A GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual-isolated polarization antenna structure

Abstract

The application discloses a GNSS-R snow thickness and atmospheric parameter joint detection system based on a double-isopolar antenna structure, which comprises a GNSS signal acquisition module, a meteorological auxiliary acquisition module, a data processing module and a data communication upload module.

Description

TECHNICAL FIELD

[0001] The application belongs to the global navigation satellite system (GNSS) environmental monitoring technology direction in the navigation and remote sensing measurement cross field, and particularly relates to a GNSS-R snow thickness and atmospheric parameter joint detection system based on a double-isopolar antenna structure. BACKGROUND

[0002] Global navigation satellite system (GNSS) has been widely used in earth science, meteorological monitoring and disaster warning and other fields due to its high precision, all-weather and global coverage. In traditional applications, GNSS is mainly used for positioning and timing services; in recent years, the rapid development of GNSS reflection measurement (GNSS-R) and interference measurement (GNSS-IR) technology makes its potential in environmental parameter inversion such as snow, water level, atmospheric water vapor and ionospheric disturbance increasingly prominent. GNSS-IR technology can invert the height difference between the antenna and the ground by receiving the interference wave formed by the direct signal and the signal reflected by the ground, and analyzing the periodic component in the signal-to-noise ratio (SNR), so as to realize the low-cost, passive and high-precision measurement of snow thickness. Although the technology has been preliminarily applied in scientific research, the existing engineering system still has the following outstanding problems in structure design and signal reception:

[0003] First, the existing ground-based GNSS system lacks targeted optimization in the design of reflection signal receiving structure and polarization characteristics, resulting in unsatisfactory inversion accuracy and interference fringe quality. Most systems still use single high-gain right-hand circularly polarized (RHCP) choke ring antenna, which helps to improve the quality of direct signal and suppress multipath interference, but it is difficult to effectively receive reflected signals. In the GNSS-IR scene, the signal often undergoes depolarization, rotation and polarization coupling after reflection on the ground, which significantly reduces the receiving efficiency of the traditional RHCP antenna on the reflection path, making it difficult to extract the main frequency of the interference fringe and the quality of the interference fringe is poor. In addition, the choke ring structure may even partially shield the reflected wave, making it impossible to enter the receiving system under low elevation conditions. Moreover, the same antenna cannot simultaneously optimize the gain direction of both direct and reflected signals. There is currently no special dual-antenna structure design scheme for GNSS-IR, and there is also a lack of systematic modeling and comparative analysis of the response characteristics and signal quality of horizontal polarization, vertical polarization and circular polarization in the reflection path. In the reflection of planar dielectric interface, the Brewster angle reflection coefficient of vertical polarization tends to zero and the phase changes dramatically, which is not conducive to interferometric height measurement. Circular polarization is prone to chiral inversion or elliptical after reflection, which requires the decomposition of H / V components and the consideration of phase difference and amplitude imbalance, increasing the difficulty of reception and processing. The reflected amplitude of horizontal polarization increases gently with the incident angle, and the phase remains constant, especially under low elevation conditions, where the reflected intensity is the highest. The relationship between the frequency of the multipath interference fringe and the height of the reflection surface is stable and clear, making it the best choice for GNSS-IR snow depth measurement.

[0004] Second, there is a lack of systematic error compensation strategies for GNSS-IR snow inversion to meet the stable height measurement requirements under multi-source interference. The error sources in GNSS-R snow inversion are complex, including antenna baseline error, spatial geometric deviation caused by inconsistent antenna installation position and elevation / azimuth angle, pseudo-peak interference in spectral analysis, low-elevation signal distortion, tropospheric delay error, and signal attenuation and multipath error under shielding conditions. In addition, in dynamic observation scenarios, satellite orbit parameter uncertainty and time-varying characteristics of the reflection surface also have a significant impact on the inversion accuracy. However, existing systems generally use a decentralized processing strategy, making it difficult to model and correct these multi-source errors collaboratively. There is currently a lack of multi-error fusion modeling system for GNSS-IR, especially in terms of dual-antenna baseline error correction, Lomb-Scargle spectrum analysis stability enhancement, elevation / azimuth angle constraint optimization, and multi-antenna data joint solution. The above problems result in poor height measurement stability of existing systems under extreme conditions such as complex terrain, signal shielding, and low-elevation observation, and the inversion results are highly volatile, making it difficult to meet the practical needs of high precision and high robustness.

