like, exhibits periodic fluctuations. Accurate monitoring of the tide level is an important topic for ocean monitoring, ocean circulation analysis and climate analysis. Particularly, the current global warming phenomenon causes glaciers to melt and seawater to thermally expand, so that the sea level rises, and the tide level monitoring is more necessary. Meanwhile, the tide level monitoring is very critical to military industries such as ship industry, maritime combat, ocean and coastal engineering and the like. Tidal level monitoring is also important for the uniformity and maintenance of global elevation references.

The traditional tidal level monitoring means are as follows: water gauge measurement, float-type tide gauge, induced pressure bell-type tide gauge, acoustic tide gauge and the like. However, each type of monitoring means has disadvantages: the water gauge measurement consumes a large amount of manpower resources; float and pressure-inducing bell-type tide gauges need to establish a special tide-testing well; the acoustic tide meter has low precision and can only be used offshore. Meanwhile, the tide gauge data recorded by the tide gauge station includes the influence of the earth crust movement, and a collocated GNSS station is generally required to eliminate such errors so as to obtain the absolute change of the tide level. Therefore, the traditional tidal level monitoring technology has the defects of high manufacturing cost or non-automation, and additional technology is needed to realize the standard unification. Another commonly used means of tidal level monitoring is the satellite altimetry. Satellite altimetry methods can monitor absolute changes in tide levels over a wide range with high accuracy, but with lower accuracy in offshore areas; meanwhile, the observation period is determined by the motion orbit, and the tidal level monitoring resolution is low. With the continuous development and improvement of GNSS, a GNSS multipath remote sensing technology is found to be used for tide level monitoring, and tide level monitoring can be completed only based on a measurement type receiver according to multipath characteristics in signal-to-noise ratio data. By utilizing the emerging GNSS multipath remote sensing technology, full-automatic, low-cost and long-term continuous tide level monitoring can be realized, and the monitoring result is automatically fixed under a stable frame; the technology can overcome the defects of the traditional monitoring technology and effectively supplement tide level monitoring data.

However, the GNSS tide level monitoring technology has two bottlenecks of accuracy and resolution, which limits the practical application range of the technology and hinders the industrialization development process thereof. And because the multi-mode multi-frequency GNSS receiver can capture multi-satellite multi-orbit data, the satellite track number can be increased, the inversion frequency is increased, and the problem of time resolution is solved. However, the introduction of multi-mode multi-frequency GNSS data brings great benefit, and also brings more errors and pseudo data, which causes data contradiction, and a corresponding method is required to reconcile the data contradiction and improve the tide level monitoring accuracy.

Disclosure of Invention

The invention aims to provide a tide level monitoring method based on a multi-mode multi-frequency GNSS receiver, which aims to solve the problems in the prior art.

In order to achieve the purpose, the invention provides the following technical scheme:

a tide level monitoring method based on a multi-mode multi-frequency GNSS receiver is based on the GNSS receiver, realizes the tide level monitoring operation of a specified signal reflection area in a target area within a target time range, and executes the following steps when the GNSS receiver receives a reflection signal sequence from the specified signal reflection area in the target area:

step 1, obtaining the signal-to-noise ratio of each reflection signal in the reflection signal sequence, generating the signal-to-noise ratio sequence of the reflection signal in a target area, obtaining each interval corresponding to the signal-to-noise ratio sequence according to the oscillation characteristics of the signal-to-noise ratio sequence, wherein the intervals comprise an altitude angle interval and an azimuth angle interval, and then entering step 2;

step 2, carrying out spectrum analysis on the local signal-to-noise ratio sequences in each interval corresponding to the signal-to-noise ratio sequences respectively to obtain a spectrum image corresponding to the local signal-to-noise ratio sequences, further obtaining frequencies corresponding to energy peak values of each reflection signal in the spectrum image respectively, carrying out inversion algorithm on the frequencies to obtain distance values from the antenna to the sea surface in the corresponding interval, taking the distance values as effective heights of each interval, carrying out inversion on the effective heights to obtain inversion values of the effective heights in each interval, and then entering step 3;

