GNSS-based atmospheric humidity inversion method and system

Abstract

The invention relates to the technical field of atmospheric humidity inversion, and relates to a GNSS-based atmospheric humidity inversion method and system, and the method comprises the steps: obtaining first information, second information and a trained first neural network, the first information comprises a GNSS signal collected by a low-altitude site, and the second information comprises surface humidity data around the low-altitude site; processing the first information by using a polarization filtering algorithm and a beam forming algorithm to obtain processed first information; training a second neural network according to the processed first information and second information to obtain a trained second neural network; inputting the first information and a correction parameter for correcting the wet delay into the trained first neural network to obtain an output result; and performing water vapor inversion according to the output result to construct a water vapor field. The method effectively improves the atmospheric humidity inversion precision of the low-altitude area.

Description

Technical Field

[0001] The present invention relates to the technical field of atmospheric humidity inversion, and in particular to a GNSS-based atmospheric humidity inversion method and system. Background Art

[0002] In the study of the earth's atmospheric environment, changes in altitude have a very significant impact on the accuracy of water vapor inversion. From the perspective of atmospheric physics, changes in altitude are directly related to a variety of physical properties of the atmosphere, which are closely related to the distribution and state of water vapor, and thus profoundly affect the accuracy of water vapor inversion. In high-altitude areas, due to the relatively thin atmosphere, the water vapor content is relatively low, and the atmospheric environment is relatively stable, which is less affected by factors such as complex topography and human activities. This enables GNSS inversion to maintain relatively stable accuracy in high-altitude areas. However, in low-altitude areas, due to the influence of complex topography and dense buildings, the existing high-altitude inversion method cannot accurately invert the atmospheric humidity in low-altitude areas. Summary of the invention

[0003] The purpose of the present invention is to provide a GNSS-based atmospheric humidity inversion method and system to improve the above-mentioned problems.

[0004] In order to achieve the above objectives, the present application provides the following technical solutions: On the one hand, an embodiment of the present application provides a method for inverting atmospheric humidity based on GNSS, the method comprising: Acquire first information, second information, and a trained first neural network, wherein the first information includes GNSS signals collected by low-altitude sites, the low-altitude sites include sites corresponding to elevations less than 500 m, the first information is collected by antenna arrays provided at the low-altitude sites, the second information includes surface humidity data around the low-altitude sites, and the trained first neural network is used to predict the weighted average temperature of the atmosphere in the low-altitude area; Processing the first information using a polarization filtering algorithm and a beamforming algorithm to obtain processed first information; Training a second neural network according to the processed first information and the second information to obtain a trained second neural network, wherein the trained second neural network is used to correct wet delay; Inputting the first information and a correction parameter for correcting wet delay into the trained first neural network to obtain an output result, wherein the output result includes a predicted value of the atmospheric weighted average temperature; Water vapor inversion is performed according to the output result to construct a water vapor field, and the water vapor field is used to characterize the atmospheric humidity distribution corresponding to the low altitude area.

[0005] In a second aspect, an embodiment of the present application provides a GNSS-based atmospheric humidity inversion system, the system comprising: an acquisition module, configured to acquire first information, second information, and a trained first neural network, wherein the first information includes GNSS signals collected by low-altitude sites, the low-altitude sites include sites corresponding to elevations less than 500 m, the first information is collected by antenna arrays provided at the low-altitude sites, the second information includes surface humidity data around the low-altitude sites, and the trained first neural network is used to predict the weighted average temperature of the atmosphere in the low-altitude area; A first processing module, configured to process the first information by using a polarization filtering algorithm and a beamforming algorithm to obtain processed first information; A second processing module is used to train a second neural network according to the processed first information and the second information to obtain a trained second neural network, wherein the trained second neural network is used to correct wet delay; a third processing module, configured to input the first information and a correction parameter for correcting wet delay into the trained first neural network to obtain an output result, wherein the output result includes a predicted value of the atmospheric weighted average temperature; The fourth processing module is used to perform water vapor inversion according to the output result to construct a water vapor field, and the water vapor field is used to characterize the atmospheric humidity distribution corresponding to the low altitude area.

[0006] In a third aspect, an embodiment of the present application provides a GNSS-based atmospheric humidity inversion device, the device comprising a memory and a processor. The memory is used to store a computer program; the processor is used to implement the steps of the GNSS-based atmospheric humidity inversion method when executing the computer program.