[0005] Third, functional modules are separated, lacking integrated multi-parameter collaborative observation capabilities. Most current GNSS ground-based systems are developed using dedicated approaches, such as high-frequency sampling and receiving systems for ionospheric total electron content (TEC) inversion, conventional meteorological station-type GNSS devices for tropospheric water vapor (PWV) inversion, and snow cover inversion modules based on SNR extraction. These systems operate independently, with inconsistent data interfaces and processing logic, failing to form a standardized, integrated operational framework. In practical deployments, multiple sets of equipment are often required to obtain various environmental parameters, leading to increased costs, difficulties in data synchronization, and low space utilization. Existing technologies have not proposed a fusion architecture integrating GNSS-IR inversion, ionospheric disturbance analysis, and tropospheric wet delay estimation, limiting the system's practicality and scalability in comprehensive environmental monitoring scenarios. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and propose a GNSS-R joint detection system for snow thickness and atmospheric parameters based on a dual heteropolarized antenna structure.

[0007] In view of this, the present invention proposes a GNSS-R joint detection system for snow thickness and atmospheric parameters based on a dual heteropolarized antenna structure, characterized in that it includes: a GNSS signal acquisition module, a meteorological auxiliary acquisition module, a data processing module, and a data communication and uploading module, wherein,

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

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

[0010] The data processing module is used to process GNSS signals from direct and reflected paths in parallel. Combined with auxiliary observation information, it corrects the installation error and reference plane offset of the dual heteropolarized antenna based on the snow inversion error optimization strategy. It introduces physical model constraints related to elevation angle to suppress false peak interference. It adaptively fuses the data from direct and reflected paths and compensates for the inversion height in real time by establishing a delay correction method based on an empirical model. This enables the inversion of snow cover and the joint calculation of total electron content in the ionosphere, ionospheric scintillation parameters, tropospheric zenith delay, precipitable water, 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 heteropolarized antenna structure of the GNSS signal acquisition module includes: a direct-fire antenna and a horizontally polarized reflector antenna; wherein,

[0013] The direct-fire antenna employs a high-gain choke coil structure to receive direct signals from GNSS satellites.

[0014] The horizontally polarized reflective antenna is used to receive the reflected path of GNSS signals from the ground surface or 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 precipitable water based on GNSS observation data and auxiliary observation information;

[0017] The ionospheric parameter calculation module is used to obtain information on ionospheric disturbances encountered during GNSS signal propagation.

[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 altimetry based on snow cover inversion error optimization strategies.

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

[0021] The GNSS data is processed to calculate the tropospheric zenith total delay (ZTD).

[0022] The zenith dry delay (ZHD) was estimated using an atmospheric model based on temperature, humidity, and station air pressure.

[0023] Subtracting ZTD from ZHD yields the zenith wet delay ZWD, which reflects the influence of water vapor.

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

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

[0026] The frequency delay difference was calculated based on dual-frequency GNSS observation data, and the total electron content (TEC) of the ionosphere was determined.

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

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

[0029] The signal-to-noise ratio sequence is extracted from GNSS observation data and used as the raw input for interferometric fringe analysis;

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

[0031] Based on the interference frequency, combined with the system's geometric parameters and the electrical characteristics of the snow surface, the distance difference between the antenna and the reflecting surface is retrieved, and the snow thickness is estimated based on the difference in 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 reflected 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 angle, make a reasonable judgment on the main frequency results, suppress false peak interference, and improve the reliability of the results.

[0035] The dual-antenna combination optimization submodule is used to adaptively fuse data from direct and reflected paths based on the difference between satellite azimuth and antenna receiving direction, thereby improving the inversion continuity under blind zone or weak signal conditions.

[0036] The tropospheric error optimization submodule is used to simplify the complex modeling process of traditional dry and wet components by establishing a delay correction method based on an empirical model, directly calculate the total delay of GNSS signal propagation 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, satisfying 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 of the two antennas from the fixed point, and θ1 and θ2 are their respective installation tilt angles.

