37 results about "Total electron content" patented technology

Described herein are solutions for real-time precise ionosphere corrections. An integrate and dump (I&D) server can receive a Networked Transport of Radio Technical Commission For Maritime Services (RTCM) via Internet Protocol (NTRIP) bit stream of a spherical harmonic model for precise point positioning (PPP). The model can be a vertical total electron content (VTEC) spherical harmonic expansion model and / or can include a set of ionosphere coefficients for correcting errors in satellite signals due to ionospheric conditions. The I&D server can generate a file of ionosphere coefficients, according to a refresh timer, and can send the file to a content delivery network (CDN) for distribution to user equipment (UEs) upon request. These and many other features and examples are described herein.

The application discloses a method, system, device, medium and product for ionospheric modeling based on detection data, relates to the field of electromagnetic wave transmission and ionospheric modeling, and comprises the following steps: acquiring GNOS-II detection data of different dates according to a space environment, and screening effective rays based on the correction degree of electron density of the detection data; acquiring grid points through by the effective rays based on a space-based ionospheric inversion method according to corresponding data of the effective rays; determining total electron content of the space-based ionospheric inversion by using a dual-frequency calculation method according to the corresponding data of the effective rays; performing horizontal constraint and vertical constraint on global ionospheric electron density distribution by using a ground-based ionospheric inversion method according to the grid points through by the effective rays and the total electron content of the space-based ionospheric inversion; outputting the constrained global ionospheric electron density distribution to obtain a global ionospheric model. The application realizes real-time high-precision ionospheric construction in a global range.

The application discloses a method and system for ionospheric irregularity identification based on machine learning. The method comprises the following steps: receiving a GNSS observation file and an ephemeris file, and preprocessing the GNSS observation file and the ephemeris file; extracting an electron total content rate index feature, a time integral and total electron content index feature, and a Doppler coefficient feature according to the preprocessed GNSS observation file and ephemeris file; identifying ionospheric irregularities according to the extracted electron total content rate index feature, time integral and total electron content index feature, and Doppler coefficient feature, and outputting an identification result. The method can avoid setting a single threshold to determine whether an ionospheric irregularity event occurs during the process of identifying ionospheric irregularities, and effectively avoids data loss caused by cycle slip detection, thereby effectively identifying EPB events and TID events.

The invention provides a GNSS-R wide swath ionosphere vertical total electron content measurement method, device and equipment and a medium. The method comprises the following steps: measuring the propagation delay inequality of a direct signal and a reflection signal from the same navigation satellite through a satellite-borne GNSS-R dual-frequency receiver; calculating a first VTEC value below the satellite orbit at the specular reflection point according to the propagation delay inequality; according to the two-dimensional ionosphere model and the first VTEC value, calculating a second VTEC value and a third VTEC value below the satellite orbit at the incident puncture point and the reflection puncture point; for each target point in the specular reflection point, the incident puncture point and the reflection puncture point, calculating the total VTEC value of the vertical path based on the three-dimensional ionosphere model and the VTEC value below the satellite orbit at the target point, and taking the total VTEC value as the VTEC observation result of the single navigation satellite; and fusing VTEC observation results of a plurality of navigation satellites to generate wide-swath ionized layer VTEC observation data.

The application provides a kind of heat wave influence on global navigation satellite system positioning accuracy evaluation method and system, determine the research area and research year of target heat wave event, according to the corresponding climate state of research year;Obtain the temperature data of each meteorological station in the research area, interpolate and supplement the missing data among them;According to the data after interpolation and supplement, determine the research period of heat wave;Analyze the abnormal value spatial distribution of two-meter temperature above ground and daily average soil moisture in the research period of research area;Analyze the change of abnormal value of air temperature and atmospheric precipitable water per hour, and explain the reason of atmospheric precipitable water anomaly through the corresponding relationship of factors;Analyze the abnormal value change of ionospheric total electron content and air temperature per hour;Compare the difference of global navigation satellite system positioning accuracy in heat wave period and non-heat wave period in research period.The application provides a reference for users to carry out high-precision GNSS positioning during heat wave.