[0007] In a fourth aspect, an embodiment of the present application provides a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned GNSS-based atmospheric humidity inversion method are implemented.

[0008] The beneficial effects of the present invention are: The present invention utilizes polarization filtering and beamforming algorithms to process the first information, suppresses multipath noise caused by strong reflection sources such as buildings and water areas, and then trains the second neural network in combination with the second information. The correction parameters of wet delay are determined according to the output results of the second neural network, effectively solving the problem of low signal-to-noise ratio caused by the multipath effect at low altitudes. At the same time, the reflection signal that is traditionally regarded as interference is converted into an inversion input to improve data utilization. The correction parameters of wet delay and the first information are then input into the trained first neural network to obtain an output result. A water vapor field is constructed according to the output result to realize the inversion of atmospheric humidity, thereby improving the inversion accuracy in low altitude areas.

[0009] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 It is a schematic diagram of the flow chart of the atmospheric humidity inversion method based on GNSS described in an embodiment of the present invention.

[0012] Figure 2 It is a schematic diagram of the structure of the GNSS-based atmospheric humidity inversion system described in an embodiment of the present invention.

[0013] Figure 3 It is a schematic diagram of the structure of the GNSS-based atmospheric humidity inversion device described in an embodiment of the present invention.

[0014] Labels in the figure: 800, GNSS-based atmospheric humidity inversion device; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component; 901, acquisition module; 902, first processing module; 903, second processing module; 904, third processing module; 905, fourth processing module. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0016] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. Embodiment 1:

[0017] This embodiment provides a GNSS-based atmospheric humidity inversion method. It can be understood that a scenario can be laid out in this embodiment, for example: a scenario of detecting the atmospheric humidity corresponding to an area with an altitude of less than 500m.

[0018] See also Figure 1 , the figure shows that the method includes steps S1, S2, S3, S4 and S5, which specifically include: Step S1, obtaining first information, second information and a trained first neural network, wherein the first information includes GNSS signals collected by low-altitude sites, the low-altitude sites include sites corresponding to elevations less than 500m, the first information is collected by antenna arrays set at the low-altitude sites, the second information includes surface humidity data around the low-altitude sites, and the trained first neural network is used to predict the weighted average temperature of the atmosphere in the low-altitude area; In this step, a multi-band antenna array is deployed at a low-altitude site (less than 500 m above sea level) to capture GNSS signals in the L1 / L2 / L5 bands in real time, providing basic data for subsequent processing and analysis.

[0019] The step S1 also includes step S11, step S12, step S13, step S14 and step S15, which specifically include: Step S11, acquiring historical data of a high altitude area, wherein the historical data of the high altitude area includes meteorological observation data, GNSS observation data and time series data; Step S12: constructing a feature matrix corresponding to the high altitude area according to the historical data of the high altitude area to obtain a second feature matrix; In this step, the second characteristic matrix includes wet delay, atmospheric weighted average temperature, and altitude.

[0020] Step S13, pre-training the first neural network using the second characteristic matrix corresponding to the high altitude area to obtain a pre-trained first neural network; In this step, the first neural network uses an LSTM network (128 hidden units) to process the historical data of the high-altitude site, and the input features are the features included in the second feature matrix.

[0021] Step S14, constructing a characteristic matrix according to historical data of the low altitude area and correction parameters for correcting wet delay to obtain a third characteristic matrix; Step S15: training the pre-trained first neural network according to the third characteristic matrix to obtain a trained first neural network.

[0022] In this step, when training the pre-trained first neural network, the bottom layer weights of LSTM are frozen, and only the top fully connected layer is trained. The pre-trained first neural network is trained through a small sample of low-altitude historical data, thereby obtaining the trained first neural network.

[0023] In this embodiment, since there are fewer historical data samples in low-altitude areas, historical data from high-altitude areas are first used for pre-training, and then a small sample of low-altitude historical data is used to fine-tune the pre-trained first neural network, thereby obtaining the trained first neural network. This not only solves the model generalization problem caused by insufficient low-altitude samples, but also improves the accuracy of the prediction of the weighted average temperature of the atmosphere in low-altitude areas.