step 3, aiming at each interval, preprocessing the reflection signals in the frequency spectrum image corresponding to the corresponding interval by using the frequency corresponding to the energy peak value of the reflection signals in the interval obtained in the step 2 and the inversion value of the effective height, identifying invalid peak values in the reflection signals as pseudo data, deleting the invalid peak values, finishing preprocessing the reflection signals in the corresponding interval, updating each reflection signal corresponding to the corresponding interval, and then entering the step 4;

step 4, aiming at the reflection signal sequence in the reflection area of the received appointed signal in the target time range, selecting a sliding window with a preset time length in the target time range according to the effective height of each interval obtained in the step 3, obtaining the reflection signal subsequence corresponding to each sliding window, obtaining an effective height inversion fusion solution corresponding to each sliding window based on the sliding window, establishing an inversion model by taking each reflection signal in the sliding window as input and taking the effective height bit inversion fusion solution corresponding to the reflection signal subsequence in each sliding window as output, and then entering the step 5;

and 5, obtaining a tide level inversion value according to the effective height inversion fusion value obtained in the step 4, converting the tide level inversion value from a WGS84 standard into a tide level standard, obtaining a tide level standard corresponding to each reflection signal subsequence, namely obtaining a tide level standard sequence result under each sliding window in the target area, and finally obtaining a tide level standard value with equal intervals.

Further, in the step 2, performing spectrum analysis on the local snr sequence by using a Lomb-Scargle spectrum analysis method to obtain a frequency corresponding to the local snr sequence, and further according to the following formula:

h＝λf/2

and obtaining the effective height of the corresponding interval of each local signal-to-noise ratio sequence, wherein h is the effective height containing errors, f is the frequency of the local signal-to-noise ratio sequence, and lambda is the frequency wavelength.

Further, the preprocessing of the reflected signal in the spectrum image in the step 3 includes the following steps:

step 3-1, calculating a ratio of peak energy to background energy in the signal-to-noise sequence spectrum image, when the ratio is larger than a preset threshold value, considering the peak value to be significant, keeping an inversion point corresponding to the peak value, when the ratio is smaller than the preset threshold value, considering the peak value to be insignificant, and deleting the peak value;

and 3-2, inverting the spectrum peak value, screening the spectrum peak value inversion result, identifying pseudo data which are not in a preset tide level interval, and deleting the spectrum peak value corresponding to the pseudo data.

Further, the establishing of the inverse model in the step 4 includes the following steps:

step 4-1, dividing the effective height inversion values in each sliding window in the specified signal reflection area in different time windows by using the sliding windows to obtain corresponding reflection signal subsequences;

step 4-2, based on the sliding windows, combining the effective height inversion values and the inversion errors corresponding to the sliding windows, and establishing an inversion model asWherein L is an effective height inversion sequence containing errors, H is a fusion solution of the effective height inversion values,for sea surface dynamic variation error, Δ_TThe atmospheric refraction error is, and delta is other inversion errors;

and 4-3, performing least square solution on the inversion model to obtain effective height inversion fusion solutions corresponding to all the sliding windows.

A second aspect of the present invention provides a tide level monitoring system based on a multi-mode multi-frequency GNSS receiver, including:

one or more processors;

step 1, obtaining the signal-to-noise ratio of each reflection signal in the reflection signal sequence, generating the signal-to-noise ratio sequence of the reflection signal in a target area, obtaining each interval corresponding to the signal-to-noise ratio sequence according to the oscillation characteristics of the signal-to-noise ratio sequence, wherein the intervals comprise an altitude angle interval and an azimuth angle interval, and then entering step 2;

step 2, carrying out spectrum analysis on the local signal-to-noise ratio sequences in each interval corresponding to the signal-to-noise ratio sequences respectively to obtain a spectrum image corresponding to the local signal-to-noise ratio sequences, further obtaining frequencies corresponding to energy peak values of each reflection signal in the spectrum image respectively, carrying out inversion algorithm on the frequencies to obtain distance values from the antenna to the sea surface in the corresponding interval, taking the distance values as effective heights of each interval, carrying out inversion on the effective heights to obtain inversion values of the effective heights in each interval, and then entering step 3;

