GNSS-R accumulated snow thickness and atmospheric parameter joint detection system based on double-different-polarization antenna structure

Abstract

The invention discloses a GNSS-R snow thickness and atmospheric parameter joint detection system based on a double-different-polarization antenna structure, and the system comprises a GNSS signal collection module which employs a double-different-polarization antenna structure, and is used for receiving GNSS signals of a direct and reflection path, and capturing the interference fringe change in a reflection signal; the meteorological auxiliary acquisition module is used for acquiring auxiliary observation information including temperature, humidity and observation station air pressure; the data processing module is used for carrying out parallel processing on the GNSS signals of the direct and reflection paths, realizing inversion of accumulated snow based on an accumulated snow inversion error optimization strategy in combination with auxiliary observation information, and carrying out combined calculation on the total electron content of the ionized layer, the scintillation parameter of the ionized layer, the zenith delay of the troposphere, the precipitable water amount and the thickness of the accumulated snow; and the data communication uploading module is used for organizing the processed key parameters into standardized data products and transmitting the standardized data products to a server or a user terminal in real time.

Description

Technical Field

[0001] The present invention belongs to the technical direction of global navigation satellite system (GNSS) environmental monitoring, which is at the intersection of navigation and remote sensing measurement, and in particular relates to a GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual-polarized antenna structure. Background Art

[0002] The Global Navigation Satellite System (GNSS) has been widely used in many fields such as earth science, meteorological monitoring and disaster warning due to its high precision, all-weather and global coverage capabilities. In traditional applications, GNSS is mainly used for positioning and timing services. In recent years, the rapid development of GNSS reflectometry (GNSS-R) and interferometry (GNSS-IR) technologies has continuously highlighted its potential in the inversion of environmental parameters such as snow accumulation, water level, atmospheric water vapor, and ionospheric disturbances. GNSS-IR technology receives the interference fluctuations formed by the direct GNSS signal and the signal reflected from the surface, and analyzes the periodic components in the signal-to-noise ratio (SNR). It can invert the height difference between the antenna and the surface, thereby achieving low-cost, passive, and high-precision measurements such as snow thickness. Although this technology has been initially applied in scientific research, the existing engineering system still has the following outstanding problems in structural design and signal reception:

[0003] First, existing ground-based GNSS systems lack targeted optimization of their reflected signal receiving structures and polarization characteristics, resulting in suboptimal inversion accuracy and interference fringe quality. Most systems still utilize a single high-gain right-handedly polarized (RHCP) choke ring antenna. While this helps improve direct signal quality and mitigate multipath interference, it also hinders effective reception of reflected signals. In GNSS-IR scenarios, signals often experience depolarization, rotation, and polarization coupling after reflecting from the ground. This significantly reduces the reception efficiency of traditional RHCP antennas along the reflected path, making it difficult to extract the fringe's main frequency and causing significant deviations in interference fringe quality. At low elevation angles, the choke ring structure can even partially shield reflected waves, preventing them from reaching the receiving system. Furthermore, balancing direct and reflected signals with the same antenna not only creates polarization conflicts but also makes it difficult to optimize the gain of both simultaneously. Currently, there are no dual-antenna design solutions specifically designed for GNSS-IR, nor is there systematic modeling and comparative analysis of the response characteristics and signal quality of horizontal, vertical, and circular polarizations along the reflected path. In view of the reflection at the interface of planar media: the reflection coefficient of vertical polarization approaches zero and the phase changes dramatically at the Brewster angle, which is not conducive to interferometric height measurement; circular polarization is prone to chirality flip or elliptical after reflection, and it is necessary to decompose the H / V components and take into account the phase difference and amplitude imbalance, which increases the difficulty of reception and processing; while the reflection amplitude of horizontal polarization increases gently with the incident angle and the phase remains constant. In particular, the reflection intensity is highest at low elevation angles, and the correspondence between the multipath interference fringe frequency and the height of the reflecting surface is stable and clear, making it the optimal choice for snow depth measurement based on GNSS-IR.

[0004] Second, there is a lack of systematic error compensation strategies for GNSS-IR snow inversion to meet the requirements for stable altimetry under multi-source interference. The sources of error in GNSS-IR snow inversion are complex, including antenna baseline errors, spatial geometric deviations caused by inconsistencies between antenna installation position and elevation / azimuth angles, pseudo-peak interference in spectrum analysis, low-elevation signal distortion, tropospheric delay errors, and signal attenuation and multipath errors under obstruction. Furthermore, in dynamic observation scenarios, satellite orbit parameter uncertainties and the time-varying characteristics of the reflecting surface can significantly impact inversion accuracy. However, existing systems generally employ distributed processing strategies, making it difficult to collaboratively model and jointly correct these multi-source errors. Currently, there is a lack of a multi-error fusion modeling system for GNSS-IR, particularly in the areas of dual-antenna baseline error correction, Lomb-Scargle spectrum analysis stability enhancement, elevation / azimuth constraint optimization, and joint multi-antenna data processing. The above problems result in poor height measurement stability of existing systems under extreme conditions such as complex terrain, signal obstruction, and low-elevation-angle observation in the field, and large fluctuations in inversion results, making it difficult to meet the practical requirements of high precision and high robustness.