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

[0043] During the main frequency extraction process, the standard Lomb-Scargle method was used to perform spectral analysis on the signal-to-noise ratio sequence of the reflected signal to preliminarily identify the maximum peak value in the power spectrum, and the frequency corresponding to it was selected as the candidate main frequency f. cand ;

[0044] By introducing a physical modeling relationship between the reflection frequency and the satellite elevation angle θ, the desired dominant frequency f is calculated. expected (θ):

[0045]

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

[0047] Candidate main frequency f cand With expected main frequency f expected (θ) is compared, and its frequency consistency score S is calculated:

[0048]

[0049] If the score S is higher than the set threshold, the dominant frequency is considered reasonable and adopted; otherwise, the candidate dominant frequency is refitted using a secondary peak or marked as low confidence until the score S is higher than the set threshold, at which point the validated dominant frequency f is output. final And its score S.

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

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

[0052] If the deviation exceeds the preset tolerance range, or if a decrease in the quality of the reflected signal is detected, it is determined that the current reflection path does not meet the conditions for high-quality inversion, and the direct channel is activated 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 of the GNSS signal propagating in the troposphere calculated by the tropospheric error optimization submodule is...trop for:

[0054]

[0055] Where h is the reflected signal inversion altitude, 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. Improving GNSS reflected signal reception performance and enhancing interference fringe quality: Existing GNSS ground-based systems often employ a single choke antenna structure, or, although a dual-antenna structure, the polarization of the reflecting antenna is not restricted. This makes it difficult to effectively receive signals that have undergone polarization degradation or rotation after reflection from the ground surface. Especially under low elevation angle conditions, reflected signals are easily blocked or attenuated, resulting in weak GNSS-IR interference fringe signals and low inversion accuracy. This application constructs a dual heteropolarized antenna structure consisting of a choke positioning antenna and a horizontally polarized (HP) reflecting antenna, optimizing the reception performance of both direct and reflected paths, significantly enhancing inversion accuracy.

[0059] 2. Constructing a unified modeling and compensation mechanism for multi-source errors: GNSS-R snow cover inversion is affected by various errors, and existing systems lack a unified processing framework, affecting inversion stability. This system systematically solves the stability and accuracy problems of GNSS-R snow cover thickness inversion under conditions of signal failure, equipment installation errors, and atmospheric interference, filling the gap in the lack of a systematic error compensation scheme in existing dual-antenna GNSS-R systems.

[0060] 3. Achieving integrated multi-parameter collaborative observation capability: Existing systems have fragmented functions, making it difficult to meet the requirements 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. Attached Figure Description

[0061] Figure 1 This is a block diagram of the GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual heteropolarized antenna structure, as described in this invention.

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

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

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

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

[0066] Figure 6 This is a block diagram of the overall structure of the data optimization module;

[0067] Figure 7 This is a block diagram of a dual-antenna placement structure;

[0068] Figure 8 This is the flowchart of the Lomb-Scargle spectral analysis optimization module;

[0069] Figure 9 This is a flowchart of the dual-antenna combination optimization module;

[0070] Figure 10 shows the height measurement results of different polarization antennas and positioning antennas (choke antennas). Among them, Figure 10(a) is a comparison of the height inversion of the horizontally polarized antenna and the positioning antenna; Figure 10(b) is a comparison of the height inversion of the vertically polarized antenna and the positioning antenna; and Figure 10(c) is a comparison of the height inversion of the circularly polarized antenna and the positioning antenna.

[0071] Figure 11 This is a comparison of the average reference height of antennas with different polarizations and the results of GNSS-IR altimetry measurements;

[0072] Figure 12 It is the root mean square error (RMSE) of different polarized antennas at the reference height and the GNSS-IR altimeter results. Detailed Implementation

[0073] This invention provides a GNSS snow thickness and atmospheric parameter joint detection system, such as... Figure 1 As shown, the system adopts a modular, layered design, consisting of four main functional layers: a GNSS signal acquisition module, a meteorological auxiliary acquisition module, a data processing module, and a data communication and uploading module. By integrating GNSS direct signals, reflected signals, and meteorological observation data, the system achieves simultaneous sensing and calculation of multiple environmental parameters such as snow thickness, ionospheric disturbance, and tropospheric water vapor content, possessing technical advantages such as strong real-time performance, multi-parameter integration, and high automation.

[0074] The system's front-end acquisition module includes a GNSS signal acquisition module and a meteorological auxiliary acquisition module. The GNSS signal acquisition module uses a dual-antenna structure to receive GNSS signals from both direct and reflected paths, capturing changes in interference fringes in the reflected signals for subsequent snow thickness calculations and atmospheric parameter inversion. The meteorological auxiliary acquisition module, primarily referring to the meteorological station, is responsible for acquiring auxiliary observation information such as temperature, humidity, and air pressure, improving the accuracy of water vapor calculations, and providing support for data optimization and model adjustment.