[0024] Step S2: Processing the first information using a polarization filtering algorithm and a beamforming algorithm to obtain processed first information; The step S2 also includes step S21, step S22, step S23, step S24 and step S25, which specifically include: Step S21, preprocessing the first information to obtain first information after noise removal; Step S22: determining a polarization mode corresponding to the GNSS signal according to the first information; Step S23, designing a polarization filter according to the polarization mode corresponding to the GNSS signal; It is understandable that the signal transmitted by the GNSS satellite has a specific polarization mode, and the polarization characteristics of the multipath reflected signal will change due to reflection from the reflector, which is different from the polarization characteristics of the direct signal. Therefore, by determining the polarization mode of the collected GNSS signal, the polarization filter can be designed, thereby designing a suitable polarization filter, thereby maximally suppressing the multipath reflected signal with different polarization characteristics from the direct signal.

[0025] Step S24, processing the first information after the noise is removed according to the polarization filter to obtain third information; In this step, the GNSS direct signal is usually right-hand circularly polarized. The polarization filter is designed to mainly pass the right-hand circularly polarized signal and has a high attenuation on the left-hand circularly polarized signal. This can effectively suppress the left-hand circularly polarized component that may appear in the multipath reflection signal.

[0026] Step S25: Process the third information using a beamforming algorithm to obtain the processed first information.

[0027] The step S25 also includes step S251, step S252, step S253, step S254, step S255 and step S256, which specifically include: Step S251: Process the third information using a multiple signal classification algorithm to obtain an arrival angle of the signal; In this step, based on the orthogonality of the signal subspace and the noise subspace, firstly, the polarization filtered signal received by the antenna array is sampled to obtain a signal matrix. Then, the signal matrix is ​​eigen-decomposed to decompose it into the signal subspace and the noise subspace. Since the signal subspace is orthogonal to the noise subspace, the peak position of the spatial spectrum is searched by constructing a spatial spectrum function, and the angle corresponding to the peak is the arrival angle of the signal.

[0028] Step S252: establishing an array flow model according to the geometric structure of the antenna array; In this step, the array flow model describes the response characteristics of the signal when it reaches each element of the antenna array. In a specific implementation, the specific representation of the array manifold vector is:

[0029] In the above formula, represents the array manifold vector, represents the angle of arrival of the signal, Indicates the amplitude, Indicates the phase difference.

[0030] Step S253, determining the time delay of the signal reaching different array elements according to the arrival angle of the signal and the array flow model; In a specific implementation, when the antenna array is a uniform linear array, the first array element is used as a reference array element. When the arrival angle of the signal is known, the path difference of each array element relative to the reference array element can be calculated, and then the time delay of each array element relative to the reference array element can be calculated based on the path difference of each array element relative to the reference array element.

[0031] Step S254, determining the phase difference between the array elements according to the time delay of the signal reaching different array elements, and obtaining phase difference information; In this step, since the phase of the signal is proportional to the time delay, the phase difference between the array elements can be calculated through the time delay.

[0032] Step S255: Process the phase difference information using a minimum variance distortion-free response algorithm to obtain a weight coefficient corresponding to each array element; The step S255 also includes step S2551, step S2552, step S2553, step S2554 and step S2555, which specifically include: Step S2551, sampling the third information to obtain discrete samples of the signal; Step S2552: Calculate the discrete samples of the signal using a root mean square algorithm to obtain an amplitude value of the signal; Step S2553: constructing an array manifold vector according to the amplitude value of the signal and the phase difference information, wherein the array manifold vector is used to describe the relationship between the amplitude and phase of the signal received by each array element when the signal arrives; Step S2554, calculating the covariance matrix corresponding to the third information; Step S2555: Calculate according to the array manifold vector and the covariance matrix to obtain a weight coefficient corresponding to each array element.

[0033] In this step, the specific calculation process of the weight coefficient is:

[0034] In the above formula, W represents the weight coefficient vector, is the inverse of the covariance matrix, is the array manifold vector, represents the angle of arrival of the signal, It is the conjugate transpose of the array manifold vector. By solving the above formula, the weight coefficient corresponding to each array element can be obtained. The present invention can minimize the power output by the array while ensuring that the desired signal passes without distortion, thereby suppressing interference and noise.

[0035] Step S256: weight the third information received by the antenna array according to the weight coefficient to obtain the processed first information.