[0005] Third, the functional modules are separated and lack the ability of multi-parameter integrated collaborative observation. Most of the current GNSS ground-based systems are developed through dedicated approaches, such as high-frequency sampling and receiving systems for ionospheric total electron content (TEC) inversion, conventional weather station-type GNSS devices for tropospheric water vapor (PWV) inversion, and snow inversion modules based on SNR extraction. These systems are independent of each other in terms of functions, data interfaces and processing logic are not unified, and a standardized and integrated operating framework cannot be formed. In actual deployment, it is often necessary to configure multiple sets of equipment separately to obtain multiple environmental parameters, resulting in increased costs, difficulty in data synchronization, and low space utilization. The existing technology has not proposed a fusion architecture that integrates GNSS-IR inversion, ionospheric disturbance analysis, and tropospheric wet delay estimation, which limits the practicality and scalability of the system in comprehensive environmental monitoring scenarios. Summary of the Invention

[0006] The purpose of the present invention is to overcome the defects of the prior art and propose a GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual-polarized antenna structure.

[0007] In view of this, the present invention proposes a GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual polarized antenna structure, which is characterized by comprising: a GNSS signal acquisition module, a meteorological auxiliary acquisition module, a data processing module and a data communication upload module, wherein:

[0008] The GNSS signal acquisition module adopts a dual polarized antenna structure to receive GNSS signals from direct and reflected paths respectively, and capture the changes in interference fringes in the reflected signals;

[0009] The meteorological auxiliary collection module is used to collect auxiliary observation information including temperature, humidity and station pressure;

[0010] The data processing module is used to process the GNSS signals of the direct and reflected paths in parallel, combine auxiliary observation information, and based on the snow inversion error optimization strategy, correct the installation error and reference plane offset of the dual polarization antenna, introduce physical model constraints related to elevation angle, suppress pseudo-peak interference, adaptively fuse the processed direct and reflected path data, and compensate the inversion height in real time by establishing a delay correction method based on an empirical model to achieve the inversion of snow cover and the joint calculation of ionospheric total electron content, ionospheric scintillation parameters, tropospheric zenith delay, precipitable water content, and snow thickness;

[0011] The data communication upload module is used to organize the processed key parameters into standardized data products and transmit them to the server or user terminal in real time.

[0012] Preferably, the dual polarized antenna structure of the GNSS signal acquisition module includes: a direct antenna and a horizontally polarized reflective antenna; wherein,

[0013] The direct antenna adopts a high-gain choke structure and is used to receive direct signals from GNSS satellites;

[0014] The horizontally polarized reflective antenna is used to receive the GNSS signal reflection path from the ground surface or the snow surface.

[0015] Preferably, the data processing module includes: a tropospheric water vapor calculation module, an ionospheric parameter calculation module, a snow thickness inversion module and a data optimization module; wherein,

[0016] The tropospheric water vapor calculation module is used to invert the precipitable water content based on GNSS observation data and auxiliary observation information;

[0017] The ionospheric parameter calculation module is used to obtain the ionospheric disturbance information received during the propagation of the GNSS signal;

[0018] The snow thickness inversion module is used to realize snow thickness inversion based on interferometry;

[0019] The data optimization module is used to improve the overall accuracy and stability of GNSS reflection height measurement based on the snow inversion error optimization strategy.

[0020] Preferably, the processing process of the tropospheric water vapor calculation module includes:

[0021] Solve the GNSS data and calculate the total tropospheric zenith delay (ZTD);

[0022] Zenith dry delay (ZHD) is estimated using an atmospheric model based on temperature, humidity, and station pressure.

[0023] Subtract ZTD from ZHD to obtain the zenith wet delay ZWD reflecting the influence of water vapor;

[0024] By introducing a set wet delay conversion coefficient, ZWD is converted into precipitable water volume (PWV), thus achieving accurate inversion of atmospheric water vapor content.

[0025] Preferably, the processing of the ionospheric parameter calculation module includes:

[0026] Calculate the frequency delay difference based on dual-frequency GNSS observation data and solve the ionospheric total electron content (TEC);

[0027] The strength of the GNSS signal is continuously monitored, and the S4 index is obtained by calculating the ratio of the standard deviation of the signal strength series to the mean value to evaluate the degree of amplitude scintillation. At the same time, short-time-scale differential operations are performed on the carrier phase to extract the phase fluctuation characteristics to reflect the ionospheric phase scintillation intensity.

[0028] Preferably, the processing of the snow thickness inversion module includes:

[0029] Extract the signal-to-noise ratio sequence from the GNSS observation data as the original input for interference fringe analysis;

[0030] The Lomb-Scargle spectrum analysis method is used to extract the spectrum of the signal-to-noise ratio sequence to identify the interference fringe frequency corresponding to the main frequency;

[0031] According to the interference frequency combined with the system geometric parameters and the electrical characteristics of the snow surface, the distance difference between the antenna and the reflecting surface is inverted, and the snow thickness is estimated based on the difference in the distance between the antenna and the reflecting surface before and after snowfall.

[0032] Preferably, the data optimization module includes: an antenna baseline optimization submodule, a Lomb-Scargle spectrum analysis optimization submodule, a dual-antenna combination optimization submodule and a tropospheric error optimization submodule, wherein:

[0033] The antenna baseline optimization submodule is used to correct the installation deviation between the direct and reflective antennas to ensure the accuracy of the inversion height reference;

[0034] The Lomb-Scargle spectrum analysis optimization submodule is used to introduce physical model constraints related to elevation angles and make rationality judgments on the main frequency results to suppress pseudo-peak interference and improve the credibility of the results;

[0035] The dual-antenna combination optimization submodule is used to adaptively fuse the data of the direct and reflected paths according to the difference between the satellite orientation and the antenna receiving direction, thereby improving the inversion continuity in blind areas or weak signal conditions;

[0036] The tropospheric error optimization submodule is used to simplify the complex modeling process of the traditional dry component and wet component by establishing a delay correction method based on an empirical model, directly calculate the total delay of the GNSS signal propagating in the troposphere, and perform real-time compensation for the inversion altitude.