The application discloses a method and device for ionospheric delay correction based on regional ground-based GNSS and occultation data, and the method comprises the following steps: acquiring double-frequency observation data of ground-based GNSS in a target region and calculating VTEC; constructing a regional ionospheric TEC model by using a spherical cap harmonic function, calculating VTEC on each grid point in the target region according to a preset resolution, and screening effective grid points according to the number of piercing points of the grid points to form a regional grid constraint data set; taking the regional grid constraint data as virtual observation values, fusing the virtual observation values with global occultation vertical total electron content observation values, constructing a joint observation equation, and solving real-time broadcast ionospheric model coefficients; and calculating ionospheric delay correction values according to the real-time broadcast ionospheric model coefficients. According to the application, the regional VTEC grid constraint is constructed and is cooperatively inverted with global occultation data, so that the numerical ill-conditioned problem caused by direct fusion of multi-source heterogeneous data is effectively relieved, and on the premise of not depending on an empirical background field, the global precision, stability and dynamic adaptability of the model are significantly improved.

The application discloses a global ionospheric model precision factor generation method and device, and belongs to the technical field of satellite navigation and ionospheric modeling. The method comprises the following steps: inversing ionospheric vertical total electron content based on carrier phase smoothing pseudo-range; extracting ionospheric unmodeled error by using a global ionospheric grid map; analyzing the skewness and kurtosis distribution characteristics of the error; combining experience error optimization and measured error driving to reconstruct the ionospheric model precision factor, and improving the reliability and stability of the precision factor by introducing monitoring point classification, scale adjustment factor, inflation factor time smoothing and other mechanisms; further proposing an ionospheric error precision consistency map and an RMS envelope probability index to verify the consistency of the precision factor and the measured error. The application can generate a precision factor that truly reflects the statistical characteristics of ionospheric error, provides effective support for the construction of an ionospheric random model in high-precision GNSS positioning, and significantly improves the positioning precision and convergence performance.

The invention discloses a volcanic eruption monitoring method fusing wind cloud satellite and COSMIC ionosphere occultation data, which comprises the following steps: firstly, collecting and preprocessing data, fitting the preprocessed data through a spherical harmonic function, and inverting to obtain the vertical total electron content of each grid point; defining an abnormal state and a normal state according to the solar radiation flux index under the grid, and obtaining an abnormal state dynamic threshold value and a normal state dynamic threshold value; collecting real-time data, and obtaining a vertical total electron content real-time value of a global grid through inversion, wherein the vertical total electron content real-time value is used for judging a suspected abnormal disturbance signal and recording related spatio-temporal information; and extracting vertical total electron content data of a preset area in a time window corresponding to the suspected abnormal disturbance signal, calculating a grid proportion through conditions, judging that the interference is solar or geomagnetic activity interference according to the proportion, and otherwise, judging that the interference is local volcanic eruption. According to the method, efficient, rapid, global-coverage and high-precision volcanic eruption monitoring can be realized, and timely and reliable decision support is provided for disaster prevention and reduction departments.

The present disclosure presents systems method for remote sensing of the ionosphere. One such method comprises providing a single-board computer communicatively connected to a global navigation satellite systems (GNSS) receiver which is preconfigured to determine global positioning system coordinates by communicating with a set of GNSS satellites, reconfiguring the function of the single-board computer and GNSS receiver to acquire a set of GNSS signal parameters; and determining the set of physical properties of an ionosphere from the set of GNSS signal parameters. Other methods and systems are also provided.

An ionosphere monitoring method, device and equipment and medium using crowd-sourced GNSS data, the method comprising: obtaining inter-station single-difference ionosphere slant delay at crowd-sourced GNSS users and static reference stations, and calculating absolute ionosphere slant delay of the crowd-sourced GNSS users; obtaining line-of-sight direction total electron content of the crowd-sourced GNSS users through the absolute ionosphere slant delay of the crowd-sourced GNSS users; taking absolute ionosphere slant delay at the static reference stations as a reference, calculating absolute ionosphere slant delay of the crowd-sourced GNSS user group, and generating line-of-sight direction total electron content distribution map of the crowd-sourced GNSS user group to monitor the ionosphere. The application enhances the spatial coverage of the static reference station through the dynamic GNSS user group, improves the spatial resolution of the ionosphere discrete sampling information, and uses the static reference station to provide high-precision reference for ionosphere modeling of the dynamic GNSS user group, thereby forming a complementary enhancement mechanism of GNSS dynamic and static observation data, and finally cooperatively improving the spatial resolution and accuracy of ionosphere monitoring.