[0075] The data processing module is the core of the system, containing four sub-modules: a tropospheric water vapor calculation module, an ionospheric parameter calculation module, a snow thickness inversion module, and a data optimization module. Specifically, the tropospheric water vapor calculation module calculates the zenith delay parameter based on GNSS observation data and inverts the PWV; the ionospheric parameter calculation module analyzes the phase delay of multi-frequency signals to obtain the TEC value and scintillation index; and the snow thickness inversion module uses a dominant frequency extraction method based on Lomb-Scargle spectral analysis, combined with elevation angle changes, to estimate the reflector height and ultimately determine the snow thickness.

[0076] To further improve the system's measurement accuracy in complex terrain and multipath interference environments, a data optimization module has been introduced. This module includes four sub-functions: antenna baseline optimization, Lomb-Scargle spectrum analysis optimization, dual-antenna combination optimization, and tropospheric error optimization, which work together to extract the reflected signal and retrieve the altitude. The antenna baseline optimization submodule corrects installation errors and reference plane offsets between direct and reflected antennas, ensuring that altimetry results are based on a unified reference plane. The Lomb-Scargle spectrum analysis optimization submodule introduces a physical model related to elevation angle to make reasonable judgments on the main peak of the spectrum, effectively suppressing "false peak" interference and improving the accuracy and stability of spectrum extraction. The dual-antenna combination optimization submodule adaptively selects or merges direct and reflected signal paths based on the difference between satellite direction and antenna field of view, improving the continuity and robustness of inversion results in blind zones or weak signal environments. The tropospheric error optimization submodule directly estimates the total propagation delay of GNSS signals in the troposphere by establishing a delay correction method based on an empirical model and dynamically compensates for the inversion altitude, further improving altimetry accuracy and system practicality.

[0077] The system's backend data communication and upload module is responsible for organizing 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 identification, and historical data playback functions, making it suitable for long-term field deployment and remote operation and maintenance management.

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

[0079] Example

[0080] Embodiments of this invention propose a GNSS-R joint detection system for snow thickness and atmospheric parameters based on a dual heteropolarized antenna structure, such as... Figure 1 As shown below, this will be explained in detail.

[0081] 1GNSS Receiver 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 reflective antenna. The direct antenna employs a high-gain choke structure to receive direct signals from GNSS satellites. This antenna has excellent multipath suppression capabilities and is primarily used for precise calculation of parameters such as total ionospheric electron content (TEC) and tropospheric water vapor. It can also serve as a reference signal source for reflection signal compensation and quality assessment. The reflective antenna is a horizontally polarized reflective antenna used to receive the reflection path of GNSS signals from the Earth's surface or snow cover. Its horizontal polarization design enhances the response to surface reflection components, making it particularly suitable for environments where the reflection polarization state changes in snow-covered scenarios. 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, the tropospheric water vapor calculation module is mainly used to retrieve precipitable water volume (PWV) based on GNSS observation data. The overall process includes several key steps. First, the system processes 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 factor, the ZWD is converted into precipitable water volume (PWV), thereby achieving accurate retrieval 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, the ionospheric parameter calculation module is mainly used to acquire ionospheric disturbance information encountered during GNSS signal 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 determine the total electron content (TEC), which reflects the overall variation in electron density within the ionosphere. Next, the system continuously monitors the GNSS signal strength and obtains the S4 exponent by calculating the ratio of the standard deviation to the mean of the signal strength sequence to assess the degree of amplitude scintillation. Simultaneously, short-timescale differential operations are performed on the carrier phase to extract phase fluctuation characteristics, reflecting 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 crucial support for ionospheric correction and anomaly monitoring.

[0087] 4 Snow thickness inversion module

[0088] like Figure 5 As shown, the snow thickness inversion module implements the snow thickness inversion function based on GNSS-IR, and consists of three parts: SNR data extraction, Lomb-Scargle spectral 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 interferometric fringe analysis. Then, the Lomb-Scargle spectral analysis method is used to extract the spectrum of the SNR sequence to identify the interferometric fringe frequencies corresponding to the dominant frequency. Finally, based on the interferometric frequency combined with 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 distance between the antenna and the reflecting surface before and after snowfall.