[0037] Preferably, the installation deviation corrected by the antenna baseline optimization submodule includes: the vertical height difference H' of the antenna centroid of the dual antenna structure satisfies the following formula:

[0038] H′=H-H1-H2

[0039] H1=L1sin(θ1);

[0040] H2=L2sin(θ2)

[0041] Where H1 and H2 are the centroid height offsets of the two antennas relative to the vertical direction, L1 and L2 are the distances from the two antennas to the fixed point, and θ1 and θ2 are the installation inclination angles.

[0042] Preferably, the processing process of the Lomb-Scargle spectrum analysis optimization submodule includes:

[0043] In the process of extracting the main frequency, the standard Lomb-Scargle method is used to perform spectrum analysis on the signal-to-noise ratio sequence of the reflected signal, and the maximum peak in the power spectrum is preliminarily identified, and its corresponding frequency is used as the candidate main frequency f cand ;

[0044] Introduce the physical modeling relationship between the reflection frequency and the satellite elevation angle θ to calculate the expected main frequency f expected (θ):

[0045]

[0046] Where h is the known or estimated reflection height, and λ is the GNSS carrier wavelength;

[0047] The candidate main frequency f cand and the expected main frequency f expected (θ) and calculate its frequency consistency score S:

[0048]

[0049] When the score S is higher than the set threshold, the main frequency is reasonable and adopted; otherwise, the secondary peak is used to refit or the candidate main frequency is marked as low confidence until the score S is higher than the set threshold, and the verified main frequency f is output. final and its rating S.

[0050] Preferably, the processing process of the dual-antenna combination optimization submodule includes:

[0051] Based on the real-time analysis of satellite azimuth and elevation information, combined with the main lobe direction parameters of the reflector antenna, the deviation between the satellite incident direction and the antenna receiving direction is dynamically calculated;

[0052] When the deviation exceeds the preset tolerance range, or the quality of the reflected signal is detected to be degraded, it is determined that the current reflection path does not meet the conditions for high-quality inversion, and the direct channel is started as an auxiliary input for inversion; otherwise, the reflected signal is used for inversion and the inversion result is output.

[0053] Preferably, the total delay Delay of the GNSS signal propagating in the troposphere calculated by the troposphere error optimization submodule istrop for:

[0054]

[0055] Where h is the inversion height of the reflected signal, θ is the satellite elevation angle, and k and b are empirical fitting coefficients.

[0056] Preferably, the data communication upload module is also used to provide receiver operating status, data quality identification and historical data playback functions.

[0057] Compared with the prior art, the advantages of the present invention are:

[0058] 1. Improve GNSS reflected signal reception performance and interference fringe quality: Existing GNSS ground-based systems often use a single choke antenna structure, or a dual-antenna structure with unrestricted reflector antenna polarization. This makes it difficult to effectively receive signals that undergo polarization degradation or rotation after reflection from the ground. Especially at low elevation angles, the reflected signal is easily blocked or attenuated, resulting in weak GNSS-IR interference fringe signals and low inversion accuracy. This application constructs a dual-polarized antenna structure consisting of a choke positioning antenna and a horizontally polarized (HP) reflector antenna, optimizing the reception performance of the direct and reflected paths respectively, significantly enhancing inversion accuracy.

[0059] 2. Build a unified modeling and compensation mechanism for multi-source errors: GNSS-R snow depth retrieval is subject to multiple errors, and existing systems lack a unified processing framework, which affects the stability of the retrieval. This system addresses the stability and accuracy issues of GNSS-R snow depth retrieval under conditions of signal failure, equipment installation errors, and atmospheric interference, filling the gap in the existing dual-antenna GNSS-R system's lack of a systematic error compensation solution.

[0060] 3. Achieve integrated multi-parameter collaborative observation capabilities: Existing systems have fragmented functions and are unable to meet the needs of multi-parameter joint inversion. This application designs a detection system that integrates GNSS-R snow thickness measurement, ionospheric TEC estimation, and tropospheric ZTD derivation, improving system integration and environmental adaptability, and simplifying deployment and data fusion processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a block diagram of the GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual-polarized antenna structure of the present invention;

[0062] Figure 2 This is the structural block diagram of the GNSS signal acquisition module;

[0063] Figure 3 This is the structural diagram of the tropospheric water vapor calculation module;

[0064] Figure 4 This is the structural diagram of the ionospheric parameter calculation module;

[0065] Figure 5 This is the structural diagram of the snow thickness inversion module;

[0066] Figure 6 This is the overall structural diagram of the data optimization module;

[0067] Figure 7 It is a block diagram of the dual antenna placement structure;

[0068] Figure 8 This is the flow chart of the Lomb-Scargle spectrum analysis optimization module;

[0069] Figure 9 It is the flow chart of the dual antenna combination optimization module;

[0070] Figure 10 shows the height measurement results of different polarized antennas and the positioning antenna (choke ring antenna). Figure 10(a) shows the height comparison between the horizontally polarized antenna and the positioning antenna; Figure 10(b) shows the height comparison between the vertically polarized antenna and the positioning antenna; and Figure 10(c) shows the height comparison between the circularly polarized antenna and the positioning antenna.