[0036] In this embodiment, the calculated weight coefficient is applied to the polarization filtered signal received by the antenna array. The signal received by each array element is multiplied by the corresponding weight coefficient, and the amplitude adjustment and phase correction of each array element signal are performed so that the signal from the desired direction is superimposed in phase at the output end of the antenna array, thereby enhancing the signal strength in that direction; while the signals from other directions (such as the direction of multipath reflection signals) are offset or weakened due to phase inconsistency and other reasons. In this way, after weighted processing, the residual multipath reflection signal is further suppressed, and the quality and signal-to-noise ratio of the GNSS signal are improved.

[0037] Step S3, training a second neural network according to the processed first information and the second information to obtain a trained second neural network, wherein the trained second neural network is used to correct wet delay; The step S3 also includes step S31, step S32, step S33, step S34 and step S35, which specifically include: Step S31, extracting the signal-to-noise ratio in the processed first information to obtain signal-to-noise ratio information; Step S32, performing wavelet decomposition on the signal-to-noise ratio information to obtain decomposed signal information, wherein the decomposed signal information includes a high-frequency fluctuation part of the reflection signal; In this step, the signal-to-noise ratio information is subjected to wavelet decomposition (db4 wavelet is selected) to separate the low-frequency trend term (dominated by the direct signal) and the high-frequency fluctuation term (dominated by the reflected signal), while only retaining the reflected signal component with a frequency of 0.1-0.5 Hz.

[0038] Step S33, performing time-frequency analysis on the decomposed signal information to determine characteristic information corresponding to the decomposed signal information, wherein the characteristic information includes amplitude, phase delay and time delay difference; In this step, the decomposed signal information is subjected to short-time Fourier transform to extract the dominant frequency component of the reflected signal, and then the corresponding amplitude, phase delay and time delay difference are calculated based on the dominant frequency component of the reflected signal.

[0039] Step S34, establishing a first feature matrix according to the feature information and the second information, and training the second neural network according to the first feature matrix to obtain a trained second neural network; In this step, the feature information and the second information are aligned by timestamp to form a first feature matrix to train the second neural network, and a mapping function between the reflection path delay and the surface humidity is established, thereby obtaining a reflection path-humidity mapping model, that is, the trained second neural network.

[0040] Step S35, correcting the wet delay according to the trained second neural network.

[0041] The step S35 also includes step S351, step S352, step S353, step S354 and step S355, which specifically include: Step S351, obtaining real-time feature information; Step S352, sending the real-time feature information to the trained second neural network to obtain fourth information, wherein the fourth information includes a predicted value of surface humidity at a reflection point; Step S353: Calculate according to the fourth information and the surface humidity value of the reflection point measured at the site to obtain a correction parameter of the wet delay; In this step, the specific calculation formula of the correction parameter of wet delay is: ;

[0042] In the above formula, represents the correction parameter for wet delay, represents the predicted value of surface humidity at the reflection point, and Represents the model coefficients, used to value to adjust.

[0043] Step S355: correct the wet delay according to the correction parameter of the wet delay.

[0044] In this step, the specific process of correcting the wet delay is as follows: ;

[0045] In the above formula, is the corrected wet delay, is the wet delay before correction, represents the correction parameter for wet delay, It represents the elevation correction factor. The H corresponding to the low altitude area is 1.02∼1.05, which compensates for the near-surface water vapor density gradient.

[0046] Step S4, inputting the first information and the correction parameter for correcting the wet delay into the trained first neural network to obtain an output result, wherein the output result includes a predicted value of the atmospheric weighted average temperature; Step S5: Perform water vapor inversion according to the output result to construct a water vapor field, wherein the water vapor field is used to characterize the atmospheric humidity distribution corresponding to the low altitude area.

[0047] The step S5 also includes step S51, step S52, step S53 and step S54, which specifically include: Step S51, obtaining water density information, water vapor gas constant and atmospheric refraction constant; Step S52, calculating according to the water density information, the water vapor gas constant, the atmospheric refraction constant and the output result to obtain a conversion coefficient; In this step, the specific calculation process of the conversion coefficient is: ;

[0048] In the above formula, represents the conversion factor, represents the density of water, is the water vapor gas constant, and All represent the atmospheric refraction constant, where It is related to the combined refraction effect of dry air and water vapor in the atmosphere. Related to the refraction effect of water vapor, Indicates the output result.