step 3, aiming at each interval, preprocessing the reflection signals in the frequency spectrum image corresponding to the corresponding interval by using the frequency corresponding to the energy peak value of the reflection signals in the interval obtained in the step 2 and the inversion value of the effective height, identifying invalid peak values in the reflection signals as pseudo data, deleting the invalid peak values, finishing preprocessing the reflection signals in the corresponding interval, updating each reflection signal corresponding to the corresponding interval, and then entering the step 4;

step 4, aiming at the reflection signal sequence in the reflection area of the received appointed signal in the target time range, selecting a sliding window with a preset time length in the target time range according to the effective height of each interval obtained in the step 3, obtaining the reflection signal subsequence corresponding to each sliding window, obtaining an effective height inversion fusion solution corresponding to each sliding window based on the sliding window, establishing an inversion model by taking each reflection signal in the sliding window as input and taking the effective height bit inversion fusion solution corresponding to the reflection signal subsequence in each sliding window as output, and then entering the step 5;

and 5, obtaining a tide level inversion value according to the effective height inversion fusion value obtained in the step 4, converting the tide level inversion value from a WGS84 standard into a tide level standard, obtaining a tide level standard corresponding to each reflection signal subsequence, namely obtaining a tide level standard sequence result under each sliding window in the target area, and finally obtaining a tide level standard value with equal intervals.

A third aspect of the present invention provides a computer-readable medium storing software, wherein the software includes instructions executable by one or more computers, which when executed by the one or more computers perform the operations of the tidal level monitoring method of any of claims 1-4.

Compared with the prior art, the tide level monitoring method based on the multimode multi-frequency GNSS receiver has the following technical effects:

(1) by utilizing the method provided by the invention, based on the multi-mode multi-frequency GNSS receiver, the tide level monitoring performance which is high in precision, equal in interval, full-automatic, low in cost, long-term and continuous and is supported by a stable framework can be realized.

(2) According to the method, the tide level monitoring precision can be improved and the monitoring data sampling at equal intervals can be realized by properly processing the tide level inversion data of the multi-mode multi-frequency GNSS.

(3) The mathematical model established by the method and the selected resolving strategy are simple in algorithm and easy to implement, all known error sources are considered, the selected resolving strategy can be self-adaptive to avoid gross errors, and the method has good applicability to the conditions of large tidal level change and static water surface.

Drawings

FIG. 1 is a schematic diagram of a multimode multi-frequency GNSS tidal level monitoring system in accordance with an exemplary embodiment of the present invention;

fig. 2 is a flowchart of a multimode multi-frequency GNSS tide level monitoring technique according to an exemplary embodiment of the invention.

Detailed Description

In order to better understand the technical content of the present invention, specific embodiments are described below with reference to the accompanying drawings.

Aspects of the invention are described herein with reference to the accompanying drawings, in which a number of illustrative embodiments are shown. Embodiments of the invention are not limited to those shown in the drawings. It is to be understood that the invention is capable of implementation in any of the numerous concepts and embodiments described hereinabove or described in the following detailed description, since the disclosed concepts and embodiments are not limited to any embodiment. In addition, some aspects of the present disclosure may be used alone, or in any suitable combination with other aspects of the present disclosure.