[0071] Figure 11 The comparison between the average reference height of antennas with different polarizations and the GNSS-IR height measurement results;

[0072] Figure 12 is the root mean square error (RMSE) between the altitude measurement results of different polarization antennas at the reference altitude and the GNSS-IR altitude. DETAILED DESCRIPTION

[0073] The present invention provides a GNSS snow thickness and atmospheric parameter joint detection system, such as Figure 1 As shown in the figure, the system adopts a modular layered design, consisting of four main functional layers: GNSS signal acquisition module, meteorological auxiliary acquisition module, data processing module, and data communication upload module. By integrating GNSS direct and reflected signals with meteorological observation data, the system achieves simultaneous perception and calculation of multiple environmental parameters such as snow depth, ionospheric disturbances, and tropospheric water vapor content. It boasts strong real-time performance, multi-parameter integration, and a high degree of automation.

[0074] The system's front-end acquisition modules include a GNSS signal acquisition module and a meteorological auxiliary acquisition module. The GNSS signal acquisition module uses a dual-antenna structure to receive both direct and reflected GNSS signals, capturing interference fringes in the reflected signals for subsequent snow depth calculations and atmospheric parameter inversion. The meteorological auxiliary acquisition module, primarily a weather station, is responsible for acquiring auxiliary observational information such as temperature, humidity, and air pressure to improve the accuracy of water vapor calculations and provide support for data optimization and model adjustments.

[0075] The data processing module is the core of the system and consists of four submodules: the tropospheric water vapor calculation module, the ionospheric parameter calculation module, the snow thickness retrieval module, and the data optimization module. The tropospheric water vapor calculation module calculates the zenith delay parameter based on GNSS observation data and retrieves the PWV. The ionospheric parameter calculation module analyzes the phase delay of multi-frequency signals to obtain TEC values ​​and scintillation index. The snow thickness retrieval module uses a dominant frequency extraction method based on Lomb-Scargle spectrum analysis, combined with elevation angle changes to estimate the reflector height, ultimately determining snow thickness.

[0076] To further enhance measurement accuracy in complex terrain and multipath interference environments, the system incorporates a data optimization module. This module includes four sub-functions: antenna baseline optimization, Lomb-Scargle spectrum analysis optimization, dual-antenna combination optimization, and tropospheric error optimization. These sub-modules work together to extract reflected signals and perform altitude inversion. The antenna baseline optimization submodule is used to correct the installation error and reference plane offset between the direct and reflected antennas, ensuring that the altitude measurement results are based on a unified reference plane. The Lomb-Scargle spectrum analysis optimization submodule introduces a physical model related to the elevation angle to make a reasonable judgment on the main peak of the spectrum, effectively suppressing "pseudo-peak" interference and improving the accuracy and stability of spectrum extraction. The dual-antenna combination optimization submodule adaptively selects or fuses the direct and reflected signal paths based on the difference between the satellite direction and the antenna field of view, improving the continuity and robustness of the inversion results in blind spots or weak signal environments. The tropospheric error optimization submodule directly estimates the total propagation delay of the GNSS signal in the troposphere by establishing a delay correction method based on an empirical model, and dynamically compensates the inverted altitude, further improving the altitude measurement accuracy and system practicality.

[0077] The system's back-end data communication upload module organizes all processed key parameters, such as snow depth, TEC, and PWV, into standardized data products, supporting real-time transmission to servers or user terminals via wired or wireless interfaces. This module also provides receiver operating status, data quality indicators, and historical data playback, making it suitable for long-term field deployment and remote operation and maintenance management.

[0078] The technical solution of the present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0079] Example

[0080] The embodiment of the present invention proposes a GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual polarized antenna structure. Figure 1 As shown, it is described in detail below.

[0081] 1GNSS receiving and antenna configuration module

[0082] like Figure 2 As shown, the GNSS signal acquisition module consists of two antenna channels: a direct antenna and a reflector antenna. The direct antenna utilizes a high-gain choke structure to receive direct signals from GNSS satellites. This antenna exhibits excellent multipath suppression and is primarily used for the precise calculation of parameters such as the ionospheric total electron content (TEC) and tropospheric water vapor. It can also serve as a reference signal source for reflected signal compensation and quality assessment. The reflector antenna is a horizontally polarized reflector antenna, designed to receive GNSS signal reflection paths from the ground or snowy surfaces. Its horizontal polarization design enhances its response to surface reflection components, making it particularly suitable for environments with varying reflective polarization in snow-covered scenes. This antenna, combined with the GNSS-IR algorithm, can be used for high-precision snow thickness inversion.

[0083] 2 Tropospheric water vapor calculation module

[0084] like Figure 3 As shown in the figure, the tropospheric water vapor calculation module is mainly used to invert the precipitable water volume (PWV) based on GNSS observation data. The overall process includes several key steps. First, the system solves the GNSS data to calculate the tropospheric zenith total delay (ZTD). Then, based on meteorological data such as station pressure and temperature, an atmospheric model is used to estimate the zenith dry delay (ZHD). The two are then subtracted to obtain the zenith wet delay (ZWD), which reflects the influence of water vapor. Finally, by introducing an appropriate wet delay conversion coefficient, the ZWD is converted to precipitable water volume (PWV), thereby achieving accurate inversion of atmospheric water vapor content. This module is a core component of the GNSS atmospheric sounding system, providing key parameter support for meteorological monitoring, weather forecasting, and tropospheric delay correction.