[0049] Step S53, calculating according to the conversion coefficient and the corrected wet delay to obtain atmospheric precipitable water; In this step, the atmospheric precipitable water is obtained by multiplying the conversion coefficient with the corrected wet delay.

[0050] Step S54: Use Kriging interpolation to process the atmospheric precipitable water corresponding to all stations in the low-altitude area to construct a water vapor field.

[0051] In this step, Kriging interpolation is performed on the atmospheric precipitable water at all GNSS stations in the low-altitude area to generate a water vapor distribution map with a resolution of 1 km, which intuitively displays the distribution of water vapor in the area and provides detailed data and visualization information for meteorological analysis and decision-making.

[0052] It should be noted that the water vapor field needs to be further optimized. Embodiment 2:

[0053] like Figure 2 As shown, this embodiment provides a GNSS-based atmospheric humidity inversion system, the system includes an acquisition module 901, a first processing module 902, a second processing module 903, a third processing module 904 and a fourth processing module 905, which specifically include: An acquisition module 901 is used to acquire first information, second information and a trained first neural network, wherein the first information includes GNSS signals collected by low-altitude sites, the low-altitude sites include sites corresponding to elevations less than 500m, the first information is collected by antenna arrays set at the low-altitude sites, the second information includes surface humidity data around the low-altitude sites, and the trained first neural network is used to predict the weighted average temperature of the atmosphere in the low-altitude area; A first processing module 902, configured to process the first information using a polarization filtering algorithm and a beamforming algorithm to obtain processed first information; A second processing module 903 is used to train a second neural network according to the processed first information and the second information to obtain a trained second neural network, wherein the trained second neural network is used to correct wet delay; A third processing module 904 is used to input the first information and the correction parameter for correcting the wet delay into the trained first neural network to obtain an output result, wherein the output result includes a predicted value of the atmospheric weighted average temperature; The fourth processing module 905 is used to perform water vapor inversion according to the output result to construct a water vapor field, where the water vapor field is used to characterize the atmospheric humidity distribution corresponding to the low altitude area.

[0054] In a specific embodiment of the present disclosure, the first processing module further includes a first processing unit, a second processing unit, a third processing unit, a fourth processing unit and a fifth processing unit, which specifically include: A first processing unit, configured to preprocess the first information to obtain first information after noise is removed; A second processing unit, configured to determine a polarization mode corresponding to the GNSS signal according to the first information; A third processing unit, configured to design a polarization filter according to a polarization mode corresponding to the GNSS signal; a fourth processing unit, configured to process the first information after the noise is removed according to the polarization filter to obtain third information; The fifth processing unit is used to process the third information by using a beamforming algorithm to obtain the processed first information.

[0055] In a specific implementation of the present disclosure, the fifth processing unit further includes a sixth processing unit, a seventh processing unit, an eighth processing unit, a ninth processing unit, a tenth processing unit and an eleventh processing unit, which specifically include: a sixth processing unit, configured to process the third information using a multiple signal classification algorithm to obtain an arrival angle of the signal; a seventh processing unit, configured to establish an array flow model according to a geometric structure of the antenna array; an eighth processing unit, configured to determine a time delay of a signal arriving at different array elements according to an arrival angle of the signal and the array flow model; A ninth processing unit, configured to determine the phase difference between the array elements according to the time delay of the signal reaching different array elements, and obtain phase difference information; a tenth processing unit, configured to process the phase difference information using a minimum variance distortion-free response algorithm to obtain a weight coefficient corresponding to each array element; The eleventh processing unit is used to weight the third information received by the antenna array according to the weight coefficient to obtain the processed first information.

[0056] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here. Embodiment 3:

[0057] Corresponding to the above method embodiment, this embodiment further provides a GNSS-based atmospheric humidity inversion device, and the GNSS-based atmospheric humidity inversion device described below and the GNSS-based atmospheric humidity inversion method described above can refer to each other.

[0058] Figure 3 FIG. 8 is a block diagram of a GNSS-based atmospheric humidity inversion device 800 according to an exemplary embodiment. Figure 3 As shown, the GNSS-based atmospheric humidity inversion device 800 may include: a processor 801 and a memory 802. The GNSS-based atmospheric humidity inversion device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0059] The processor 801 is used to control the overall operation of the GNSS-based atmospheric humidity inversion device 800 to complete all or part of the steps in the above-mentioned GNSS-based atmospheric humidity inversion method. The memory 802 is used to store various types of data to support the operation of the GNSS-based atmospheric humidity inversion device 800, and these data may include, for example, instructions for any application or method operating on the GNSS-based atmospheric humidity inversion device 800, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 802 or sent via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the GNSS-based atmospheric humidity inversion device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include: Wi-Fi module, Bluetooth module, NFC module.