[0089] 5 Data Optimization Module

[0090] Data optimization module, such as Figure 6 As shown, the system comprises four sub-modules: antenna baseline optimization, Lomb-Scargle spectrum analysis optimization, dual-antenna combination optimization, and tropospheric error optimization. These modules aim to improve the overall accuracy and stability of GNSS reflection altimetry. Specifically, the antenna baseline optimization module corrects installation deviations between direct and reflective antennas, ensuring accurate altitude inversion references. The Lomb-Scargle spectrum analysis introduces physical model constraints related to elevation angle to assess the rationality of the dominant frequency results, thereby suppressing "false peak" interference and improving the reliability of the results. The dual-antenna combination optimization module adaptively fuses direct and reflective channel data based on the difference between satellite azimuth and antenna receiving direction, improving inversion continuity under blind zone or weak signal conditions. The tropospheric error optimization module simplifies the complex modeling process of traditional dry and wet components by establishing a delay correction method based on an empirical model, directly calculating the total delay of GNSS signal propagation in the troposphere and providing real-time compensation for the inverted altitude.

[0091] 5.1 Antenna Polarization Optimization Module

[0092] like Figure 7 As shown, since the GNSS direct-fire antenna and the reflector antenna are mounted on the same pole, but they are installed at a certain angle, a vertical offset is formed between the antenna's center of mass and the horizontal direction of the pole. Assume the nominal height between the antennas is H, and the vertical projection differences between the direct-fire antenna and the reflector antenna due to their tilt angles are H1 and H2, respectively. Figure 7 As shown, since the GNSS direct-fire antenna and the reflective antenna are mounted on the same pole, but they are installed at a certain angle, a vertical offset is formed between the antenna's center of mass and the horizontal direction of the pole. Assuming the nominal height between the antennas is H, and the vertical projection differences between the direct-fire antenna and the reflective antenna due to their tilt angles are H1 and H2 respectively, then the actual vertical height difference between their antenna centers of mass should be corrected as follows:

[0093] H' = H - H1 - H2

[0094] The vertical offsets are as follows:

[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 point, and θ1 and θ2 are the installation tilt angles.

[0098] If the vertical offset term is not considered during reflection height calculation or snow depth inversion, it may lead to systematic inversion errors. Therefore, this geometric error needs to be modeled and corrected by the antenna baseline optimization module to ensure the accuracy of the geometric relationship between the GNSS reflection path and the antenna reference point, thereby improving the accuracy of altitude measurement.

[0099] 5.2 Lomb-Scargle Spectral Analysis Optimization Module

[0100] like Figure 8 As shown, in the process of extracting the dominant frequency, the standard Lomb-Scargle method is first used to perform spectral analysis on the SNR sequence of the reflected signal to initially identify the maximum peak value in the power spectrum, and the frequency corresponding to it is used as the candidate dominant frequency f. cand Subsequently, a physical modeling relationship between the reflection frequency and the satellite elevation angle θ is introduced, expressed through theoretical expressions. Where h is the known or estimated reflection height, and λ is the GNSS carrier wavelength, the desired frequency at this elevation angle is calculated. The system compares the candidate dominant frequency with the desired dominant frequency and calculates their frequency consistency score:

[0101] When the score S is higher than the set threshold, the dominant frequency is considered physically reasonable and adopted; if the score is too low, a second peak may be used to refit the sample or the sample may be marked as low confidence. Finally, the system outputs the validated dominant frequency f. final Its reliability score provides a 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, the dual-antenna combination optimization module aims to solve the problems of blind spots or weak signals in GNSS reflection measurements caused by antenna orientation limitations, satellite trajectory changes, or obstructed environments. Based on real-time analyzed satellite azimuth and elevation information, combined with the main lobe direction parameters of the reflecting antenna, this module dynamically calculates the deviation between the satellite's incident direction and the antenna's receiving direction. When this deviation exceeds a preset tolerance range, or the system detects a decrease in reflected signal quality (such as fringe blurring or reduced SNR), the module automatically determines that the current reflection path does not meet the conditions for high-quality inversion and activates the direct-view channel as an auxiliary input. Through optimal switching between the direct and reflected channels, the system can maintain the continuity and stability of the inversion results even when the signal is unsatisfactory or the reflection path fails. This module significantly improves the collaborative capability of the dual-antenna architecture and is a key technical component for achieving stable all-weather, all-directional altimetry 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 dry and wet components separately, an empirical model-based tropospheric delay correction method is adopted. This method, based on standard atmospheric refraction theory, directly calculates the total delay caused by the signal propagation path in the troposphere. The correction formula is as follows:

[0106]

[0107] Where h represents the reflected signal inversion altitude, θ is the satellite elevation angle, and k and b are empirical fitting coefficients with values ​​of k = 3.166 × 10⁻⁶. -5 b = 1.025 × 10 -2 The model was obtained by fitting measured data. Without relying on real-time meteorological data, this model can effectively compensate for signal delay errors caused by the troposphere, making it particularly suitable for applications requiring high accuracy but fast model response, such as GNSS-R snow cover inversion.

[0108] It is worth noting that in the embodiments of the above system, the modules included are 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 each functional module are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0109] Technical effects:

[0110] This invention proposes a GNSS-R joint detection system for snow thickness and atmospheric parameters based on a dual heteropolarized antenna structure. While inheriting the existing GNSS ground-based system's monitoring functions for key atmospheric parameters such as total electron content (TEC) in the ionosphere and zenith delay (ZTD) in the troposphere, it innovatively introduces a dual-antenna architecture consisting of a choke-loop positioning antenna (RHCP) and a horizontally polarized reflector antenna (HP), and designs a GNSS-R signal interferometry analysis and snow thickness inversion processing module.

[0111] This system achieves real-time, high-precision, and multi-parameter coordinated detection of snow thickness using GNSS-R technology on a ground-based platform, overcoming the technical bottlenecks of traditional single-antenna systems such as polarization direction conflict, low efficiency in receiving reflected signals, and inseparable signal paths. Simultaneously, the system integrates the advantages of various detection methods, including GNSS positioning, interferometric fringe spectrum analysis, meteorological sensing, and multi-channel observation, exhibiting the following significant technical advantages and beneficial effects in terms of structural configuration, error compensation, and data fusion:

[0112] 1. Significantly improved accuracy, with systematic error better than ±1.5cm

[0113] The system employs a dual-antenna architecture design, effectively avoiding the shielding of reflected signals by the choke structure through independent reflection channels. It also incorporates terrain reflection compensation and antenna height correction algorithms, combined with an adaptive adjustment mechanism based on the historical inversion model, achieving error control within ±1.5cm under complex terrain and variable snow conditions. Comparative measurements show that the dual-antenna structure has significant advantages in inversion accuracy and interference fringe clarity compared to the traditional single-antenna choke receiver scheme. Furthermore, the reflection channel using a horizontally polarized antenna exhibits higher signal responsivity and stability in snow layer detection.

[0114] This figure illustrates three GNSS reflector antennas with different polarizations (horizontal, vertical, and circular polarization) combined with a positioning antenna (receiving direct signals) under the same experimental scenario, measuring the altitude from the ground to the antenna. By comparing the results with a known reference altitude, the accuracy of the reflector antennas with different polarizations in GNSS-IR altimetry is verified.

[0115] Figure 10 shows the height measurement results of different polarization antennas and positioning antennas (choke antennas). Figure 10(a) compares the height retrieved by a horizontally polarized antenna and a positioning antenna; Figure 10(b) compares the height retrieved by a vertically polarized antenna and a positioning antenna; and Figure 10(c) compares the height retrieved by a circularly polarized antenna and a positioning antenna. Figures 10(a), 10(b), and 10(c) show the Lomb-Scargle power spectrum comparison results of the retrieved heights obtained by different combinations of polarization reflective antennas and positioning antennas. It is evident that the spectral peaks at the dominant frequency position in the LSP spectra of horizontally polarized, vertically polarized, and circularly polarized reflective antennas are sharper, more energy-concentrated, and have higher peak values ​​than when using a choke positioning antenna alone. This indicates stronger reception of reflected signals, clearer interference fringes, and more significant inversion results. The horizontally polarized antenna, in particular, exhibits the most prominent dominant frequency peak and the narrowest spectral line, 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 presents statistical data on altimetry results for various polarization antennas, including the deviation (mean difference) between the mean inversion altitude and the reference altitude, the root mean square error (RMSE), and the standard deviation. It is clear that the horizontally polarized antenna performs best in terms of altimetry accuracy. Its mean error is the smallest, close to the reference altitude, and its RMSE and standard deviation are significantly lower than other polarization methods, reflecting its significant advantages in the stability of reflected signal reception and the accuracy of fringe extraction. This result verifies the rationality and necessity of selecting a horizontally polarized antenna as the GNSS reflected signal receiving antenna, providing a clear technical basis for subsequent system design.