The invention provides an ionosphere modeling simulation method and device and related equipment, and the method comprises the steps: fusing first electron density fields of at least two simulation models of an ionosphere on a satellite signal propagation link, and obtaining a second electron density field of the ionosphere; superposing the second electron density field with disturbance terms of M scales to obtain a third electron density field of the ionized layer, M being an integer greater than 1; based on the orbit height corresponding to the satellite signal and the third electron density field, correcting a simulation environment about the ionosphere on the satellite signal propagation link to obtain a total electron content TEC correction result, matched with the orbit height, of the ionosphere; and based on the TEC observation data and the TEC correction result of the satellite signal about the ionized layer, performing data assimilation processing on the simulation environment about the ionized layer on the satellite signal propagation link to obtain a fourth electron density field of the ionized layer.

The application discloses a method, device, medium and product for predicting the total electron content of the ionosphere, which comprises the following steps: acquiring geomagnetic disturbance data of a region where a single-station global navigation satellite system is located, wherein the geomagnetic disturbance data comprises a magnetic north component and a magnetic east component; calculating the horizontal geomagnetic disturbance intensity based on the magnetic north component and the magnetic east component; inputting the horizontal geomagnetic disturbance intensity, the geomagnetic disturbance data, the acquired solar activity index and the historical observation sequence of the total electron content into a preset prediction model, so as to obtain a plurality of subspace attention features through a multi-head attention mechanism, and to obtain an encoding feature matrix through linear change processing; decoding the encoding feature matrix through a decoder of the prediction model to obtain a context vector, and performing autoregressive processing on the context vector until a target total electron content prediction sequence is obtained. The application can improve the prediction accuracy of the total electron content in the ionosphere above the single-station global navigation satellite system.

This invention belongs to the field of satellite navigation and space environment monitoring technology, and relates to a dual-frequency TEC inversion optimization method based on real-time altimetry data. Based on GNSS ground-based dual-frequency observations, it integrates ionospheric altimetry data and absolute signal deviation products to achieve high-precision conversion of STEC (Slant Total Electron Content) to VTEC (Vertical Total Electron Content). The method includes data preprocessing, TEC inversion, cycle slip detection, deviation correction, and mapping correction, and generates ROTI (Rate of TEC Index, i.e., ionospheric scintillation characterization parameter) products. By introducing a single-layer model for correcting the effective ionospheric height, the ability to characterize ionospheric disturbances is improved.

The application discloses a method and system for identifying ionospheric irregularities, and the method comprises the following steps: pre-processing the night disk observation data by using a data integrity check and invalid value removal method, obtaining an effective radiation data set, and extracting a fixed-wavelength radiation data set in the southern and northern hemispheres from the effective radiation data set respectively; merging the fixed-wavelength radiation data sets in the southern and northern hemispheres, and drawing a radiation distribution map by combining a two-dimensional grid interpolation method; identifying ionospheric irregularities by using a radiation difference threshold identification method on the radiation distribution map to obtain a preliminary ionospheric irregularity identification result; and matching the preliminary identification result with total electron content data, calculating and determining a final ionospheric irregularity identification result. The method does not depend on densely distributed ground-based GNSS receiving stations, can significantly reduce the calculation complexity, and improves the processing efficiency.

The application provides a GNSS-R wide-swath ionospheric vertical total electron content measurement method, device, equipment and medium, and the method comprises the following steps: measuring the propagation time delay difference of direct signal and reflected signal from the same navigation satellite through a space-borne GNSS-R dual-frequency receiver; calculating a first VTEC value below the satellite orbit at the mirror reflection point according to the propagation time delay difference; calculating a second VTEC value and a third VTEC value below the satellite orbit at the incident piercing point and the reflection piercing point according to a two-dimensional ionospheric model and the first VTEC value; for each target point in the mirror reflection point, the incident piercing point and the reflection piercing point, calculating the total VTEC value of the vertical path based on a three-dimensional ionospheric model and the VTEC value below the satellite orbit at the target point, as the VTEC observation result of a single navigation satellite; and fusing the VTEC observation results of multiple navigation satellites to generate wide-swath ionospheric VTEC observation data.