[0085] 3. Ionospheric parameter calculation module

[0086] like Figure 4As shown in the figure, the ionospheric parameter calculation module is mainly used to obtain information about the ionospheric disturbances encountered by GNSS signals during propagation, including key parameters such as total electron content (TEC), ionospheric amplitude scintillation, and phase scintillation. First, the system calculates the frequency delay difference based on dual-frequency GNSS observation data to resolve the ionospheric total electron content (TEC), a parameter that reflects the overall changes in electron density in the ionosphere. Next, the system continuously monitors the intensity of the GNSS signal and calculates the S4 index by calculating the ratio of the standard deviation of the signal intensity sequence to the mean value to assess the degree of amplitude scintillation. At the same time, a short-timescale differential operation is performed on the carrier phase to extract the phase fluctuation characteristics, which reflects the intensity of ionospheric phase scintillation. This module can be used to analyze ionospheric disturbances, space weather changes, and their impact on GNSS measurement accuracy, providing key support for ionospheric correction and anomaly monitoring.

[0087] 4Snow thickness inversion module

[0088] like Figure 5 As shown in the figure, the snow thickness inversion module implements GNSS-IR-based snow thickness inversion. It consists of three parts: SNR data extraction, Lomb-Scargle spectrum analysis, and reflection height calculation and snow depth inversion. First, the signal-to-noise ratio (SNR) sequence is extracted from the GNSS observation data as the raw input for interference fringe analysis. The SNR sequence is then spectrally extracted using the Lomb-Scargle spectrum analysis method to identify the interference fringe frequency corresponding to the dominant frequency. Finally, the distance difference between the antenna and the reflector is inverted based on the interference frequency, combined with the system's geometric parameters and the electrical properties of the snow surface. The snow thickness is estimated based on the difference in the distance between the antenna and the reflector before and after snowfall.

[0089] 5Data Optimization Module

[0090] Data optimization modules such as Figure 6 As shown, it includes four submodules: antenna baseline optimization, Lomb-Scargle spectrum analysis optimization, dual-antenna combination optimization, and tropospheric error optimization, aiming to improve the overall accuracy and stability of GNSS reflection altitude measurement. The antenna baseline optimization module is used to correct the installation deviation between the direct and reflective antennas to ensure the accuracy of the inversion altitude reference. The Lomb-Scargle spectrum analysis introduces physical model constraints related to the elevation angle to make reasonable judgments on the main frequency results, thereby suppressing "pseudo-peak" interference and improving the credibility of the results. The dual-antenna combination optimization module adaptively fuses the direct and reflective channel data based on the difference between the satellite azimuth and the antenna receiving direction, improving the inversion continuity in blind areas or weak signal conditions. The tropospheric error optimization module simplifies the complex modeling process of the traditional dry and wet components by establishing a delay correction method based on an empirical model, directly calculates the total delay of the GNSS signal propagating in the troposphere, and compensates the inversion altitude in real time.

[0091] 5.1 Antenna Polarization Optimization Module

[0092] like Figure 7 As shown in the figure, since the GNSS direct antenna and reflector antenna are installed on the same pole, but there is a certain tilt angle between the two, a vertical offset is formed between the center of mass of the antenna and the horizontal direction of the pole. Assuming the nominal height between the antennas is H, and the vertical projection differences formed by the direct antenna and the reflector antenna due to the tilt angle are H1 and H2, respectively, as shown in the figure. Figure 7 As shown in the figure, since the GNSS direct antenna and reflector antenna are installed on the same pole, but there is a certain tilt angle between them, resulting in a vertical offset between the antenna center of mass and the horizontal direction of the pole. Assuming the nominal height between the antennas is H, and the vertical projection differences formed by the direct antenna and reflector antenna due to the tilt angle are H1 and H2 respectively, the actual vertical height difference between the two antenna center of mass should be corrected to:

[0093] H'=H-H1-H2

[0094] The vertical offsets are:

[0095] H1=L1sin(θ1);

[0096] H2=L2sin(θ2)

[0097] Where H1 and H2 are the centroid height offsets of the two antennas relative to the vertical direction, L1 and L2 are the distances from the antennas to the fixed points, and θ1 and θ2 are the installation inclination angles.

[0098] Failure to account for this vertical offset in the reflection height calculation or snow depth inversion process can lead to systematic inversion errors. Therefore, this geometric error must be modeled and corrected by the Antenna Baseline Optimization module to ensure the correct geometric relationship between the GNSS reflection path and the antenna reference point, thereby improving height measurement accuracy.

[0099] 5.2 Lomb-Scargle Spectrum Analysis Optimization Module

[0100] like Figure 8 As shown in the figure, in the main frequency extraction process, the standard Lomb-Scargle method is first used to perform spectrum analysis on the SNR sequence of the reflected signal, and the maximum peak in the power spectrum is preliminarily identified, and its corresponding frequency is used as the candidate main frequency f cand Then, the physical modeling relationship between the reflection frequency and the satellite elevation angle θ is introduced, and the theoretical expression Where h is the known or estimated reflection height, λ is the GNSS carrier wavelength, and the expected frequency at this elevation angle is calculated. The system compares the candidate main frequency with the expected main frequency and calculates their frequency consistency score:

[0101] When the score S is higher than the set threshold, the main frequency is considered physically reasonable and is adopted; if the score is low, the system can try to refit using the secondary peak or mark the sample as low confidence. Finally, the system outputs the verified main frequency f final and its credibility score, providing quality-controlled frequency input for subsequent reflection height calculation and snow thickness inversion.