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

[0118] Antenna type Reference height 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 cover inversion error optimization strategy

[0120] This invention systematically proposes an optimized snow thickness retrieval scheme for GNSS-R dual-antenna structures, integrating multiple key strategies to address altimetry errors in complex environments. This strategy comprises four core components: First, an antenna baseline error modeling and correction method to correct installation deviations and inconsistencies in the reference plane between reflecting and direct antennas, reducing structural altitude errors; second, an improved Lomb-Scargle spectral analysis method, which enhances the stability and anti-interference capability of the reflected dominant frequency extraction by introducing an elevation angle constraint model and a spectral smoothing mechanism; third, a dual-antenna signal fusion mechanism that adaptively selects or weights and fuses direct and reflected channels based on the relationship between the satellite field of view and antenna direction, effectively enhancing the retrieval continuity under weak signal or obstruction conditions; and fourth, a rapid tropospheric delay compensation model based on elevation angle and geometric altitude, simplifying the traditional atmospheric modeling process and enabling real-time correction of GNSS signal path delay. The coordinated operation of these modules significantly improves the accuracy and adaptability of snow thickness retrieval while maintaining system compactness, filling the technical gap in existing GNSS-R altimetry technology for dual-antenna architectures by addressing the lack of a systematic error optimization scheme.

[0121] 3. The system has a high degree of integration and possesses the capability for integrated observation of multiple parameters.

[0122] This system features an open architecture and standardized interfaces, allowing parallel access to meteorological station modules to acquire key meteorological parameters such as temperature, humidity, and air pressure, enabling joint inversion of GNSS signal parameters and atmospheric environmental information. The main unit is an integrated measurement receiver capable of simultaneously processing signals received from both direct and reflected antennas, calculating key environmental parameters such as total ionospheric electron content (TEC), ionospheric scintillation index, tropospheric zenith wet delay (ZTD), precipitable water volume (PWV), and snow depth. The system supports Ethernet transmission, allowing real-time uploading of processing results to a server; in PC-free scenarios, the main unit can also independently perform data storage and remote transmission tasks via network interface.

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

Claims

1. A GNSS-R joint detection system for snow thickness and atmospheric parameters based on a dual heteropolarized antenna structure, characterized in that, include: The system comprises a GNSS signal acquisition module, a meteorological auxiliary acquisition module, a data processing module, and a data communication and uploading module. The GNSS signal acquisition module adopts a dual heteropolarized antenna structure to receive GNSS signals from both direct and reflected paths, and to capture changes in interference fringes in the reflected signals. The meteorological auxiliary acquisition module is used to collect auxiliary observation information including temperature, humidity and station air pressure; The data processing module is used to process GNSS signals from direct and reflected paths in parallel. Combined with auxiliary observation information, it corrects the installation error and reference plane offset of the dual heteropolarized antenna based on the snow inversion error optimization strategy. It introduces physical model constraints related to elevation angle to suppress false peak interference. It adaptively fuses the data from direct and reflected paths and compensates for the inversion height in real time by establishing a delay correction method based on an empirical model. This enables the inversion of snow cover and the joint calculation of total electron content in the ionosphere, ionospheric scintillation parameters, tropospheric zenith delay, precipitable water, 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 heteropolarized antenna structure according to claim 1, characterized in that, The dual heteropolarized antenna structure of the GNSS signal acquisition module includes: a direct-fire antenna and a horizontally polarized reflector antenna; wherein... The direct-fire antenna employs a high-gain choke coil structure to receive direct signals from GNSS satellites. The horizontally polarized reflective antenna is used to receive the reflected path of GNSS signals from the ground surface or snow surface.

3. The GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual heteropolarized antenna structure according to claim 1, 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 precipitable water based on GNSS observation data and auxiliary observation information; The ionospheric parameter calculation module is used to obtain information on ionospheric disturbances encountered during GNSS signal propagation. 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 altimetry based on snow cover inversion error optimization strategies.