The invention aims to realize equal-interval sampling and high-precision GNSS sea level monitoring performance and establish a model for fusing multimode multi-frequency GNSS sea level inversion results. Firstly, the multi-mode multi-frequency data means multi-satellite multi-orbit data, and high-frequency sampling of GNSS tide level monitoring can be guaranteed; secondly, inversion redundancy is added to the multimode multi-frequency GNSS data, and a mathematical model of sea level inversion is established by the patent aiming at the error characteristics in sea level inversion of the multimode multi-frequency GNSS; establishing an equation set in the window according to the model by using a sliding window, and simultaneously performing equation set solution by using a least square method; finally, outputting a tide level inversion value in each window, wherein the specific steps are shown in combination with fig. 1 and 2 as follows:

a tide level monitoring method based on a multimode multi-frequency GNSS receiver is based on the GNSS receiver, as shown in figure 1, only a traditional geodetic multi-mode multi-frequency GNSS receiver is needed to be arranged near a sea area to be monitored to complete tide level monitoring, tide level monitoring operation of a specified signal reflection area in a target area within a target time range is realized, when the GNSS receiver receives a reflection signal sequence from the specified signal reflection area in the target area, the following steps are executed in combination with figure 2:

step 1, obtaining the signal-to-noise ratio of each reflection signal in the reflection signal sequence, generating the signal-to-noise ratio sequence of the reflection signal in a target area, ensuring that a signal reflection area in an interval is the sea surface, determining each interval corresponding to the signal-to-noise ratio sequence from the reflection area of the target sea area according to the oscillation characteristic of the signal-to-noise ratio sequence and an electronic map, wherein the intervals comprise an altitude angle interval and an azimuth angle interval so as to ensure that the interference oscillation of the signal-to-noise ratio is caused by the reflection of the sea surface, and then entering step 2;

step 2, performing spectrum analysis on the local signal-to-noise ratio sequences in the intervals corresponding to the signal-to-noise ratio sequences respectively to obtain spectrum images corresponding to the local signal-to-noise ratio sequences, further obtaining frequencies corresponding to energy peak values of all reflection signals in the spectrum images respectively, performing inversion algorithm on the frequencies to obtain distance values from the antennas to the sea surface in the corresponding intervals, taking the distance values as effective heights of the intervals, performing inversion on the effective heights to obtain inversion values of the effective heights in the intervals, and then entering step 3;

preferably, the distance difference between the direct signal and the reflected signal is D2 hsin (e), where e is the signal height angle and h is the distance from the antenna center to the reflecting surface, i.e. the effective height, so that the phase difference between the direct signal and the reflected signal isOne frequency information is hidden in the phase difference: obtaining the frequency corresponding to the local signal-to-noise ratio sequence by a Lomb-Scargle spectrum analysis method according to the following formula:

h＝λf/2

and obtaining the effective height of the corresponding interval of each local signal-to-noise ratio sequence, wherein h is the effective height, f is the frequency of the local signal-to-noise ratio sequence, and lambda is the frequency wavelength.

Step 3, aiming at each interval, preprocessing the reflection signals in the frequency spectrum image corresponding to the corresponding interval by using the frequency corresponding to the energy peak value of the reflection signal in the interval obtained in the step 2 and the inversion value of the tide level, identifying invalid peak values in the reflection signals as pseudo data, deleting the invalid peak values, completing preprocessing the reflection signals in the corresponding interval, updating each reflection signal sequence corresponding to the corresponding interval, and then entering the step 4;

preferably, the preprocessing of the reflected signal in the spectrum image in step 3 includes the following steps:

step 3-1, calculating a ratio of peak energy to background energy in the signal-to-noise sequence spectrum image, when the ratio is larger than a preset threshold value, considering the peak value to be significant, keeping an inversion point corresponding to the peak value, when the ratio is smaller than the preset threshold value, considering the peak value to be insignificant, and deleting the peak value;

and 3-2, inverting the spectrum peak value, screening the spectrum peak value inversion result, identifying pseudo data which are not in a preset tide level interval, and deleting the spectrum peak value corresponding to the pseudo data.