[0102] 5.3 Dual Antenna Combination Optimization Module

[0103] like Figure 9 As shown in the figure, the dual-antenna combination optimization module is designed to solve the problem of reflected signal blind spots or weak signals caused by antenna direction limitations, satellite trajectory changes or obstruction environments in GNSS reflection measurements. Based on the real-time analyzed satellite azimuth and elevation information, combined with the main lobe direction parameters of the reflecting antenna, the module dynamically calculates the deviation between the satellite incident direction and the antenna receiving direction. When the deviation exceeds the preset tolerance range, or the system detects a decrease in the quality of the reflected signal (such as blurred fringes, weakened SNR, etc.), the module will automatically determine that the current reflection path does not have high-quality inversion conditions and start the direct channel as an auxiliary input. By preferentially switching between the direct and reflected channels, the system can maintain the continuity and stability of the inversion results even when the signal is not ideal or the reflection path fails. This module significantly improves the collaborative ability of the dual-antenna architecture and is a key technical link in achieving all-weather, all-round and stable height measurement.

[0104] 5.4 Tropospheric Error Optimization Module

[0105] In this system, to simplify the tropospheric delay error modeling process and avoid the complexity of modeling the dry and wet components separately, a tropospheric delay correction method based on an empirical model is adopted. This method directly calculates the total delay caused by the signal propagation path in the troposphere based on standard atmospheric refraction theory. The correction formula is as follows:

[0106]

[0107] Where h represents the inversion height of the reflected signal, θ is the elevation angle of the satellite, k and b are empirical fitting coefficients, and their values ​​are: k = 3.166 × 10 -5 ,b=1.025×10 -2 , obtained by fitting measured data. This model can effectively compensate for the signal delay error caused by the troposphere without relying on real-time meteorological data. It is particularly suitable for application scenarios such as GNSS-R snow inversion, which require high accuracy but fast model response.

[0108] It is worth noting that in the embodiment of the above system, the modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0109] Technical effects:

[0110] This paper proposes a GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual-polarized antenna structure. While inheriting the existing GNSS ground-based system's monitoring functions for key atmospheric parameters such as the ionospheric total electron content (TEC) and the tropospheric zenith delay (ZTD), it innovatively introduces a dual-antenna architecture consisting of a choke ring positioning antenna (RHCP) and a horizontally polarized reflector antenna (HP), and also designs supporting GNSS-R signal interference analysis and snow thickness inversion processing modules.

[0111] The system achieves real-time, high-precision, multi-parameter coordinated detection of snow depth using GNSS-R technology on a ground-based platform, overcoming the technical bottlenecks of traditional single-antenna systems, such as polarization conflicts, low reflected signal reception efficiency, and inseparable signal paths. Furthermore, the system integrates the advantages of multiple detection methods, including GNSS positioning, interferometric fringe spectrum analysis, meteorological perception, and multi-channel observation. The system offers the following significant technical advantages and benefits in terms of structural configuration, error compensation, and data fusion:

[0112] 1. Accuracy is significantly improved, with system error better than ±1.5cm

[0113] The system utilizes a dual-antenna architecture, effectively avoiding shielding of reflected signals by the choke coil structure through independent reflection channels. Terrain reflection compensation and antenna height correction algorithms are also incorporated, along with an adaptive adjustment mechanism based on a historical inversion model. This allows for error control within ±1.5cm under complex terrain and variable snow conditions. Comparative field data demonstrates that, compared to traditional single-antenna choke coil receivers, the dual-antenna architecture offers significant advantages in both inversion accuracy and interference fringe clarity. Specifically, the reflection channel using a horizontally polarized antenna exhibits greater signal responsiveness and stability in snow layer detection.

[0114] This figure shows three GNSS reflector antennas with different polarizations (horizontal, vertical, and circular) combined with a positioning antenna (receiving direct signals) to measure the ground-to-antenna height in the same experimental scenario. Comparison with a known reference altitude verifies the accuracy of reflector antennas with different polarizations in GNSS-IR altitude measurements.

[0115] Figure 10 shows the height measurement results for different polarized antennas and a positioning antenna (choke ring antenna). Figure 10(a) compares the inversion heights of the horizontally polarized antenna and the positioning antenna; Figure 10(b) compares the inversion heights of the vertically polarized antenna and the positioning antenna; and Figure 10(c) compares the inversion heights of the circularly polarized antenna and the positioning antenna. Figures 10(a), 10(b), and 10(c) show the Lomb-Scargle power spectrum comparisons of the inversion heights obtained for the different polarized reflector and positioning antenna combinations. It is clear that the LSP spectra of the horizontally, vertically, and circularly polarized reflector antennas exhibit sharper, more concentrated energy, and higher peaks at the main frequency than when using the choke ring positioning antenna alone. This indicates stronger reception of the reflected signal, clearer interference fringes, and more pronounced inversion results. The horizontally polarized antenna, in particular, exhibits the most prominent main frequency peak and the narrowest spectrum, demonstrating high sensitivity to ground-reflected signals and excellent frequency resolution. The above results verify the effectiveness of using a dual-antenna architecture and a horizontally polarized reflective antenna in the GNSS-IR altimeter system, providing key support for improving the accuracy of snow thickness inversion.