The application provides a non-spherical assumption ionosphere occultation inversion method and device, comprising the following steps: solving occultation observation collision parameters and absolute total electron content through a data processing module; and constructing a non-spherical assumption inversion function model through a model establishing module. The application has the beneficial effect that the application solves the problem that mainstream ionosphere occultation inversion algorithms are limited by the spherical assumption, thereby making it difficult to improve inversion accuracy, and provides an ionosphere occultation inversion method based on a non-spherical assumption, thereby improving the accuracy of ionosphere occultation inversion, and the product can be applied to the fields of ionosphere weather monitoring and forecasting, navigation and positioning enhancement and the like.

The present application belongs to the technical field of satellite navigation, and specifically discloses an ionospheric delay correction method of a space-based data driven broadcast ionospheric model. The present application aims to use the occultation observation data of a low earth orbit (LEO) constellation to directly obtain global total electron content (TEC) which is independent of ground stations by integrating from the occultation electron density profile; and to introduce a neural network model to quickly calibrate and optimize the quality of the space-based TEC data; finally, to directly drive the NTCM-G broadcast ionospheric model using the calibrated TEC product which is high-precision and globally uniformly covered, to generate its global broadcast correction coefficients and broadcast them to users. The present application method establishes a new method of realizing a broadcast ionospheric model with space-based data as the core driving source, avoids the limitations of the ground-based observation network from the data source, and improves the autonomy, complete coverage and overall precision of the ionospheric correction service of the global navigation system.

The invention discloses a differential oblique ionosphere delay uncertainty estimation method, system and device based on deep learning, and a medium. The method comprises the following steps: extracting differential oblique total electron content at each station of a reference network; calculating a differential slant total electron content interpolation at the user position by adopting a Kriging interpolation method, and constructing an interpolation error sample; performing non-Gaussian expansion processing on the interpolation error sample and the accurate standard deviation to obtain a conservative standard deviation, and performing space expansion processing on the conservative standard deviation in a user neighborhood to obtain an ionosphere integrity parameter; and broadcasting the ionosphere precision parameter and the ionosphere integrity parameter to a user side. According to the method, the Kriging interpolation method is combined with the neural network, so that accurate estimation of the ionosphere delay uncertainty is realized.

This application relates to a method, apparatus, computer equipment, storage medium, and computer program product for processing product data of ionospheric products. The method includes: acquiring product data through ionospheric products and determining the spherical harmonic function model corresponding to the current epoch; determining the first vertical total electron content of the ionosphere corresponding to the current epoch based on observation data from a global navigation satellite system of the target area; correcting the coefficients of the spherical harmonic function model corresponding to the current epoch based on the first vertical total electron content of the ionosphere and the product data to obtain a corrected spherical harmonic function model; and determining the target ionospheric vertical total electron content based on the corrected spherical harmonic function model. This method can improve the accuracy of ionospheric product data.

The invention provides a method and system for evaluating the influence of heat waves on the positioning accuracy of a global navigation satellite system, and the method comprises the steps: determining a research region and a research year of a target heat wave event, and determining a corresponding climate state according to the research year; acquiring air temperature data of each meteorological station in the research area, and performing interpolation supplement on missing data in the air temperature data; according to the data after interpolation supplementation, determining a research time period of the heat waves; analyzing abnormal value spatial distribution of daily average values of temperature and soil water content two meters above the ground surface in the research time period of the research area; the change of the abnormal value of the air temperature and the atmospheric precipitable water per hour is analyzed, and the reason of the atmospheric precipitable water abnormality is explained through the corresponding relation of the factors; analyzing the total electron content of the ionized layer and the abnormal value change of the air temperature per hour; and comparing the difference between the positioning precision of the global navigation satellite system in the hot wave period and the non-hot wave period in the research time period. According to the invention, a reference is provided for a user to carry out high-precision GNSS positioning during a heat wave period.