Step 4, aiming at each reflection signal sequence in the designated signal reflection area received in the target time range, selecting a sliding window with a preset time length in the target time range according to the effective height of each interval obtained in the step 3, obtaining a reflection signal subsequence corresponding to each sliding window, obtaining an effective height inversion fusion solution corresponding to each sliding window based on the sliding window, establishing an inversion model by taking each reflection signal in the sliding window as input and taking the effective height inversion fusion solution corresponding to the reflection signal subsequence in each sliding window as output, and then entering the step 5;

preferably, the step 4 of establishing the inverse model comprises the following steps:

step 4-1, dividing the tidal level inversion values in each interval in the specified signal reflection area in different time windows by using a sliding window interval to obtain corresponding reflection signal subsequences;

step 4-2, based on the sliding windows, combining the effective height inversion values and the inversion errors corresponding to the sliding windows, and establishing an inversion model asWherein L is an effective height inversion sequence containing errors, H is a fusion solution of the effective height inversion values,for sea surface dynamic variation error, Δ_TThe atmospheric refraction error is, and delta is other inversion errors;

wherein, Δ ═ Δ_g+Δ_s+Δ_n，Δ_gIs a coarse difference, Δ_sAs systematic error, Δ_nIn the case of an occasional error,Δ_Tfor two types of systematic errors that can be modeled,modeling as a function of the sea surface dynamic rate of change and satellite altitudeIn the formula (I), the compound is shown in the specification,the rate of change of the vertical elevation from the sea surface to the antenna, e the satellite elevation angle,indicating the rate of change of satellite altitude, since H is unknown,in errorThe parameters are also position parameters, therefore, the inverse model is further written as

A time parameter is introduced, taking into account the variation of H with time. Let the inversion point time be t_lWhen the parameter to be solved corresponds toIs m between t_iThe tidal head between the two is approximatelyThe inverse model is further transformed into:

reduced to L ═ AX + Δ_T+ Delta; wherein the content of the first and second substances,can be written as an error equation:in the formula (I), the compound is shown in the specification,is an estimate of X, L ═ L-. DELTA._T；

According to the sliding window interval, the inverted value of the trace L belonging to the sliding window i, the signal j can be written as L_i,j,l(t_i,j,l) The system of equations within the window can be written as in equation (1)

That is, the inverse model within window i is

In the formula (I), the compound is shown in the specification,

4-3, performing least square solution on the inversion model to obtain effective height inversion fusion solutions corresponding to all sliding windows;

the random model of the inversion error contains system error and gross error, and is not normally distributed, and considering the random model, the data is solved by using a gross error least square method, the gross error least square method can resist the model deviation, has stability, can resist the gross error and has tolerance, the gross error least square method is optimal or close to optimal on the premise of resisting the gross error, the core is weight selection iteration, the first iteration: solving parameter estimates according to an equal-weight least squares methodThe residual of this iteration isAnd (5) according to the residual error re-weighting, continuing the least square iteration, and stopping the iteration when the absolute value of the difference between the estimated values of the (k) th iteration and the (k-1) th iteration is converged within the threshold value epsilon. Finally, the estimation result is

And each sliding window interval can be settled to obtain a corresponding H parameter, namely a sea level inversion fusion solution.

Step 5, obtaining a sea level inversion value according to the effective height inversion fusion value obtained in the step 4, wherein the specific formula is as follows: and SL-const-H, wherein SL is a tide level inversion value, const is the height of the antenna under WGS84, the tide level inversion value is converted from WGS84 reference to tide level reference, the tide level reference corresponding to each sub-sequence of the reflection signals is obtained, namely the result of the tide level reference sequence under each sliding window in the target area is obtained, and finally the tide level reference values at equal intervals are obtained.

According to an embodiment of the disclosure, there is also provided a tidal level monitoring system, including:

one or more processors;

a memory storing instructions that are operable, when executed by the one or more processors, to cause the one or more processors to perform operations comprising performing the processes of the terrain classification method of any of the preceding embodiments.

Particularly preferably, the aforementioned processor is a processor of a computer system, including but not limited to an ARM-based embedded processor, an X86-based microprocessor, or a type-based processor.

The memory is arranged as a carrier that can store data, typically comprising RAM and ROM.

It should be understood that the computer system may communicate with each subsystem through the bus to obtain the corresponding parameters, so as to implement the control of the operation of each subsystem.

Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention should be determined by the appended claims.