4. The GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual heteropolarized antenna structure according to claim 3, characterized in that, The processing steps of the tropospheric water vapor calculation module include: The GNSS data is processed to calculate the tropospheric zenith total delay (ZTD). The zenith dry delay (ZHD) was estimated using an atmospheric model based on temperature, humidity, and station air pressure. Subtracting ZTD from ZHD yields the zenith wet delay ZWD, which reflects the influence of water vapor. By introducing a set wet delay conversion coefficient, ZWD is converted into precipitable water vapor (PWV), thereby achieving accurate inversion of atmospheric water vapor content.

5. The GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual heteropolarized antenna structure according to claim 3, characterized in that, The processing steps of the ionospheric parameter calculation module include: The frequency delay difference was calculated based on dual-frequency GNSS observation data, and the total electron content (TEC) of the ionosphere was determined. The intensity of GNSS signals is continuously monitored, and the S4 exponent is obtained by calculating the ratio of the standard deviation to the mean of the signal intensity sequence to assess the amplitude scintillation. At the same time, short-time-scale differential operations are performed on the carrier phase to extract 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 heteropolarized antenna structure according to claim 3, characterized in that, The processing steps of the snow thickness inversion module include: The signal-to-noise ratio sequence is extracted from GNSS observation data and used as the raw input for interferometric fringe analysis; The Lomb-Scargle spectral analysis method was used to extract the spectrum of the signal-to-noise ratio sequence in order to identify the interference fringe frequencies corresponding to the main frequency; Based on the interference frequency, combined with the system's 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 heteropolarized antenna structure according to claim 3, characterized in that, 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... The antenna baseline optimization submodule is used to correct the installation deviation between the direct and reflected 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 angle, make a reasonable judgment on the main frequency results, suppress false peak interference, and improve the reliability of the results. The dual-antenna combination optimization submodule is used to adaptively fuse data from direct and reflected paths based on the difference between satellite azimuth and antenna receiving direction, thereby improving the inversion continuity under blind zone or weak signal conditions. The tropospheric error optimization submodule is used to simplify the complex modeling process of traditional dry and wet components by establishing a delay correction method based on an empirical model, directly calculate the total delay of GNSS signal propagation 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 heteropolarized antenna structure according to claim 7, characterized in that, The installation deviation corrected by the antenna baseline optimization submodule includes: the vertical height difference H' of the antenna centroids in the dual-antenna structure, satisfying the following formula: Where H1 and H2 are the centroid height offsets of the two antennas relative to the vertical direction, L1 and L2 are the distances of the two antennas from the fixed point, θ1 and θ2 are the installation tilt angles, and H is the nominal height between the antennas.

9. The GNSS-R snow thickness and atmospheric parameter joint detection system based on a dual heteropolarized antenna structure according to claim 7, characterized in that, The processing steps of the Lomb-Scargle spectral analysis optimization submodule include: During the main frequency extraction process, the standard Lomb-Scargle method was used to perform spectral analysis on the signal-to-noise ratio sequence of the reflected signal to preliminarily identify the maximum peak value in the power spectrum, and the frequency corresponding to it was selected as the candidate main frequency f. cand ; By introducing a physical modeling relationship between the reflection frequency and the satellite elevation angle θ, the desired dominant frequency f is calculated. expected (θ): Where h is the known or estimated reflection height, and λ is the GNSS carrier wavelength; Candidate main frequency f cand With expected main frequency f expected (θ) is compared, and its frequency consistency score S is calculated: If the score S is higher than the set threshold, the dominant frequency is considered reasonable and adopted; otherwise, the candidate dominant frequency is refitted using a secondary peak or marked as low confidence until the score S is higher than the set threshold, at which point the validated dominant 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 heteropolarized antenna structure according to claim 7, characterized in that, The processing procedure of the dual-antenna combination optimization submodule includes: Based on real-time analyzed satellite azimuth and elevation information, combined with the main lobe direction parameters of the reflecting antenna, the deviation between the satellite incident direction and the antenna receiving direction is dynamically calculated. If the deviation exceeds the preset tolerance range, or if a decrease in the quality of the reflected signal is detected, it is determined that the current reflection path does not meet the conditions for high-quality inversion, and the direct channel is activated 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 heteropolarized antenna structure according to claim 7, characterized in that, The tropospheric error optimization submodule calculates the total delay of GNSS signal propagation in the troposphere. trop for: Where h is the reflected signal inversion altitude, 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 heteropolarized 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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