[0116] Figure 11 and 12 Table 1 shows the statistical data for altitude measurements using various polarization antennas, including the deviation (mean difference), root mean square error (RMSE), and standard deviation between the mean inverted altitude and the reference altitude for each antenna type. It is clear that horizontally polarized antennas perform best in altitude measurement accuracy. Their mean error is minimal, close to the reference altitude, and their RMSE and standard deviation are significantly lower than those of other polarization methods, demonstrating their significant advantages in reflected signal reception stability and fringe extraction accuracy. This result validates the rationale and necessity of selecting horizontally polarized antennas as GNSS reflected signal receiving antennas, providing a clear technical basis for subsequent system design.

[0117] Table 1 Comparison of GNSS-IR height measurement results and stability of various polarization antennas

[0118] Antenna Type Reference altitude Actual height Height measurement accuracy Standard deviation Positioning antenna 2.2100m 2.0750m 0.1350m 0.4288 Circularly polarized antenna 1.9100m 2.1040m 0.1940m 0.4783 vertically polarized antenna 1.9100m 1.8840m 0.026m 0.1105 Horizontally polarized antenna 1.9100m 1.8980m 0.012m 0.0792

[0119] 2. System-level snow inversion error optimization strategy

[0120] This paper systematically proposes an optimization scheme for snow thickness inversion under a GNSS-R dual-antenna configuration, integrating multiple key strategies to address altitude error issues in complex environments. This strategy includes four core components: first, an antenna baseline error modeling and correction method, which corrects for installation deviations and datum plane inconsistencies between the reflective and direct antennas, thereby reducing structural altitude errors. Second, an improved Lomb-Scargle spectrum analysis method, which introduces an elevation angle constraint model and a spectral line smoothing mechanism to improve the stability and anti-interference capability of reflection main frequency extraction. Third, a dual-antenna signal fusion mechanism, which adaptively selects or weights the direct and reflected channels based on the relationship between the satellite field of view and antenna orientation, effectively enhancing inversion continuity under weak signal or obstruction conditions. Fourth, a rapid tropospheric delay compensation model based on elevation angle and geometric height simplifies the traditional atmospheric modeling process and enables real-time correction of GNSS signal path delay. The coordinated operation of these modules significantly improves the accuracy and adaptability of snow inversion while ensuring the compactness of the system structure, filling the technical gap in existing GNSS-R altimetry technology, which lacks a systematic error optimization solution under a dual-antenna architecture.

[0121] 3. The system has high integration and multi-parameter integrated observation capabilities

[0122] This system features an open architecture and standardized interfaces. It can be connected in parallel to a weather station module to obtain key meteorological parameters such as temperature, humidity, and air pressure, enabling the joint inversion of GNSS signal parameters and atmospheric environmental information. The host computer is an integrated measurement receiver capable of simultaneously processing signals received by both direct and reflected antennas to calculate key environmental parameters such as the ionospheric total electron content (TEC), ionospheric scintillation index, tropospheric zenith wet delay (ZTD), precipitable water volume (PWV), and snow depth. The system supports Ethernet transmission, enabling real-time upload of processing results to a server. In scenarios without a PC, the host computer can also independently complete data storage and remote transmission tasks via a network interface.

[0123] Finally, it should be noted that the above embodiments are intended only to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions to the technical solutions of the present invention do not depart from the spirit and scope of the technical solutions of the present invention and are intended to be encompassed by the claims of the present invention.

Claims

1. A GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual polarized antenna structure, characterized in that: include: GNSS signal acquisition module, meteorological auxiliary acquisition module, data processing module and data communication upload module, among which, The GNSS signal acquisition module adopts a dual polarized antenna structure to receive GNSS signals from direct and reflected paths respectively, and capture the changes in interference fringes in the reflected signals; The meteorological auxiliary collection module is used to collect auxiliary observation information including temperature, humidity and station pressure; The data processing module is used to process the GNSS signals of the direct and reflected paths in parallel, combine auxiliary observation information, and based on the snow inversion error optimization strategy, correct the installation error and reference plane offset of the dual polarization antenna, introduce physical model constraints related to elevation angle, suppress pseudo-peak interference, adaptively fuse the processed direct and reflected path data, and compensate the inversion height in real time by establishing a delay correction method based on an empirical model to achieve the inversion of snow cover and the joint calculation of ionospheric total electron content, ionospheric scintillation parameters, tropospheric zenith delay, precipitable water content, and snow thickness; The data communication upload module is used to organize the processed key parameters into standardized data products and transmit them to the server or user terminal in real time.

2. The GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual polarized antenna structure according to claim 1 is characterized in that: The dual polarized antenna structure of the GNSS signal acquisition module includes: a direct antenna and a horizontally polarized reflective antenna; wherein, The direct antenna adopts a high-gain choke structure and is used to receive direct signals from GNSS satellites; The horizontally polarized reflective antenna is used to receive the GNSS signal reflection path from the ground surface or the snow surface.

3. The GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual-polarized antenna structure according to claim 1 is characterized in that: The data processing module includes: a tropospheric water vapor calculation module, an ionospheric parameter calculation module, a snow thickness inversion module and a data optimization module; wherein, The tropospheric water vapor calculation module is used to invert the precipitable water content based on GNSS observation data and auxiliary observation information; The ionospheric parameter calculation module is used to obtain the ionospheric disturbance information received during the propagation of the GNSS signal; The snow thickness inversion module is used to realize snow thickness inversion based on interferometry; The data optimization module is used to improve the overall accuracy and stability of GNSS reflection height measurement based on the snow inversion error optimization strategy.