The invention relates to a global ionosphere prediction method based on a latitude adaptive partitioning mechanism. The method comprises the following steps: S1, downloading and analyzing a GIM product; s2, performing normalization processing on the VTEC of the ionized layer based on the analyzed GIM product, and dividing the processed VTEC into a training set and a verification set; s3, a latitude self-adaptive Vision transfer model is constructed, and the parameters of the model are initialized; s4, designing a latitude partition loss function for the model; s5, training the model by adopting a training set, optimizing model parameters by minimizing the latitude partition loss function, ending training when the model meets an early stop standard, and storing the trained model; and S6, inputting the analyzed GIM product into the trained model, and carrying out reverse normalization processing on the predicted ionospheric vertical total electron content value output by the model to generate a predicted GIM product.

The invention discloses an ionosphere prediction method and device based on a white spectrum method and deep learning, and belongs to the technical field of intersection of spatial physics and satellite navigation, and the method comprises the steps: carrying out the preprocessing of multi-source observation data, obtaining time series data, carrying out the white spectrum method analysis of the time series data, and obtaining a disturbance time series; processing the disturbance time sequence to generate a global solar activity level index, a middle and low latitude geomagnetic disturbance index and a polar region geomagnetic disturbance index; the indexes and satellite inversion data serve as input, a deep learning reconstruction model formed by a multi-layer full-connection feedforward neural network is adopted, and ionosphere four-dimensional electron density distribution is output; ionized layer four-dimensional electron density distribution and total electron content observation data serve as input, a space-time prediction model is adopted, and an ionosphere prediction result at the future moment is output. The problems that a traditional spatial index is poor in timeliness and low in resolution and cannot effectively capture the complex time-space characteristics of the ionized layer can be solved.

This invention discloses a method for constructing a multilayer ionospheric model based on prior constraints, comprising the following steps: obtaining the ionospheric electron density matrix output by the NeQuick-G model; processing the ionospheric electron density matrix using principal component analysis, and determining the number of ionospheric layers based on the cumulative contribution rate of the principal components; clustering the ionospheric electron density and corresponding height using the K-means clustering algorithm based on the number of layers to obtain the height and boundary of each layer; obtaining simulated total electron content data based on the height and boundary of each layer; selecting the optimal polynomial order for each layer and each modeling time period from multiple candidate polynomial orders using the Akaike information content criterion, and constructing a polynomial function model for each layer and each time period; constructing an ionospheric model using the original GNSS observation data based on the number of layers, the height and boundary of each layer, and the polynomial function model, and solving the ionospheric model parameters.

The application discloses a GNSS-S radar echo phase correction method based on a spaceborne vertical sounding instrument and a occultation receiver, which utilizes a GNSS-S radar, an occultation receiver and a spaceborne vertical sounding instrument on the same low-orbit satellite platform for cooperative detection. The first electron density profile from the bottom to the top of the ionosphere at a specific position in front of the satellite is acquired by the occultation receiver; when the satellite flies to the position, the second electron density profile from the peak height of the ionosphere to the height of the satellite platform is acquired by the vertical sounding instrument; the second profile is used to calibrate and fuse the first profile to obtain a corrected profile; based on the fused profile, the complete electron density distribution of the downlink and uplink propagation paths of the GNSS-S radar signal is reconstructed, and the total electron content of the paths is calculated respectively; then, the phase error correction amount is calculated according to the total electron content and a phase delay model, and finally, the radar echo is phase compensated to correct the target positioning result. The application can realize accurate correction of the GNSS-S radar phase error caused by the ionosphere.

This invention belongs to the field of satellite navigation technology, specifically disclosing a method for ionospheric delay correction of a broadcast ionospheric model driven by space-based data. The invention aims to utilize occultation observation data from the LEO constellation to directly obtain the global total electron content (TEC) independent of ground stations by integrating from the occultation electron density profile. A neural network model is introduced to rapidly calibrate and optimize the quality of the space-based TEC data. Finally, this high-precision, globally uniformly covered calibrated TEC product is used to directly drive the NTCM-G broadcast ionospheric model, generating its global broadcast correction coefficients and broadcasting them to users. This invention establishes a novel method for implementing broadcast ionospheric models with space-based data as the core driving source, circumventing the limitations of ground-based observation networks from the data source, and improving the autonomy, coverage integrity, and overall accuracy of the global navigation system's ionospheric correction service.