[0060] In an exemplary embodiment, the GNSS-based atmospheric humidity inversion device 800 can be implemented by one or more application-specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned GNSS-based atmospheric humidity inversion method.

[0061] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned GNSS-based atmospheric humidity inversion method are implemented. For example, the computer-readable storage medium may be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions may be executed by the processor 801 of the GNSS-based atmospheric humidity inversion device 800 to complete the above-mentioned GNSS-based atmospheric humidity inversion method. Embodiment 4:

[0062] Corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment. The readable storage medium described below and the GNSS-based atmospheric humidity inversion method described above can refer to each other.

[0063] A readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the GNSS-based atmospheric humidity inversion method of the above method embodiment are implemented.

[0064] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other readable storage medium that can store program codes.

[0065] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0066] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A GNSS-based atmospheric humidity inversion method, characterized in that: include: Acquire first information, second information, and a trained first neural network, wherein the first information includes GNSS signals collected by low-altitude sites, the low-altitude sites include sites corresponding to elevations less than 500 m, the first information is collected by antenna arrays provided at the low-altitude sites, the second information includes surface humidity data around the low-altitude sites, and the trained first neural network is used to predict the weighted average temperature of the atmosphere in the low-altitude area; Processing the first information using a polarization filtering algorithm and a beamforming algorithm to obtain processed first information; Training a second neural network according to the processed first information and the second information to obtain a trained second neural network, wherein the trained second neural network is used to correct wet delay; Inputting the first information and a correction parameter for correcting wet delay into the trained first neural network to obtain an output result, wherein the output result includes a predicted value of the atmospheric weighted average temperature; Water vapor inversion is performed according to the output result to construct a water vapor field, and the water vapor field is used to characterize the atmospheric humidity distribution corresponding to the low altitude area.

2. The GNSS-based atmospheric humidity inversion method according to claim 1, characterized in that: The first information is processed by using a polarization filtering algorithm and a beamforming algorithm to obtain the processed first information, including: Preprocessing the first information to obtain first information after noise is removed; Determine a polarization mode corresponding to the GNSS signal according to the first information; Designing a polarization filter according to the polarization mode corresponding to the GNSS signal; Processing the first information after the noise is removed according to the polarization filter to obtain third information; The third information is processed using a beamforming algorithm to obtain the processed first information.

3. The GNSS-based atmospheric humidity inversion method according to claim 2, characterized in that: Processing the third information by using a beamforming algorithm to obtain the processed first information includes: Processing the third information using a multiple signal classification algorithm to obtain an arrival angle of the signal; An array flow model is established according to the geometric structure of the antenna array; Determine the time delay of the signal arriving at different array elements according to the arrival angle of the signal and the array flow model; Determine the phase difference between the array elements according to the time delay of the signal reaching different array elements, and obtain phase difference information; The phase difference information is processed using the minimum variance distortion-free response algorithm to obtain the weight coefficient corresponding to each array element; The third information received by the antenna array is weighted according to the weight coefficient to obtain the processed first information.

4. The GNSS-based atmospheric humidity inversion method according to claim 3, characterized in that: The phase difference information is processed using the minimum variance distortion-free response algorithm to obtain the weight coefficient corresponding to each array element, including: Sampling the third information to obtain discrete samples of the signal; Calculating discrete samples of the signal using a root mean square algorithm to obtain an amplitude value of the signal; constructing an array manifold vector according to the amplitude value of the signal and the phase difference information, wherein the array manifold vector is used to describe the relationship between the amplitude and phase of the signal received by each array element when the signal arrives; Calculate the covariance matrix corresponding to the third information; Calculation is performed based on the array manifold vector and the covariance matrix to obtain a weight coefficient corresponding to each array element.

