48 results about "Total electron content" patented technology

The invention discloses a foundation space ionosphere and troposphere integrated joint detection method and device, and belongs to the technical field of satellite navigation, and the method comprises the steps: detecting the vertical total electron content of an ionosphere: extracting the inclined total electron content of the ionosphere at an ionosphere puncture point, and converting the inclined total electron content into the vertical total electron content of the ionosphere; detecting a foundation Beidou ionosphere disturbance index; and detecting an ionosphere phase scintillation index, a troposphere zenith total delay, a troposphere precipitable amount and an ionosphere amplitude scintillation index, and outputting ground-based Beidou space ionosphere and troposphere integrated joint detection parameters. The method effectively improves the reliability, robustness and meteorological perception capability of a navigation positioning system in a complex atmospheric environment, and is especially suitable for special environmental conditions such as severe convection weather, typhoon landing, violent disturbance of an ionized layer and the like.

The invention discloses a low-cost GNSS (Global Navigation Satellite System) ionosphere disturbance monitoring and early warning method based on Transform, which is characterized in that a low-cost GNSS receiver is used for extracting total electron content (TEC) and processing data of a plurality of monitoring stations in a region in real time in combination with an ROTI index, and ionosphere disturbance is quickly predicted and early warning delay is remarkably reduced through a dynamic attention mechanism and multi-scale feature extraction of the Transform. Compared with a traditional method, more comprehensive ionosphere state information is provided by integrating observation sequences of all receivers in an area, meanwhile, the dependence on expensive high-frequency equipment is greatly reduced, and the monitoring cost is reduced. According to the invention, the architecture based on deep learning has high expandability, can be seamlessly integrated to the existing GNSS network, and provides an economical, efficient and high-timeliness solution for global ionosphere monitoring.

The embodiment of the invention discloses a training method, a using method and a related device of an electron content prediction model, and the method comprises the steps: carrying out the feature extraction of the total electron content data, geomagnetic activity index data and solar activity index data of an ionized layer collected by a Beidou satellite navigation system through a random forest algorithm; obtaining key feature data corresponding to the total electron content data; adopting a sliding window mechanism, taking the key feature data under a preset time window length as input time sequence data, taking the total electron content data at the last moment of the time window length as target output, and constructing a training sample pair; and performing prediction training of total electron content data according to the training sample pair and a preset deep learning model to obtain a trained electron content prediction model. In this way, the nonlinear dynamic characteristics of the ionosphere delay are effectively captured by the deep learning model, the prediction precision of the total electron content is improved, and the correction precision of the ionosphere delay is improved.

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 invention relates to a global ionosphere TEC inversion method based on a Galileo HAS service, and the method comprises the steps: obtaining correction information through the HAS service, carrying out the preprocessing of observation data of a reference station according to the correction information, and obtaining a preprocessed carrier phase observation value; calculating carrier phase ambiguity and a purified carrier phase observation value according to the preprocessed carrier phase observation value; calculating the total electron content STEC of an inclined path according to the carrier phase ambiguity and the purified carrier phase observation value; constructing a double-layer ionosphere network model according to the geographic coordinates of the ionosphere puncture point and STEC; and carrying out inversion on the vertical total electron content of the ionized layer by using Kalman filtering to obtain the vertical total electron content of the ionized layer and carrying out compression. The method breaks through the network dependence of traditional foundation enhancement, and can still provide continuous and reliable ionized layer delay correction under the scenes of ocean, desert and the like.

The application discloses an ionosphere total electron content prediction method, a model training method and a system, relates to the field of satellite geodesy and space weather prediction, and the model training method comprises: an ionosphere total electron content prediction model comprising an encoder part of a stacked autoencoder, an Informer network and a decoder part of the stacked autoencoder connected in sequence; wherein the model training comprises stacked autoencoder training and Informer network training. The ionosphere total electron content prediction model is constructed based on the Informer network and the stacked autoencoder, the ionosphere total electron content prediction model is used for ionosphere total electron content prediction, the time complexity is reduced, the memory occupation is reduced, and the ionosphere total electron content prediction efficiency is improved.

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 invention discloses an ionosphere modeling method, system and device based on detection data, a medium and a product, and relates to the field of electromagnetic wave transmission and ionosphere modeling, and the method comprises the steps: obtaining GNOS-II detection data of different dates according to a space environment, and screening effective rays based on the correction degree of the detection data on electron density; according to the corresponding data of the effective ray, based on a space-based ionosphere inversion method, obtaining grid points through which the effective ray passes; according to the corresponding data of the effective rays, determining the total electron content of space-based ionosphere inversion by using a dual-frequency calculation method; and according to the grid points through which the effective rays penetrate and the total electron content of space-based ionosphere inversion, carrying out horizontal constraint and vertical constraint on the global ionosphere electron density distribution by utilizing a ground-based ionosphere inversion method, and outputting the constrained global ionosphere electron density distribution to obtain a global ionosphere model. According to the invention, real-time high-precision ionosphere building in a global range is realized.

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.