The invention discloses a global ionosphere model precision factor generation method and device, and belongs to the technical field of satellite navigation and ionosphere modeling. The method comprises the following steps: inverting the vertical total electron content of an ionized layer based on a carrier phase smoothing pseudo-range; using a global ionosphere grid map to extract ionosphere unmodeled errors; analyzing skewness and kurtosis distribution characteristics of the error; in combination with empirical error optimization and actual measurement error driving, ionosphere model precision factors are reconstructed, and the reliability and stability of the precision factors are improved by introducing mechanisms such as monitoring point classification, scale adjustment factors and expansion factor time smoothing; and further proposing an ionosphere error precision consistency graph and an RMS envelope probability index, and verifying the consistency of a precision factor and an actual measurement error. According to the method, the precision factor truly reflecting the ionosphere error statistical characteristics can be generated, effective support is provided for ionosphere random model construction in high-precision GNSS positioning, and the positioning precision and convergence performance are remarkably improved.

The invention discloses an ionosphere occultation additional phase correction method and system without auxiliary side data, which are used for realizing accurate inversion of ionosphere electron density under the condition of only occultation side observation. The method comprises the following steps: preprocessing additional phase and satellite positioning data, sorting according to collision height and screening according to intervals to finish initial accurate correction; calculating the initial total electron content at each collision height based on a quasi-correction result, and iteratively solving a top electron density coefficient and an elevation coefficient; calculating a top electron density parameter, an elevation parameter and a total electron content compensation item of each height by using the final coefficient; and adding the compensation item into the initial total electron content to obtain a correction result, and further generating an electron density profile. According to the method, high-precision inversion can be carried out without auxiliary side data, the limitation of a traditional differential correction method is overcome, the inversion precision and stability in a low-orbit satellite task are remarkably improved, and the application range of an occultation technology is expanded.

This invention proposes a method and system for acquiring real-time ionospheric information using GNSS based on forecast variance, relating to the field of GNSS positioning technology. The method includes: acquiring real-time ionospheric data from multiple different analysis centers and preprocessing it to obtain corresponding spherical harmonic coefficients; converting the spherical harmonic coefficients into the total electron content at the corresponding ionospheric puncture point using a spherical harmonic function, correcting the total electron content at different puncture points using solar position information to obtain corrected electron content data, and converting the corrected electron content data into global VTEC grid data; using the electron content forecast information released by the European Orbit Determination Centre as the true value and the global VTEC grid data as the observed value, calculating the data weight value for each analysis center; and weighting and fusing the global VTEC grid data corresponding to each analysis center using the data weight value to obtain the target ionospheric electron total quantity information.

The embodiment of the invention provides a method and device for predicting the total electron content of an ionized layer, and relates to the technical field of space weather prediction.The method comprises the steps that target feature data and target space environment monitoring data of a target area are determined, and the density of a GNSS base station in the target area is smaller than a preset threshold value; the target feature data comprises observation data of a GNSS reference station in the target area at multiple moments before a specified moment and geographic information data of the target area; the target space environment monitoring data comprises space environment monitoring data at a plurality of moments before the specified moment; and according to the target feature data and the target space environment monitoring data, predicting the total electron content of the ionized layer of the target area at the specified moment and the moment after the specified moment. By adopting the technical scheme, the problem that the total electron content of the ionized layer of the sparse region of the GNSS reference station cannot be accurately determined is solved.

This application relates to the field of satellite navigation and positioning technology, and in particular to a method, apparatus, and device for generating real-time ionospheric grid products. The method includes: acquiring ionospheric delayed data streams published by multiple analysis center terminals; extracting the vertical total electron content of the corresponding analysis center terminal and associating it with grid points indexed by latitude and longitude; determining a target geographical area based on the current location of the user terminal; constructing a differential time series for each grid point within the area based on the vertical total electron content of each analysis center, and calculating the uncertainty of each analysis center terminal; calculating the weighted average of the vertical total electron content of each grid point based on the uncertainty, and generating a real-time ionospheric grid product for the target area. This solves the problems of high computational complexity and low positioning accuracy in ionospheric data processing in related technologies.