4. The GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual-polarized antenna structure according to claim 3 is characterized in that: The processing process of the tropospheric water vapor calculation module includes: Solve the GNSS data and calculate the total tropospheric zenith delay (ZTD); Zenith dry delay (ZHD) is estimated using an atmospheric model based on temperature, humidity, and station pressure. Subtract ZTD from ZHD to obtain the zenith wet delay ZWD reflecting the influence of water vapor; By introducing a set wet delay conversion coefficient, ZWD is converted into precipitable water volume (PWV), thus achieving accurate inversion of atmospheric water vapor content.

5. The GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual polarized antenna structure according to claim 3 is characterized in that: The processing process of the ionospheric parameter calculation module includes: Calculate the frequency delay difference based on dual-frequency GNSS observation data and solve the ionospheric total electron content (TEC); The strength of the GNSS signal is continuously monitored, and the S4 index is obtained by calculating the ratio of the standard deviation of the signal strength series to the mean value to evaluate the degree of amplitude scintillation. At the same time, short-time-scale differential operations are performed on the carrier phase to extract the phase fluctuation characteristics to reflect the ionospheric phase scintillation intensity.

6. The GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual polarized antenna structure according to claim 3 is characterized in that: The processing of the snow thickness inversion module includes: Extract the signal-to-noise ratio sequence from the GNSS observation data as the original input for interference fringe analysis; The Lomb-Scargle spectrum analysis method is used to extract the spectrum of the signal-to-noise ratio sequence to identify the interference fringe frequency corresponding to the main frequency; According to the interference frequency combined with the system geometric parameters and the electrical characteristics of the snow surface, the distance difference between the antenna and the reflecting surface is inverted, and the snow thickness is estimated based on the difference in the distance between the antenna and the reflecting surface before and after snowfall.

7. The GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual-polarized antenna structure according to claim 3 is characterized in that: The data optimization module includes: antenna baseline optimization submodule, Lomb-Scargle spectrum analysis optimization submodule, dual antenna combination optimization submodule and tropospheric error optimization submodule, wherein, The antenna baseline optimization submodule is used to correct the installation deviation between the direct and reflective antennas to ensure the accuracy of the inversion height reference; The Lomb-Scargle spectrum analysis optimization submodule is used to introduce physical model constraints related to elevation angles and make rationality judgments on the main frequency results to suppress pseudo-peak interference and improve the credibility of the results; The dual-antenna combination optimization submodule is used to adaptively fuse the data of the direct and reflected paths according to the difference between the satellite orientation and the antenna receiving direction, thereby improving the inversion continuity in blind areas or weak signal conditions; The tropospheric error optimization submodule is used to simplify the complex modeling process of the traditional dry component and wet component by establishing a delay correction method based on an empirical model, directly calculate the total delay of the GNSS signal propagating in the troposphere, and perform real-time compensation for the inversion altitude.

8. The GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual-polarized antenna structure according to claim 7 is characterized in that: The installation deviation corrected by the antenna baseline optimization submodule includes: the vertical height difference H' of the antenna center of mass of the dual antenna structure, which satisfies the following formula: H'=H-H1-H2 H1=L1sin(θ1); H2=L2sin(θ2) Where H1 and H2 are the centroid height offsets of the two antennas relative to the vertical direction, L1 and L2 are the distances from the two antennas to the fixed point, and θ1 and θ2 are the installation inclination angles.

9. The GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual-polarized antenna structure according to claim 7 is characterized in that: The processing process of the Lomb-Scargle spectrum analysis optimization submodule includes: In the process of extracting the main frequency, the standard Lomb-Scargle method is used to perform spectrum analysis on the signal-to-noise ratio sequence of the reflected signal, and the maximum peak in the power spectrum is preliminarily identified, and its corresponding frequency is used as the candidate main frequency f cand ; Introduce the physical modeling relationship between the reflection frequency and the satellite elevation angle θ to calculate the expected main frequency f expected (θ): Where h is the known or estimated reflection height, and λ is the GNSS carrier wavelength; The candidate main frequency f cand and the expected main frequency f expected (θ) and calculate its frequency consistency score S: When the score S is higher than the set threshold, the main frequency is reasonable and adopted; otherwise, the secondary peak is used to refit or the candidate main frequency is marked as low confidence until the score S is higher than the set threshold, and the verified main frequency f is output. final and its score S.

10. The GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual-polarized antenna structure according to claim 7, characterized in that: The processing process of the dual antenna combination optimization submodule includes: Based on the real-time analysis of satellite azimuth and elevation information, combined with the main lobe direction parameters of the reflector antenna, the deviation between the satellite incident direction and the antenna receiving direction is dynamically calculated; When the deviation exceeds the preset tolerance range, or the quality of the reflected signal is detected to be degraded, it is determined that the current reflection path does not meet the conditions for high-quality inversion, and the direct channel is started as an auxiliary input for inversion; otherwise, the reflected signal is used for inversion and the inversion result is output.

11. The GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual-polarized antenna structure according to claim 7, characterized in that: The total delay Delay of the GNSS signal propagating in the troposphere calculated by the tropospheric error optimization submodule trop for: Where h is the inversion height of the reflected signal, θ is the satellite elevation angle, and k and b are empirical fitting coefficients.

12. The GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual-polarized antenna structure according to claim 7, characterized in that: The data communication upload module is also used to provide receiver operating status, data quality identification and historical data playback functions.

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