5. The GNSS-based atmospheric humidity inversion method according to claim 1, characterized in that: The second neural network is trained according to the processed first information and the second information to obtain a trained second neural network, including: Extracting a signal-to-noise ratio from the processed first information to obtain signal-to-noise ratio information; Performing wavelet decomposition on the signal-to-noise ratio information to obtain decomposed signal information, wherein the decomposed signal information includes a high-frequency fluctuation part of the reflected signal; Performing time-frequency analysis on the decomposed signal information to determine characteristic information corresponding to the decomposed signal information, wherein the characteristic information includes amplitude, phase delay, and time delay difference; Establishing a first feature matrix according to the feature information and the second information, and training a second neural network according to the first feature matrix to obtain a trained second neural network; The wet delay is corrected according to the trained second neural network.

6. The GNSS-based atmospheric humidity inversion method according to claim 1, characterized in that: Acquiring the trained first neural network includes: Acquire historical data of a high altitude area, wherein the historical data of the high altitude area includes meteorological observation data, GNSS observation data, and time series data; constructing a feature matrix corresponding to the high-altitude area according to the historical data of the high-altitude area to obtain a second feature matrix; Pre-training the first neural network using the second feature matrix corresponding to the high altitude area to obtain a pre-trained first neural network; A characteristic matrix is ​​constructed according to historical data of low altitude areas and correction parameters for correcting wet delay to obtain a third characteristic matrix; The pre-trained first neural network is trained according to the third characteristic matrix to obtain a trained first neural network.

7. The GNSS-based atmospheric humidity inversion method according to claim 1, characterized in that: Perform water vapor inversion according to the output results to construct a water vapor field, including: Obtain water density information, water vapor gas constant and atmospheric refraction constant; Calculating according to the water density information, the water vapor gas constant, the atmospheric refraction constant and the output result to obtain a conversion coefficient; Calculating according to the conversion coefficient and the corrected wet delay to obtain atmospheric precipitable water; Kriging interpolation is used to process the atmospheric precipitable water corresponding to all stations in the low-altitude area to construct the water vapor field.

8. A GNSS-based atmospheric humidity inversion system, characterized in that: include: an acquisition module, configured to acquire first information, second information, and a trained first neural network, wherein the first information includes GNSS signals collected by low-altitude sites, the low-altitude sites include sites corresponding to elevations less than 500 m, the first information is collected by antenna arrays provided at the low-altitude sites, the second information includes surface humidity data around the low-altitude sites, and the trained first neural network is used to predict the weighted average temperature of the atmosphere in the low-altitude area; A first processing module, configured to process the first information by using a polarization filtering algorithm and a beamforming algorithm to obtain processed first information; A second processing module is used to train a second neural network according to the processed first information and the second information to obtain a trained second neural network, wherein the trained second neural network is used to correct wet delay; a third processing module, configured to input the first information and a correction parameter for correcting wet delay into the trained first neural network to obtain an output result, wherein the output result includes a predicted value of the atmospheric weighted average temperature; The fourth processing module is used to perform water vapor inversion according to the output result to construct a water vapor field, and the water vapor field is used to characterize the atmospheric humidity distribution corresponding to the low altitude area.

9. The GNSS-based atmospheric humidity inversion system according to claim 8, characterized in that: The first processing module comprises: A first processing unit, configured to preprocess the first information to obtain first information after noise is removed; A second processing unit, configured to determine a polarization mode corresponding to the GNSS signal according to the first information; A third processing unit, configured to design a polarization filter according to a polarization mode corresponding to the GNSS signal; a fourth processing unit, configured to process the first information after the noise is removed according to the polarization filter to obtain third information; The fifth processing unit is used to process the third information by using a beamforming algorithm to obtain the processed first information.

10. The GNSS-based atmospheric humidity inversion system according to claim 9, characterized in that: The fifth processing unit comprises: a sixth processing unit, configured to process the third information using a multiple signal classification algorithm to obtain an arrival angle of the signal; a seventh processing unit, configured to establish an array flow model according to a geometric structure of the antenna array; an eighth processing unit, configured to determine a time delay of a signal reaching different array elements according to an arrival angle of the signal and the array flow model; A ninth processing unit, configured to determine the phase difference between the array elements according to the time delay of the signal reaching different array elements, and obtain phase difference information; a tenth processing unit, configured to process the phase difference information using a minimum variance distortion-free response algorithm to obtain a weight coefficient corresponding to each array element; The eleventh processing unit is used to weight the third information received by the antenna array according to the weight coefficient to obtain the processed first information.

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